EECSDeblina Sarkar Massachusetts Institute of Technology Melanie Schmidt Carnegie Mellon University...

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2015

Rising StarsEECS

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“MIT is delighted to host such an esteemed group of women in computer science and electrical engineering. The Rising Stars program gives you a terrific opportunity to present your research, network with your peers, and learn about ways to pursue a career in academia. It can serve as a professional launching pad, and I am thrilled you are here to take part!”

—Cynthia Barnhart Chancellor Ford Professor of Engineering Massachusetts Institute of Technology

“Welcome to MIT! The Rising Stars Workshop has again brought together some of the most tal-ented women in computer science and electrical engineering globally. You will help lead research, education, and the professional community in these fields, and others, in the years to come. We hope this program will provide guidance and inspiration as you launch your careers, and help foster a strong collegial network that will persist long into the future.”

— Ian A. Waitz Dean of Engineering Jerome C. Hunsaker Professor of Aeronautics and Astronautics Massachusetts Institute of Technology

Rising Stars 2015 1

From the 2015 Rising Stars Workshop ChairsWelcome to the 2015 Rising Stars in EECS Workshop at MIT. We launched Rising Stars in 2012 to identify and mentor outstanding young women electrical engineers and computer scientists interested in exploring ca-reers in academia. We are pleased that the program has grown substan-tially since its beginning. This year’s workshop will bring together 62 of the world’s brightest women PhD students, postdocs, and engineers/sci-entists working in industry, for two days of scientific interactions and ca-reer-oriented discussions aimed at navigating the early stages of careers in academia.

This year’s program focuses on the academic job search process and how to succeed as a junior faculty member. Our program includes invited pre-sentations targeting the academic search process, how to give an effective job talk, and developing and refining one’s research and teaching state-ment. There will also be panels focused on the early years of an academic career, covering topics such as forming and ramping up a research group, leadership, work-life balance, fundraising, and the promotions process.

The workshop this year will also feature 24 oral presentations and 38 post-er presentations by participants, covering a wide range of specialties rep-resentative of the breadth of EECS research. The presentations span the spectrum from materials, devices and circuits, to signal processing, com-munications, computer science theory, artificial intelligence and systems.

Many attendees from previous workshops have gone on to secure faculty positions at top universities, or research positions in leading industry labs. Toward this end, we are pleased to highlight and feature workshop partic-ipants by circulating this brochure to the leadership of EECS departments at top universities and to selected research directors in industry.

We hope, in addition, that Rising Stars will give participants the opportuni-ty to network with peers and present their research, opening the door for ongoing collaboration and professional support for years to come.

We are very grateful to the supervisors who supported the participation of the rising stars. We would also like to thank MIT’s School of Engineering, the Office of the Dean for Graduate Education, and the EECS-affiliated re-search labs—CSAIL, LIDS, MTL, and RLE—for their support.

We look forward to meeting and interacting with you all. Anantha Chandrakasan, Workshop ChairVannevar Bush Professor of Electrical Engineering and Computer ScienceDepartment Head, MIT Electrical Engineering and Computer Science

Regina Barzilay, Workshop Technical Co-ChairProfessor of Electrical Engineering and Computer Science, MIT

Dina Katabi, Workshop Technical Co-ChairAndrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, MIT

Asu Ozdaglar, Workshop Technical Co-ChairProfessor of Electrical Engineering and Computer Science, MITDirector, Laboratory for Information and Decision Systems

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“The Rising Stars in EECS Workshop provides what today’s graduates need, opportunities to take the lead, to present innovative work, to deliver professional communications, and to address global, scientific, and ethical issues. Above all, the conference connects women graduates with a critical network of mentors, colleagues, and faculty who will support their academic and professional success.”

— Christine Ortiz Dean for Graduate Education Morris Cohen Professor of Materials Science and Engineering Massachusetts Institute of Technology

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Henny Admoni Yale UniversityIlge Akkaya University of California at BerkeleySara Alspaugh University of California at BerkeleyElnaz Banan Sadeghian Georgia Institute of TechnologyKatherine Bouman Massachusetts Institute of TechnologyCarrie Cai Massachusetts Institute of Technology Precious Cantú École Polytechnique Fédérale de LausannePeggy Chi University of California at BerkeleyHannah Clevenson Massachusetts Institute of TechnologySeyedehAida (Aida) Ebrahimi Purdue UniversityMotahareh Eslamimehdiabadi University of Illinois at Urbana- ChampaignVirginia Estellers University of California at Los AngelesFei Fang University of Southern California Liyue Fan University of Southern CaliforniaGiulia Fanti University of California at BerkeleyLu Feng University of PennsylvaniaKathleen Fraser University of TorontoMarzyeh Ghassemi Massachusetts Institute of TechnologyElena Leah Glassman Massachusetts Institute of TechnologyBasak Guler Pennsylvania State UniversityDivya Gupta University of California at Los AngelesJudy Hoffman University of California at BerkeleyHui-Lin Hsu University of TorontoCarlee Joe-Wong Princeton UniversityGauri Joshi Massachusetts Institute of TechnologyAnkita Arvind Kejriwal Stanford UniversityHana Khamfroush Pennsylvania State UniversityHyeji Kim Stanford UniversityJung-Eun Kim University of Illinois at Urbana-ChampaignVarada Kolhatkar Privacy Analytics Inc.Parisa Kordjamshidi University of Illinois at Urbana-Champaign

Ramya Korlakai Vinayak California Institute of TechnologyKarla Kvaternik Princeton UniversityMin Kyung Lee Carnegie Mellon UniversityKun (Linda) Li University of California at BerkeleyHongjin Liang University of Science and Technology of ChinaXi Ling Massachusetts Institute of TechnologyFei Liu Carnegie Mellon UniversityYu-Hsin Liu University of California at San DiegoKristen Lurie Stanford UniversityJelena Marasevic Columbia UniversityGhita Mezzour International University of RabatJamie Morgenstern University of PennsylvaniaVaishnavi Nattar Ranganathan University of WashingtonXiang Ni University of Illinois at Urbana ChampaignDessislava Nikolova Columbia UniversityFarnaz Niroui Massachusetts Institute of TechnologyIdoia Ochoa Stanford UniversityEleanor O’Rourke University of WashingtonAmanda Prorok University of PennsylvaniaElina Robeva University of California at BerkeleyDeblina Sarkar Massachusetts Institute of TechnologyMelanie Schmidt Carnegie Mellon UniversityClaudia Schulz Imperial College LondonMahsa Shoaran California Institute of TechnologyEva Song Princeton UniversityVeronika Strnadova-Neeley University of California at Santa BarbaraHuan Sun University of California at Santa BarbaraEwa Syta Yale UniversityRabia Yazicigil Columbia UniversityQi (Rose) Yu University of Southern CaliforniaZhou Yu Carnegie Mellon University

2015 EECS Rising Stars

4 Rising Stars 2015 http://risingstars15-eecs.mit.edu/

Henny Admoni PhD Candidate Yale University Nonverbal Communication in Human-Robot InteractionRobotics has already improved lives by taking over dull, dirty, and dangerous jobs, freeing people for safer, more skillful

pursuits. For instance, autonomous mechanical arms weld cars in factories, and autonomous vacuum cleaners keep floors clean in millions of homes. However, most currently deployed robotic de-vices operate primarily without human interaction, and are typi-cally incapable of understanding natural human communication. My research focuses on enabling human-robot communication in order to develop social robots that interact with people in natu-ral, effective ways. Application areas include social robots that help elderly users with tasks like preparing meals or getting dressed; manufacturing robots that act as intelligent third hands, improving efficiency and safety for workers; and robot tutors that provide stu-dents with personalized lessons to augment their classroom time. Nonverbal communication, such as gesture and eye gaze, is an inte-gral part of typical human communication. Nonverbal communica-tion happens bidirectionally in an interaction, so social robots must be able to both recognize and generate nonverbal behaviors. These behaviors are extremely dependent on context, with different types of behaviors accomplishing different communicative goals like directing attention or managing conversational turn-taking. To be effective in the real world, nonverbal behaviors must occur in real time in dynamic, unstructured interactions. My research focus-es on developing bidirectional, context aware, real time nonverbal behaviors for personally assistive robots. Developing effective non-verbal communication for robots engages a number of disciplines including autonomous control, machine learning, computer vision, design, and cognitive psychology. My approach to this research is three-fold. First, I conduct well-controlled human-robot interaction studies to understand people’s perceptions of robots. Second, I build computational models of nonverbal behavior using data from human-human interactions. Third, I develop robot-agnostic behav-ior controllers for collaborative human-robot interactions based on my models of human behavior, and test these behavior controllers in real-world human-robot interactions.

Bio

Henny Admoni is a PhD candidate at the Social Robotics Laboratory in the Department of Computer Science at Yale University, where she works with Professor Brian Scassellati. This winter, Henny will begin as a Postdoctoral Fellow at the Robotics Institute at Carne-gie Mellon University, working with Siddhartha Srinivasa. Henny creates and studies intelligent, autonomous robots that improve people’s lives by providing assistance in social environments like homes and offices. Her dissertation research investigates how ro-bots can recognize and produce nonverbal behaviors, such as eye gaze and pointing, to make human-robot interactions more natural and effective for people. Her interdisciplinary work spans the fields of artificial intelligence, robotics, and cognitive psychology. Henny holds an MS in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University. Henny’s scholarship has been recognized with awards such as the NSF Graduate Research Fellowship, the Google Anita Borg Memori-al Scholarship, and the Palantir Women in Technology Scholarship.

Ilge Akkaya PhD Candidate University of California at Berkeley Compositional Actor-Oriented Learning and Optimization for Swarm ApplicationsRapid growth of networked smart

sensors today offer unprecedented volumes of continually stream-ing data, which renders many traditional control and optimization techniques ineffective for designing large-scale applications. The overarching goal of my graduate studies has been enabling seam-less composition of distributed dynamic swarm applications. In this regard, I work on developing actor-oriented frameworks for deter-ministic and compositional heterogeneous system design.

A primary goal of my graduate work is to mitigate the heteroge-neity within Internet-of-Things applications by presenting an ac-tor-oriented framework, which enables developing compositional learning and optimization applications that operate on streaming data. Ptolemy Learning, Inference, and Optimization Toolkit (PILOT) achieves this by presenting a library of reusable interfaces to ma-chine learning, control and optimization tasks for distributed sys-tems. A key goal of PILOT is to enable system engineers who are not experts in statistics and machine learning to use the toolkit in order to develop applications that rely on on-line estimation and in-ference. In this context, we provide domain-specific specializations of general learning and control techniques, including parameter estimation and decoding on Bayesian networks, model-predictive control, and state estimation. Recent and ongoing applications of the framework include cooperative robot control, real-time audio event detection, and constrained reactive machine improvisation.

A second branch of my research aims at maintaining separa-tion-of-concerns in model-based design. In industrial cyber-physi-cal systems, composition of sensors, middleware, computation and communication fabrics yields a highly complex and heterogeneous design flow. Separation-of-concerns becomes a crucial quality in model-based design of such systems. We introduce the aspect-ori-ented modeling (AOM) paradigm, which addresses this challenge by bridging actor-oriented modeling with aspect-oriented abstrac-tions. AOM specifically enables learning and optimization tasks to become aspects within a complex design flow, while greatly im-proving scalability and modularity of heterogeneous applications.

Bio

Ilge Akkaya is a PhD candidate in the Electrical Engineering and Computer Science department at UC Berkeley, working with Prof. Edward A. Lee. She received the BS degree in Electrical and Elec-tronics Engineering from Bilkent University, Ankara, Turkey in 2010. During her graduate studies, she explored systems engineering for distributed cyber-physical systems, with a focus on distributed smart grid applications and cooperative mobile robotic control. Her thesis work centers around actor-oriented machine learning inter-faces for distributed swarm applications.

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Sara Alspaugh PhD Candidate University of California at Berkeley

Characterizing Data Exploration Behavior to Identify Opportunities for Automation

Exploratory analysis is undertak-en to familiarize oneself with a

dataset. Despite being a necessary part of any analysis, it remains a nebulous art defined by an attitude and a collection of techniques, rather than a systematic methodology. It typically involves manu-ally making hundreds to thousands of individual function calls or small interactions with a GUI in order to obtain different views of the data. It is not always clear which views will be effective for a given dataset or question, how to be systematic about which views to examine, or how to map a high-level question into a series of low-level actions to answer it. This results in unnecessary repetition, disrupted mental flow, ad hoc and hard-to-repeat workflows, and inconsistent exploratory coverage. Identifying useful, repeatable exploration workflows, opportunities for automation of tedious tasks, and intelligent interfaces better suited for expressing explor-atory questions, all require a better understanding of data explora-tion behavior. We seek this through three means:

We analyze interaction records logged from data analysis tools–to identify behavioral patterns and assess the utility of log data for building intelligent assistance and recommendation algorithms that learn from user behavior. Preliminary results reveal that while logs can say which functions are used in which contexts, more com-prehensive instrumentation and collection is likely needed to train intelligent exploration assistants.

We interview experts about their data exploration habits and frus-trations–to identify good exploratory workflows and ascertain im-portant features not provided by existing tools. Preliminary results reveal opportunities to make data exploration more thorough and efficient.

We design and evaluate a prototype for obtaining quick data over-views–to assess new interface elements designed to better match data exploration needs. Preliminary results suggest that small simple automation in existing tools would decrease user effort, in-crease exploratory coverage, and help users identify erroneous as-sumptions more readily.

Bio

Sara Alspaugh is a computer scientist and PhD candidate at the UC Berkeley. In her research, she mines user interaction records logged from data analysis tools to better characterize data exploration be-havior, identify challenges and opportunities for automation, and improve system and interface design. She also conducts qualitative research through interview studies with expert analysts and usabil-ity evaluations of data exploration tools; and has prototyped new interfaces to help users get an overview of their data. More broadly, her research interests include data science, data mining, visualiza-tion, and user interaction with data analysis tools. She is a member of the AMPLab and is advised by Randy Katz and Marti Hearst. She received her MS in Computer Science from UC Berkeley in 2012 and her BA in Computer Science from the University of Virginia in 2009. She is the recipient of an NSF Graduate Fellowship, a Tableau Fel-lowship, and a Department Chair scholarship.

Elnaz Banan Sadeghian PhD Candidate Georgia Institute of Technology

Detector for Two-Dimensional Magnetic Recording

The data industry such as Google, Facebook, Yahoo, and also many

other organizations, rely heavily on data storage facilities to store their valuable data. Hard disk drives, due to their reliability and ex-tremely cheap price, form a main part of these data storage facilities. The disk drive industry is currently pursuing a huge increase in the recorded data density up to 10 Terabits per square inch of the me-dium through two-dimensional magnetic recording (TDMR). I work toward realization of this technology, specifically, to design a detec-tor which can recover the data from extremely dense hard drives. This is a challenge, in part because this novel technology shrinks the widths of the data tracks to such an extent that an attempt to read data from one track will inevitably lead to interference from neighboring tracks, and in part because of the challenging nature of the magnetic medium itself. The combination of interference between different tracks and along adjacent bits on each track is a key challenge for TDMR and motivates the development of two-di-mensional signal processing strategies of manageable complexity to mitigate this two-dimensional interference. To address this issue, we have designed a novel detection strategy for TDMR recording channel with multiple read heads. Our method suppresses the in-tertrack interference and thereby reduces the detection problem to a traditional one-dimensional problem, so that we may leverage existing one-dimensional iterative detection strategies. Simulation results show that our proposed detector is able to reliably recover five tracks from an array of five read heads at an acceptable signal-to-noise ratio. Further, we are working on a detector which also performs the task of synchronizing the reader and the writer clock speeds so that the data can be extracted more accurately. Obtained results from this research can help greatly increase hard disk capac-ities through TDMR.

Bio

Elnaz Banan Sadeghian received the BS degree in electrical engi-neering from Shahid Beheshti University, Tehran, Iran, in 2005, and the M.S. degree in Biomedical Engineering from Amirkabir Univer-sity of Technology, Tehran, Iran, in 2008. She is currently pursuing her PhD degree in electrical engineering at the Georgia Institute of Technology, Atlanta, Georgia, USA. Her current research interests are in the area of signal processing and communication theory, including synchronization, equalization, and coding as applied to magnetic recording channels.


Katherine Bouman PhD Candidate Massachusetts Institute of Technology

Visual Vibrometry: Estimating Material Properties from Small Motions in Video

The estimation of material prop-erties is important for scene understanding, with many applications in vision, robotics, and structural engineering. We have connected fundamentals of vibration mechanics with computer vision tech-niques in order to infer material properties from small, often im-perceptible motion in video. Objects tend to vibrate in a set of pre-ferred modes. The shapes and frequencies of these modes depend on the structure and material properties of an object. Focusing on the case where geometry is known or fixed, we have shown how information about an object’s modes of vibration can be extracted from video and used to make inferences about that object’s materi-al properties. We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high-speed and regular framerate video.

Bio

Katherine Bouman received a BSE in Electrical Engineering from University of Michigan, Ann Arbor, MI in 2011 respectively and an S.M. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT), Cambridge, MA in 2013. She is currently a PhD candidate in the Computer Vision group at MIT, working under the supervision of Prof. William Freeman. Katherine is the recipient of the NSF Graduate Fellowship, the Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowship, and is a Goldwater Scholar. Her research interests include computer vision, computational photography, and inverse imaging algorithms.

Carrie Cai PhD Candidate Massachusetts Institute of Technology

Wait-Learning: Leveraging Wait Time for Education

The busyness of daily life makes it hard to find time for informal learning. Yet, learning typically

requires significant time and effort, with repeated exposures to ed-ucational content on a recurring basis. My work introduces the con-cept of wait-learning: leveraging wait time for education. Despite the struggle to find time for learning, there are numerous times in a day that are wasted due to brief moments of waiting, such as wait-ing for the elevator, waiting for wifi to connect, or waiting for an instant message reply. Combining wait time with productive work opens up a new class of software systems that overcomes the prob-lem of limited time while addressing the frustration often associat-ed with waiting.

My goal is to understand how to detect and manage these waiting moments, and to discover essential design principles for wait-learn-ing systems. I have designed and built several systems that enable wait-learning: WaitChatter delivers second-language vocabulary exercises while users wait for instant message replies, and Flash-Suite integrates learning across diverse kinds of waiting, including elevators, wifi, and email loading. Through developing and evaluat-ing these systems, we identify waiting moments to use for learning, and ways to encourage learning unobtrusively while maximizing engagement. A study of WaitChatter with 20 participants found that wait-learning can be an effective and engaging way to learn. During two weeks of casual instant messaging, participants learned and retained an average of 57 Spanish and French words, or about four new words per day.

Bio

Carrie is a PhD student in Computer Science at MIT CSAIL. Her dis-sertation project focuses on wait-learning: leveraging wait time for education. Broadly, she is interested in developing systems that help humans learn and improve productivity in environments with limited time. Her research brings together disciplines in hu-man-computer interaction, education, attention management, and productivity. Carrie holds a B.A. in Human Biology and M.A. in Edu-cation from Stanford University.


Precious Cantú Fulbright Postdoctoral Fellow École Polytechnique Fédérale de Lausanne

Patterning via Optical Satu-rable Transitions

For the past 40 years, optical lithography has been the pat-terning workhorse for the semi-

conductor industry. However, as integrated circuits have become more and more complex, and as device geometries shrink, more innovative methods are required to meet these needs. In the far-field, the smallest feature that can be generated with light is lim-ited to approximately half the wavelength. This, so called far-field diffraction limit or the Abbe limit (after Prof. Ernst Abbe who first recognized this), effectively prevents the use of long-wavelength photons >300nm from patterning nanostructures <100nm. Even with a 193nm laser source and extremely complicated processing, patterns below ~20nm are incredibly challenging to create. Sources with even shorter wavelengths can potentially be used. However, these tend be much more expensive and of much lower brightness, which in turn limits their patterning speed. Multi-photon reactions have been proposed to overcome the diffraction limit. However, these require very large intensities for modest gain in resolution. Moreover, the large intensities make it difficult to parallelize, thus limiting the patterning speed. In this dissertation, a novel nanopat-terning technique using wavelength-selective small molecules that undergo single-photon reactions, enabling rapid top-down nanopatterning over large areas at low-light intensities, thereby allowing for the circumvention of the far-field diffraction barrier is developed and experimentally verified. This approach, which I refer to as Patterning via Optical Saturable Transitions (POST) has the potential for massive parallelism, enabling the creation of nano-structures and devices at a speed far surpassing what is currently possible with conventional optical lithographic techniques. The fundamental understanding of this technique goes beyond optical lithography in the semiconductor industry and is applicable to any area that requires the rapid patterning of large-area two or three-di-mensional complex geometries.

Bio

Dr. Precious Cantú is a Postdoctoral Researcher in the Materials Sci-ence and Engineering Department at École Polytechnique Fédérale de Lausanne (EPFL), where she works with Professor Francesco Stel-lacci in the Supramolecular Nanomaterials and Interfaces Laborato-ry. She recently received her PhD in Electrical Engineering from the University of Utah, advised by Prof. Rajesh Menon. Her research area of interest is Optics and Nanofabrication, with a specific focus on extending the spatial resolution of optics to the nanoscale. Her PhD dissertation focused on developing a novel nanopatterning tech-nique using wavelength-selective small molecules.

She is the recipient of the National Science Foundation Graduate Research Fellowship (NSF GRFP), University of Utah Nanotechnol-ogy Training Fellowship, Global Entrepreneurship Monitor Con-sortium (GEM) Fellowship, More Graduate Education at Mountain States Alliance (MGE/MSA) Fellowship, and The Fulbright U.S. Schol-ars Fellowship.

Peggy Chi PhD Candidate University of California at Berkeley

Designing Video-Based Interactive Instructions

When aiming to accomplish unfa-miliar, complicated tasks, people often search for online helps to

follow instructions shared by experts or hobbyists. Although the availability of content sharing sites such as YouTube and Blogger has led to an explosion in user-generated tutorials, it remains a challenge for tutorial creators to offer concise and effective content for learners to put into actions. From using software applications, performing physical tasks such as machine repair and cooking, to giving a lecture, each domain involves specific “how-to” knowledge with certain degree of complexity. Authors therefore need to care-fully design what and when to introduce an important concept in addition to accurately performing the tasks.

My research introduces video editing, recording, and playback tools optimized for producing and consuming instructional demonstra-tions. We focus on videos as they are commonly used to capture a demonstration contained with visual and auditory details. Using video and audio analysis techniques, our goal is to dramatically in-crease the quality of amateur-produced instructions, which in turn improves learning for viewers to interactively navigate. We show a series of proposed systems that create effective tutorials to support this vision, including MixT that automatically generates mixed-me-dia software instructions, DemoCut that automatically applies vid-eo editing effects to a recording of a physical demonstration, and DemoWiz that provides an increased awareness of upcoming ac-tions through glanceable visualizations.

Bio

Pei-yu (Peggy) Chi designs intelligent systems that enhance and improve everyday experiences. She is currently a fifth-year PhD stu-dent in Computer Science at UC Berkeley, working with Prof. Bjoern Hartmann on computer-generated interactive tutorials. She re-ceived the Google PhD Fellowship in Human Computer Interaction (2014-2016) and the Berkeley Fellowship for Graduate Study (2011-2013). Peggy earned her MS in Media Arts and Sciences in 2010 from the MIT Media Lab, where she was awarded as a lab fellow and worked with Henry Lieberman at the Software Agents Group. She also holds a MS in Computer Science in 2008 from National Taiwan University, where she worked with Hao-hua Chu at the UbiComp Lab.

Peggy’s research in Human-Computer Interaction focuses on novel authoring tools for content creation. Her recent work published at top HCI conferences includes: tutorial generation for software ap-plications and physical tasks, designing and scripting cross-device interactions, and interactive storytelling for sharing personal media.


SeyedehAida (Aida) Ebrahimi PhD Candidate Purdue University Droplet-Based Impedance Spectroscopy for Highly-Sensitive Biosensing within Minutes

Rapid detection of biomolecules in small volumes of highly diluted solutions is of essential interest in various applications, such as food safety, homeland security, fast drug screening, and addressing the global issue of antibiotic resistance. Toward this goal, we developed a label-free, electrical approach which is based on (i) evaporation-in-duced beating of diffusion limit for reducing the sensor response time and (ii) continuous monitoring of non-Faradic impedance of an evaporating droplet containing the analytes. Small droplets are deposited and pinned on a multifunctional, specially designed su-perhydrophobic sensor which results in highly-controlled evapora-tion rate, essential for highly-precise data acquisition. Our method is based on the change of the droplet’s impedance due to ionic modulation caused by evaporation. The time-multiplexing feature of the developed platform results in a remarkably reduced data variation, which is necessary for a reliable biosensing assay. Fur-thermore, we examined applicability of the developed technique as a fast, label-free platform for: improving the detection limit of classical methods by five orders of magnitude (detection of atto-molar concentration of biomolecules), selective identification of DNA hybridization (down to nM concentration, without any probe immobilization), and bacterial viability (detection is achieved with-in minutes, as opposed to hours in conventional methods). More specifically, the proposed viability assay relies on a basis fundamen-tally different from most bacterial viability assays which rely on cell multiplication. Instead, our method is based on modulation of the osmotic pressure to trigger cells to modify their surroundings. The developed paradigm eliminates the need for bulky reference elec-trodes (which impose integration challenges), requires only a few microliter sample volume, and is cost-effective and integrable with the microfabrication processes. It has therefore the potential for in-tegration in portable, array-formatted, point-of-care applications.

Bio

Aida Ebrahimi received her BSc and MSc degrees both in Electrical and Computer Engineering from University of Tehran, Iran. Her Mas-ter’s project was on fabrication and characterization of highly sen-sitive capacitive sensors and actuators based on Branched Carbon Nanotubes (BCNTs). In 2012, she joined CEED group, under super-vision of Prof. M. A. Alam at Purdue University, West Lafayette, IN, USA. She is currently pursuing a PhD degree in ECE. The title of her dissertation is ‘Droplet-based non-Faradaic Impedance Sensing for Combating Antibiotic Resistance’.

During her academic life, Aida has developed the required skills to approach scientific problems. She has been involved in various, yet connected, projects whose outcome has been published in 15 peer-reviewed journal articles and more than 10 conference pro-ceedings. She enjoys diversity in scientific thinking and intertwin-ing various disciplines to advance the state of the art of a specific problem, especially in health-related applications. Aida is a recip-ient of Meissner Fellowship Award (Purdue University, 2011) and Bilsland Dissertation Fellowship Award (Purdue University, 2015).

Hannah Clevenson PhD Candidate Massachusetts Institute of Technology

Sensing and Timekeeping using a Light-Trapping Diamond Waveguide

Solid-state quantum sensors are attracting wide interest because

of their sensitivity at room temperature. In particular, the spin properties of individual nitrogen–vacancy (NV) color centers in diamond make them outstanding nanoscale sensors of magnetic fields, electric fields, and temperature under ambient conditions. Recent work on NV ensemble-based magnetometers, inertial sen-sors, and clocks has employed unentangled color centers to realize significant improvements in sensitivity. However, to achieve this potential sensitivity enhancement in practice, new techniques are required to excite efficiently and to collect the optical signal from large NV ensembles. Here, we introduce a light-trapping diamond waveguide geometry with an excitation efficiency and signal col-lection that enables in excess of 5% conversion efficiency of pump photons into optically detected magnetic resonance (ODMR) fluo-rescence—an improvement over previous single-pass geometries of more than three orders of magnitude. This marked enhancement of the ODMR signal enables precision broadband measurements of magnetic field and temperature in the low-frequency range, oth-erwise inaccessible by dynamical decoupling techniques. We also use this device architecture to explore other precision sensing and timekeeping applications.

Bio

Hannah earned her BE (cum laude) in electrical engineering from Cooper Union in 2011. She was a NASA MUST scholar and spent four summers working in the nanotechnology division at NASA Ames Research Center on the Microcolumn Scanning Electron Mi-croscope (MSEMS) project and led a microgravity flight experiment. She finished her masters degree at Columbia University in 2013. She is a NASA Space Technology Research Fellow and spent a summer as a visiting technologist in the Quantum Sciences and Technology group at JPL. She is currently a PhD candidate at MIT, splitting her time between Dirk Englund’s lab on campus and Danielle Braje’s lab in group 89 at MIT Lincoln Laboratory. Her current research focuses on precision sensing and timekeeping based on large ensembles of NV centers in diamond.


Motahareh Eslamimehdiabadi PhD Candidate University of Illinois at Urbana-Champaign Reasoning about Invisible Algorithms in News Feeds

Our daily digital life is full of al-gorithmically selected content

such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users’ experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms’ existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation al-gorithm’s existence at all. Initial reactions for these previously un-aware participants were surprise and anger. We developed a sys-tem, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends’ decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becom-ing aware of the algorithm’s presence, however, algorithmic aware-ness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

Bio

Motahhare Eslami is a 4th year PhD candidate at Computer Sci-ence department, University of Illinois at Urbana-Champaign. Her research interests are in social computing, human computer inter-action and data mining areas. She is interested in performing re-search to analyze and understand people’s behavior in online social networks. Her recent work has focused on the effects of feed per-sonalization in social media and how the awareness of filtering al-gorithm’s existence affects users’ perception and behavior. Her work has published at prestigious conferences and also appeared inter-nationally in the press-in the Washington Post, TIME, MIT Technolo-gy Review, New Scientist, the BBC, CBC Radio, Oglobo (a prominent Brazilian newspaper), numerous biogs, Fortune, and more. Motah-hare has been nominated as a Google PhD Fellowship Nominee (2015) by University of Illinois as one of the two students from the entire College of Engineering. Her research has received honorable mention award at Facebook Midwest Regional Hackathon 2013 and the best paper award at CHI 2015.

Virginia Estellers Postdoctoral Fellow University of California at Los Angeles Robust Models and Efficient Algorithms for Imaging

I work on mathematical modeling and computational techniques for imaging. I am interested in the

theoretical and physical aspects of the acquisition of images, their mathematical representations, and the development of efficient al-gorithms to extract information from them. To this purpose, I focus on three lines of research.

Better Models in Image Processing: My dissertation focused on variational models for inverse problems in imaging, that is, the de-sign of minimization problems that reconstruct or analyze an im-age from incomplete and corrupted measurements. To overcome the ill-posed nature of these problems, prior knowledge about the solution — its geometry, shape, or smoothness — is incorporated into a mathematical model that both matches the measurements and is physically meaningful.

Efficient Algorithms: In the same way that simplifying an algebraic expression speeds its computation and reduces numerical errors, developing an efficient algorithm reduces the computational cost and errors of the numerical minimization. For this reason, my work focuses also on developing algorithms tailored to each problem to overcome the limitations of non-differentiable functionals, high-or-der derivatives, and non-convex problems.

Stepping out of the Image plane: Computer Vision analyzes 3D scenes from 2D images or videos and therefore requires to step out of the image plane and develop models that account for the 3D nature of the scene, modeling their geometry and topology to account for the occlusions and shadows observable in videos and images.

My research, in a nutshell, brings together models and algorithms into solid mathematical grounds to designs techniques that only extract the information that is meaningful for the problem at hand. It incorporates the knowledge available on the solution into the mathematical model of the problem, chooses a discretization suit-ed to the object being imaged, and designs optimization strategies that scale well and are easy to parallelize.

Bio

Dr. Estellers received her PhD in image processing from Ecole Poly-thechnique Federale de Lausanne in 2013, and joined the UCLA Vision Lab as a postdoctoral fellow with an SNSF fellowship. Previ-ous to that, she completed Bachelor and Master studies at the Poly-technic University of Catalonia in both Mathematics and Electrical Engineering.


Liyue Fan Postdoctoral Research Associate University of Southern California Preserving Individual Privacy in Big Data Analytics

We live in the age of big data. With an increasing number of people, devices, and sensors connected with digital networks, individual

data now can be largely collected and analyzed by data mining ap-plications for social good as well as for commercial interests. Howev-er, the data generated by individual users exhibit unique behavioral patterns and sensitive information, and therefore must be trans-formed prior to the release for analysis. The AOL search log release in 2006 is an example of privacy catastrophe, where the searches of an innocent citizen were quickly re-identified by a newspaper jour-nalist. In this talk, I present a novel framework to release continuous aggregation of private data for an important class of real-time data mining tasks, such as disease outbreak detection and web mining, to name a few. The key innovation is that the proposed framework captures the underlying dynamics of the continual aggregate statis-tics with time series state-space models, and simultaneously adopts filtering techniques to correct the observed, noisy data. It can be shown that the new framework provides a rigorous, provable priva-cy guarantee to individual data contributors without compromising the output analysis results. I will also talk about my current research, including the extension of the framework to spatial crowd-sourcing and privacy-preserving machine learning in a distributed research network.

Bio

Liyue Fan is a postdoctoral research associate at the Integrated Me-dia Systems Center at USC. She holds a PhD in Computer Science and Informatics from Emory University and a BSc in Mathematics from Zhejiang University in China. Her PhD dissertation research centers around the development of data publication algorithms which provide rigorous guarantee for individual privacy without compromising output utility. After joining USC, she also works on spatial crowd-sourcing, transportation, and healthcare informatics.

Fei Fang PhD Candidate University of Southern California Towards Addressing Spatio-Temporal Aspects in Security GamesMy research aims to provide game-theoretic solutions for fun-damental challenges of security

resource optimization in the real-world, in domains ranging from infrastructure protection to sustainable development. Whereas first generation of “security games” research provided algorithms for op-timizing security resources in mostly static settings, my thesis ad-vances the state-of-the-art to a new generation of security games, handling massive games with complex spatio-temporal settings and leading to real-world applications that have fundamentally altered current practices of security resource allocation. My work provides the first algorithms and models for advancing three key aspects of spatio-temporal challenges in security games. First, fo-cusing on games where actions are taken over continuous time (for example games with moving targets such as ferries and refugee supply lines), I provide an efficient linear-programming-based solu-tion while accurately modeling the attacker’s continuous strategy. This work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City in past few years and funda-mentally altering previously used tactics. Second, for games where actions are taken over continuous space (for example games with forest land as target), I provide an algorithm computing the optimal distribution of patrol effort. Third, my work addresses challenges with one key dimension of complexity — the temporal change of strategy. Motivated by the repeated interaction of players in do-mains such as preventing poaching and illegal fishing, I introduce a novel game model that accounts for temporal behavior change of opponents and provide algorithms to plan effective sequential defender strategies. Furthermore, I incorporate complex terrain information and design the PAWS application to combat illegal poaching, which generates patrol plans with detailed patrol routes for local patrollers. PAWS has been deployed in a protected area in Southeast Asia, with plans for worldwide deployment.

Bio

Fei Fang is a PhD candidate in Department of Computer Science at University of Southern California. She is working with Professor Milind Tambe at Teamcore Research group. She received her bach-elor degree from the department of Electronic Engineering, Tsing-hua Unviersity in July, 2011. Her research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory with applications to security and sustainability do-mains. Her work on “Protecting Moving Targets with Mobile Re-sources” has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. This work has led to her receiving the Meritorious Team Commendation from Commandant of the US Coast Guard and Flag Letter of Appreciation from Vice Admiral and she is named a poster competition finalist in the First Conference on Validating Models of Adversary Behaviors (2013). Her work on “When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing” won the Outstanding Paper Award in IJCAI-15 Computational Sustain-ability Track. She is the chair of the AAAI Spring Symposium 2015 on Applied Computational Game Theory and the recipient of WiSE Merit Fellowship (2014).


Giulia Fanti PhD Candidate University of California at Berkeley Spy vs. Spy: Anonymous Messaging

Anonymous microblogging plat-forms, such as Secret, Yik Yak, and Whisper have emerged as im-

portant tools for sharing one’s thoughts without fear of judgment by friends, the public, or authority figures. These platforms provide anonymity by allowing users to share content (e.g., short messag-es) with their peers without revealing authorship information to users. However, recent advances in rumor source detection show that existing messaging protocols, including those used in the men-tioned anonymous microblogging applications, leak authorship in-formation when the adversary has global access to metadata. For example, if an adversary can see which users of a messaging service received a particular message, or the timestamps at which a subset of users received a given message, the adversary can infer the mes-sage author’s identity with high probability. We introduce a novel anonymous messaging protocol, which we call adaptive diffusion, that is designed to resist such adversaries. We show that adaptive diffusion spreads messages quickly while achieving provably-op-timal anonymity guarantees when the underlying messaging net-work is an infinite regular tree. Simulations on real social network data show that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles.

Bio

Giulia Fanti is a 6th year PhD student at the University of Califor-nia-Berkeley, studying privacy-preserving algorithms under Profes-sor Kannan Ramchandran. She received her M.S. in EECS from the University of California-Berkeley in 2012 and her B.S. in Electrical and Computer Engineering from Olin College of Engineering in 2010. She is a recipient of the National Science Foundation Graduate Re-search Fellowship, as well as a Best Paper Award at ACM Sigmetrics 2015 for her work on anonymous rumor spreading, in collaboration with Peter Kairouz, Professor Sewoong Oh and Professor Pramod Viswanath of the University of Illinois at Urbana-Champaign.

Lu FengPostdoctoral FellowUniversity of Pennsylvania

Assuring the Safety and Security of Cyber-Physical SystemsCyber-Physical Systems (CPS)—also called the Safety-Critical In-ternet of Things—are smart sys-

tems that include co-engineered interacting networks of physical and computational components. These highly interconnected and integrated systems provide new functionalities to improve quality of life and enable technological advances in critical areas, such as smart healthcare, transportation, manufacturing, and energy. The increas-ing complexity and scale of CPS, with high-level expectations of au-tonomous operation, predictability and robustness, in the presence of environmental uncertainty and resource limitations, pose signifi-cant challenges for assuring the safety and security of CPS.

My research is focused on assuring the safety, security and depend-ability of CPS, through formal methods and data-driven approaches, with particular emphasis on probabilistic modeling and quantita-tive verification. My doctoral thesis work improves the scalability of probabilistic model checking—a powerful formal verification meth-od that focuses on analyzing quantitative properties of stochastic systems—by developing, for the first time, fully automated compo-sitional verification techniques for probabilistic systems.

My current postdoctoral research includes two themes. One theme is medical CPS, which are life-critical, context-aware, networked sys-tems of medical devices. For example, I have worked on assuring the interoperability of on-demand plug & play medical devices, and model-based development of high-confidence medical devices.

Another theme of my current work is human-in-the-loop CPS. I col-laborate with clinicians and develop data-driven modeling frame-work for studying the behavior of Diabetic patients who depend on insulin pumps. The research outcome could potentially assist in developing safer, more effective, and even personalized treatment devices. In another project, with my collaborators at the Air Force Research Lab, I develop approaches for synthesizing provably cor-rect human-in-the-loop control protocols for unmanned aerial vehi-cles (UAV). My other on-going projects include human factors in CPS security assurance, and operator behavior signatures for the haptic authentication of surgical robots.

Bio

Lu Feng is postdoctoral fellow at the PRECISE Center and Depart-ment of Computer & Information Science at the University of Penn-sylvania, advised by Professor Insup Lee. She received her DPhil (PhD) in Computer Science from the University of Oxford in 2014, under the supervision of Professor Marta Kwiatkowska. She also holds a B.Eng. in Information Engineering from the Beijing University of Posts and Telecommunications and a M.Phil. in Computer Speech, Text and Internet Technology from the University of Cambridge. Lu is a recipient of the prestigious James S. McDonnell Foundation Post-doctoral Fellowship, which only selects 10 fellows internationally and trans-disciplinary each year. She has also received various other awards, including the ACM SIGMOBILE N2 Women Young Researcher Fellowship, UK Engineering and Physical Sciences Research Council Graduate Scholarship, and Cambridge Trust Scholarship.


Kathleen Fraser PhD Candidate University of Toronto Text and Speech Processing for the Detection of Dementia

It has been shown that language can be a sensitive barometer of cognitive health. However, cur-rent approaches to screening and

diagnosis for dementia do not typically include a detailed analysis of spontaneous speech because the manual annotation of lan-guage samples is far too time-consuming. Using methods from natural language processing and machine learning, we have been able to extract relevant linguistic and acoustic features from short speech samples and their transcripts to predict whether the speak-er has Alzheimer’s Disease with 92% accuracy. We have also investi-gated a type of dementia called primary progressive aphasia (PPA), in which language ability is the primary impairment. In addition to determining whether participants had PPA or not, we were able to distinguish between semantic-variant PPA and agrammatic-variant PPA by incorporating features to detect signs of empty speech and syntactic simplification. Another component of my current work involves improving automatic speech recognition for cognitive as-sessment. By developing computational tools to collect, analyze, and interpret language data from cognitively impaired speakers, I hope to provide the groundwork for numerous potential applica-tions, including remote screening, support for diagnosis, assistive technologies for community living, and the quantitative evaluation of therapeutic interventions.

Bio

Katie Fraser is a PhD candidate at the University of Toronto in the Computational Linguistics group, where her main research interests are text processing, automatic speech recognition, and machine learning. She is particularly interested in how these techniques can be used to assess potential cognitive impairment. She received a Master of Computer Science degree from Dalhousie University in Halifax, Nova Scotia, where she developed techniques for reducing noise and blur in microscope images. Before that, she researched the structure and dynamics of glass-forming liquids as part of her Bachelor of Science in Physics at St. Francis Xavier University in An-tigonish, Nova Scotia.

Marzyeh Ghassemi PhD CandidateMassachusetts Institute of Technology Estimating the Response and Effect of Clinical InterventionsMuch prior work in clinical mod-eling has focused on building

discriminative models to detect specific easily coded outcomes with little clinical utility (e.g., hospital mortality) under specific ICU settings, or understanding the predictive value of various types of clinical information without taking interventions into account.

In this work, we focus on understanding the impact of interventions on the underlying physiological reserve of patients in different clin-ical settings. Reserve can be thought of as the latent variability in patient response to treatment after accounting for their observed state. Understanding reserve is therefore important to performing successful interventions, and can be used in many clinical settings.

I attempt to understand reserve in response to intervention in two settings: 1) the response of intensive care unit (ICU) patients to common clinical interventions like vassopressor and ventilation administration in the ICU, and 2) the response of voice patients to behavioral and surgical treatments in an ambulatory outpatient set-ting. In both settings, we use large sets of clinical data to investigate whether specific interventions are meaningful to patients in an em-pirically sound way.

Bio

Marzyeh Ghassemi is a PhD student in the Clinical Decision Making Group (MEDG) in MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Prof. Peter Szolovits. Her research uses machine learning techniques and statistical modeling to predict and stratify relevant human risks.

Marzyeh is interested in creating probabilistic latent variable mod-els to estimate the underlying physiological state of patients during critical illnesses. She is also interested in understanding the devel-opment and progression of conditions like hearing loss and vocal hyperfunction using a combination of sensor data, clinical observa-tions, and other physiological measurements.

While at MIT, Marzyeh has served on MIT’s Women’s Advisory Group Presidential Committee, as Connection Chair to the Women in Machine Learning Workshop, on MIT’s Corporation Joint Advisory Committee on Institute-wide Affairs, and on MIT’s Committee on Foreign Scholarships. Prior to MIT, Marzyeh received two B.S. de-grees in computer science and electrical engineering with a mi-nor in applied mathematics from New Mexico State University as a Goldwater Scholar, and a MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar. She also worked at In-tel Corporation in the Rotation Engineering Program, and then as a Market Development Manager for the Emerging Markets Platform Group.


Elena Leah Glassman PhD CandidateMassachusetts Institute of Technology Systems for Teaching Programming and Hardware Design at ScaleIn a massive open online course (MOOC), a single programming

exercise may yield thousands of student solutions that vary in many ways, some superficial and some fundamental. Understanding large-scale variation in programs is a hard but important problem. For teachers, this variation can be a source of pedagogically valu-able examples and expose corner cases not yet covered by auto-grading. For students, the variation in a large class means that other students may have struggled along a similar solution path, hit the same bugs, and can offer hints based on that earned expertise.

I have developed three systems to explore solution variation in large-scale programming and computer architecture classes. (1) OverCode visualizes thousands of programming solutions us-ing static and dynamic analysis to cluster similar solutions. It lets teachers quickly develop a high-level view of student understand-ing and misconceptions and provide feedback that is relevant to many student solutions. (2) Foobaz clusters variables in student programs by their names and behavior so that teachers can give feedback on variable naming. Rather than requiring the teacher to comment on thousands of students individually, Foobaz generates personalized quizzes that help students evaluate their own names by comparing them with good and bad names from other students. (3) ClassOverflow collects and organizes solution hints indexed by the autograder test that failed or a performance characteristic like size or speed. It helps students reflect on their debugging or opti-mization process, generates hints that can help other students with the same problem, and could potentially bootstrap an intelligent tutor tailored to the problem. All three systems have been evalu-ated using data or live deployments in on-campus or edX courses with thousands of students.

Bio

Elena Glassman is an EECS PhD candidate at MIT Computer Science and Artificial Intelligence Lab, where she specializes in human-com-puter interaction. For her dissertation, Elena has created tools that help teach programming and hardware design to thousands of stu-dents at once. She uses theories from the learning sciences, as well as the pain points of students and teachers, to guide the creation of new systems for teaching and learning online and at scale. Ele-na earned both her MIT EECS BS and MEng degrees in ‘08 and ‘10, respectively, with a Ph.D. expected in ‘16. She has been a visiting researcher at Stanford and an intern at Google and Microsoft Re-search. She earned the NSF and NDSEG fellowships and MIT’s Amar Bose Teaching Fellowship. She also leads the MIT chapter of MEET, which helps teach gifted Palestinians and Israelis computer science and teamwork in Jerusalem.

Basak GulerPhD CandidatePennsylvania State University Interaction, Communication, and Computation in Information and Social Networks

Modern networks are designed to facilitate the interaction of

humans with computers. These networks consist of actors with possibly different characteristics, goals, and interests. My research takes a mathematical approach to modeling semantic and social networks. I study the fundamental limits of the information trans-ferred in real-world networks, and develop algorithms to make network applications human-centric. Unlike conventional commu-nication networks, this necessitates taking into account the seman-tic relationships between words, phrases, or clauses, as well as the personal background, characteristics, and knowledge bases of the interacting parties. These differences can in turn lead to various in-terpretations of the received information in a communication sys-tem.

Modern network systems should be able to operate under such am-biguous environments, and adapt to the interpretation differences of the communicating parties. My goal is to incorporate these indi-vidual characteristics for designing effective network models that can leverage and adapt to the semantic and social features of the interacting parties. To do this, my research takes an interdisciplin-ary approach, rooted in information theory and optimization, and incorporates social networks, and mathematical logic. As such, we consider a diverse set of problems ranging from lossless and lossy source coding to reliable communication with social structures. We identify the optimal strategies to represent a remotely observed phenomenon when the communicating parties have individual and common backgrounds, as well as optimal interaction protocols for exchanging messages with semantic relationships.

Bio

Basak Guler received her BSc degree in electrical and electronics engineering from Middle East Technical University (METU), Ankara, Turkey in 2009 and her M.Sc. degree in electrical engineering from Wireless Communications and Networking Laboratory, Pennsylva-nia State University, University Park, PA, in 2012. She is currently pur-suing the PhD degree and is a graduate research assistant with the Department of Electrical Engineering, Pennsylvania State Univer-sity, University Park, PA. Her research interests include information theory, social networks, semantic communications, source coding, data compression, interactive communication, and heterogeneous wireless networks.


Divya GuptaPhD CandidateUniversity of California at Los Angeles Hosting Services on an Untrusted CloudOutsourcing computation from a weak client to a more powerful server has received a lot of atten-tion in recent years. This is partly

due to the increasing interest in cloud computing, where the goal is to outsource all the computations to a (possibly untrusted) “cloud”. Though this is quickly becoming the predominant mode of day-to-day computation, it brings with it many security challenges, and there has been large numbers papers which address them. In our work, we expand the realm of outsourcing computation to more challenging security and privacy settings.

We consider a scenario where a service provider has created a soft-ware service and desires to outsource the execution of this service to an untrusted cloud. The software service contains secrets that the provider would like to keep hidden from the cloud. For exam-ple, the software might contain a secret database, and the service could allow users to make queries to different slices of this database depending on the user’s identity.

This setting presents significant challenges not present in previous works on outsourcing or secure computation because secrets in the software itself must be protected against an adversary that has full control over the cloud that is executing this software. Furthermore, we seek to protect knowledge of the software to the maximum ex-tent possible even if the cloud can collude with several corrupted users of this service.

In this work, we provide the first formalizations of security for this setting, yielding our definition of a secure cloud service scheme. We also provide constructions of secure cloud service schemes using cryptographic tools.

Bio

Divya Gupta is a doctoral candidate in the Department of Computer Science at University of California at Los Angeles, where she started in the Fall of 2011 under the supervision of Prof. Amit Sahai. Her research interests include cryptography, security, and theoretical computer science. Before coming to UCLA, she graduated with a B.Tech. and M.Tech from IIT Delhi.

Judy HoffmanPostdoctoral Research Associate University of California at Berkeley Adapting Deep Visual Models for Visual Recognition in the WildUnderstanding visual scenes is a crucial piece in many artificial

intelligence applications ranging from autonomous vehicles and household robotic navigation to automatic image captioning for the blind. Reliably extracting high-level semantic information from the visual world in real-time is key to solving these critical tasks safely and correctly. Existing approaches based on specialized recognition models are prohibitively expensive or intractable due to limitations in dataset collection and annotation. By facilitating learned information sharing between recognition models these ap-plications can be solved; multiple tasks can regularize one another, redundant information can be reused, and the learning of novel tasks is both faster and easier.

My work focuses on transferring learned information quickly and reliably between visual data sources and across visual tasks–all with limited human supervision. I aim to both formally understand and empirically quantify the degree to which visual models can be adapted and provide algorithms to facilitate information transfer.

Most visual recognition systems learn concepts directly from a large collection of manually annotated images/videos. A model which detects pedestrians requires a human to manually go through thou-sands or millions of images and indicate all instances of pedestrians. However, this model is susceptible to biases in the labeled data and often fails to generalize to new scenarios — a detector trained in Palo Alto may have degraded performance in Rome, or a detector trained in sunny weather may fail in the snow. Rather than require human supervision for each new task or scenario, my work draws on deep learning, transformation learning, and convex-concave opti-mization to produce novel optimization frameworks which transfer information from the large curated databases to real world scenar-ios. This results in strong recognition models for novel tasks and paves the way towards scalable visual understanding.

Bio

Judy Hoffman is a PhD candidate at UC Berkeley’s Computer Vision Group. She received her B.Sc. in Electrical Engineering and Comput-er Science from UC Berkeley in 2010. Her research lies at the inter-section of computer vision, transfer learning, and machine learning: she is interested in minimizing the amount of human supervision needed to learn new visual recognition models. Judy was awarded the NSF Graduate Research Fellowship in 2010 and the Rosalie M. Stern Fellowship 2010. She was the co-president of the Women in Computer Science and Engineering at UC Berkeley 2012-2013, the outreach and diversity officer for the Computer Science Graduate Association 2013-2014, and organized the first workshop for Wom-en in Computer Vision located at CVPR 2015.


Hui-Lin HsuResearch Assistant University of Toronto Reduction in the Photoluminescence Quenching for Erbium-Doped Amorphous Carbon Photonic Materials by Deuteration and Fluorination

The integration of photonic. materials into CMOS processing in-volves the use of new materials. A simple one-step metal-organic radio frequency plasma enhanced chemical vapor deposition sys-tem (RF-PEMOCVD) was deployed to grow erbium-doped amor-phous carbon thin films (a-C:(Er)) on Si substrates at low tempera-tures (<200°C). A partially fluorinated metal-organic compound, tris(6,6,7,7,8,8,8-heptafluoro-2,2-dimethyl-3,5-octanedionate) Erbium(+III) or abbreviated Er(fod)3, was incorporated in situ into a-C based host. It was found that the prominent room-tempera-ture photoluminescence (PL) signal at 1.54 µm observed from the a-C:H:F(Er) film is attributed to several factors including a high Er concentration, the large optical bandgap of the a-C:H host, and the decrease in the C-H quenching by partial C-F substitution of met-al-organic ligand. In addition, six-fold enhancement of Er PL was demonstrated by deuteration of the a-C host. Also, the effect of RF power and substrate temperature on the PL of a-C:D:F(Er) films was investigated and analyzed in terms of the film structure. PL signal in-creases with increasing RF power, which is the result of an increase in [O]/[Er] ratio and the respective erbium-oxygen coordination number. Moreover, PL intensity decreases with increasing substrate temperature, which is attributed to an increased desorption rate or a lower sticking coefficient of the fluorinated fragments during film growth and hence [Er] decreases. In addition, it is observed that Er concentration quenching begins at ~2.2 at% and continues to in-crease until 5.5 at% in the studied a-C:D:F(Er) matrix. This technique provides the capability of doping Er in a vertically uniform profile.

Bio

Hui-Lin Hsu is a PhD graduate in Electrical Engineering (Photonics) from the University of Toronto, with M.S. and B.S. degrees in Materi-als Science and Engineering from National Tsing Hua University, Tai-wan. Her research interest is in the areas of thin film and nano-mate-rial processing, material characterizations, and microelectronic and photonic devices fabrication. Hui-Lin has completed four different research projects (Organic Thin Film Transistors (OTFTs), Flexible Carbon Nanotubes Electrodes for Neuronal Recording, Si Nanowire for Optical Waveguide Interconnection Application, and Rare Earth doped Amorphous Carbon Based Thin Films for Light Guiding/Am-plifying Applications). Hui-Lin has also first authored 3 patents (1 in USA, 2 in Taiwan) and 5 SCI journal articles, co-authored 9 SCI journal articles, and 15 international conference presentations. She did internships at Taiwan Semiconductor Manufacturing Company (TSMC) and Industrial Technology Research Institute (ITRI). She is also a recipient of the 2008 scholarship for studying abroad from Taiwan government, and an invited participant for the 2007 Taiwan Semiconductor Young Talent Camp held by Applied Materials and 2015 ASML PhD master class.

Carlee Joe-WongPhD CandidatePrinceton University Smart(er) Data Pricing

Over the past decade, many more people have begun to use the In-ternet regularly, and the prolifer-ation of mobile apps allows them to use the Internet for more and

more tasks. As a result, data traffic is growing nearly exponentially. Yet network capacity is not expanding fast enough to handle this growth in traffic, creating a problem of network congestion. My re-search argues that the very diversity in usage that is driving growth in data traffic points to a viable solution for this fundamental capac-ity problem.

Smart data pricing reduces network congestion by looking at the users who drive demand for data. In particular, we ask what incen-tives will alter user demand so as to reduce congestion, and per-haps more importantly, what incentives should we offer users in practice? For instance, simply raising data prices or throttling data throughput rates will likely drive down demand, but also lead to vast user dissatisfaction. More sophisticated pricing schemes may not work in practice, as they require users to understand the pric-es offered and algorithms to predict user responses. We demon-strate the feasibility and benefits of a smart data pricing approach through end-to-end investigations of what prices to charge users, when to charge which prices, and how to price supplementary net-work technologies.

Creating viable pricing solutions requires not only mathematical models of users’ reactions to the prices offered, but also knowledge of systems-building and human-computer interaction. My work develops a feedback loop between optimizing the prices, offering them to users, and measuring users’ reactions to the prices so as to re-calibrate the prices over time. My current research expands on this pricing work by studying users’ incentives to contribute to-wards crowd-sourced data. Without properly designed incentive mechanisms, users might “free-ride” on others’ measurements or collect redundant measurements at a high cost to themselves.

Bio

Carlee Joe-Wong is a PhD candidate and Jacobus Fellow at Prince-ton University’s Program in Applied and Computational Mathemat-ics. Her research interests include network economics, distributed systems, and optimal control. She received her A.B. in mathematics in 2011 and her M.A. in applied mathematics in 2013, both from Princeton University. In 2013, she was the Director of Advanced Re-search at DataMi, a startup she co-founded in 2012 that commer-cializes new ways of charging for mobile data. DataMi was named a “startup to watch” by Forbes in 2014. Carlee received the INFORMS ISS Design Science Award in 2014 for her research on smart data pricing, and the Best Paper Award at IEEE INFOCOM 2012 for her work on the fairness of multi-resource allocations. In 2011, she re-ceived the National Defense Science and Engineering Graduate Fel-lowship (NDSEG).


Gauri JoshiPhD CandidateMassachusetts Institute of Technology Using Redundancy to Reduce Delay in Cloud Systems

It is estimated that by 2018, more than thirty percent of all digital

content will be stored and processed on the cloud. The term ‘cloud’ refers to a shared pool of a large number of connected servers, used to host services such as Dropbox, Amazon EC2, Netflix etc. The shar-ing of resources provides scalability and flexibility to cloud systems, but it also causes randomness in the response time of individual servers, which can result in large and unpredictable delays experi-enced by users. My research develops techniques to use redundan-cy to reduce delay, while using the available resources efficiently.

In cloud storage and computing systems, a task (for e.g. searching for a term on Google, or accessing a file from Dropbox) experiences random queuing and service delays at the machine it is assigned to. To reduce the overall latency, we can launch replicas of the task on multiple machines and wait for the earliest copy to finish, albeit at the expense of extra computing and network resources. We de-velop a fundamental understanding how the randomness in the response time of a server affects latency and cost of computing re-sources. This helps us find cost-efficient strategies of launching and canceling redundant tasks to minimize latency.

Achieving low latency is even more challenging in streaming ser-vices such as Netflix and Youtube because they require fast, in-or-der playback of packets. Another focus of my research is to develop erasure codes to transmit redundant combinations of packets, and minimize the number of interruptions in playback.

Bio

Gauri Joshi is a PhD candidate at MIT, advised by Prof. Gregory Wornell. She works on applying probability and coding theory to improve today’s cloud infrastructure. She received an S.M. in EECS from MIT in 2012, for which she received the William Martin memo-rial award for best thesis in Computer Science at MIT.

Before coming to MIT in 2010, she completed a B.Tech and M. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay. She was awarded the Institute Gold Medal of IIT Bom-bay, for highest GPA across all majors.

Gauri has received several other awards and honors including the Schlumberger Faculty for the Future fellowship (2012-15) and the Claude E. Shannon Research Assistantship (2015-16). She has had summer internships at Bell Labs (2012) and Google (2013, 14).

Ankita Arvind KejriwalPhD CandidateStanford University Scalable Low-Latency Indexes for a Key-Value Store

Many large-scale key-value stor-age systems sacrifice features like secondary indexing and/or con-

sistency in favor of scalability or performance. This limits the ease and efficiency of application development on these systems.

My work shows how a large-scale key-value storage system can be extended to provide secondary indexes in a fashion that is highly scalable and offers ultra low latency access. The architecture, called SLIK, enables multiple keys for each object, and allows indexes to be partitioned and distributed independently of their objects. SLIK represents index B+ trees using objects in the underlying key-value store. It uses an ordered write approach for object updates, which allows temporary inconsistencies between indexes and their ob-jects but masks those inconsistencies from applications. When im-plemented using RAMCloud as the underlying key-value store, SLIK performs indexed reads in 11 μs and writes in 30 μs; it supports in-dexes spanning thousands of nodes, and provides linear scalability for throughput. SLIK is also an order of magnitude faster than other state-of-the-art systems.

Bio

Ankita Kejriwal is a PhD candidate in the Computer Science depart-ment at Stanford University working with Prof. John Ousterhout. She enjoys working on problems in distributed systems. She is building RAMCloud, a low-latency datacenter storage system, along with the rest of her lab. Her recent project, called SLIK, extends a key-value store to enable scalable, low-latency indexes. She interned at MSR-SVC in 2013 with Marcos Aguilera and designed an algorithm for low-latency distributed transactions. Prior to graduate school, she completed her Bachelor in Computer Science at Birla Institute of Technology and Science - Pilani, Goa Campus.


Hana KhamfroushPostdoctoral ScholarPennsylvania State University On Propagation of Phenomena in Interdependent Networks

Operational networks of different types are often interdependent and interconnected. Many of to-

day’s infrastructures are organized in the form of interdependent networks. For example, the smart grid is controlled via the Inter-net, and the Internet is powered by the smart grid. A failure in one may lead to service degradation and possibly failure in the other. This failure procedure can cascade multiple times between the two interdependent networks and therefore, results in catastrophic widespread failures. Previous works that are modeling the inter-dependency between two networks are generally based on strong assumptions and specific applications, thus fail to capture import-ant aspects of real networks. Furthermore, most of the previous works only address the asymptotic behavior of the networks. To fill this gap, we focused on the temporal evolution of the phenomena propagation in interdependent networks. The goal is to identify the importance of the nodes in terms of their influence on the propa-gation phenomenon, and to design more efficient interdependent networks.

We proposed a general theoretical model for such a propagation, which captures several possible models of interaction among af-fected nodes. Our model is general in the sense that there is no assumption on the network topology, propagation model, or the capability of the network nodes (heterogeneity of the networks). The theoretical model allows us to evaluate small-scale networks. On the other hand, we implemented a simulator, which allows for the evaluation of larger scale networks for different types of ran-dom graphs, different models of coupling between networks, and different initial spreaders. Based on our analysis, we propose a new centrality metric designed for the interdependent networks that is shown to be more precise in identifying the importance of the nodes compared to the traditional centrality metrics. Our next step would be analyzing the phenomena propagation in time-varying interdependent networks.

Bio

Hana Khamfroush is a postdoctoral scholar in the Electrical Engi-neering and Computer Science department of Penn State Universi-ty, working with Prof. Thomas La Porta. She received her PhD with highest distinction from the University of Porto in Portugal and in Collaboration with Aalborg University of Denmark in Nov. 2014. Her PhD research focused on network coding for cooperation in dynamic wireless networks. Currently at PSU, she is working on interdependent networks, network recovery and network tomog-raphy. Her research interests include complex networks, computer networks, wireless communications, and mathematical models. She received a four-year scholarship from the ministry of science of Por-tugal for her PhD, and was awarded many grants and fellowships from the European Union. Recently, she received the best poster award for her recent work in the basic research technical review meeting of DTRA.

Hyeji KimPhD Candidate Stanford University Superposition Coding is Almost Always Optimal for the Poisson Broadcast ChannelThe two fundamental building blocks of wireless networks is the

multiple access channel (multiple transmitters and one receiver) and the broadcast channel (one transmitter and multiple receivers). While the capacity region for multiple access channel is known, the capacity region for broadcast channels has been an open problem for 40 years.

A continuous-time Poisson channel is a canonical model for optical communications that is widely used to transmit telephone signals, internet communication, and cable television signals. The 2-receiv-er continuous-time Poisson broadcast channel is a 2-receiver broad-cast channel for which the channel to each receiver is a continu-ous-time Poisson channel.

We show that superposition coding is optimal for this channel for almost all channel parameter values. Interestingly, the channel in some subset of these parameter values does not belong to any of the existing classes of broadcast channels for which superposition coding is known to be optimal. For the rest of the channel parame-ter values, we show that there is a gap between the best known in-ner bound and the best known outer bound – Marton’s inner bound and the UV outer bound.

Bio

Hyeji Kim is a PhD candidate in the Department of Electrical Engi-neering at Stanford University advised by Prof. Abbas El Gamal. She received the B.S. degree with honors in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2011 and the M.S. degree in Electrical Engineering from Stanford University in 2013. Her research interest include information theory, communication systems, and statistical learning. She is a recipient of the Stanford Graduate Fellowship.


Jung-Eun KimPhD Candidate University of Illinois at Urbana-Champaign A New Real-Time Scheduling Paradigm for Safety-Critical Multicore Systems

Over the past decade, multicore processors have become increasingly common for their potential of efficiency, which has made new single-core processors become rel-atively scarce. As a result, it has created a pressing need to transition to multicore processors. However, existing safety-critical software that has been certified on single-core processors is not allowed to be fielded on a multicore system as is. The issue stems from, namely, serious inter-core interference problems on shared resources in cur-rent multicore processors, which create non-deterministic timing behavior. Meeting the timing constraints is the crucial requirement of safety-critical real-time systems as timing violations could have disastrous effects, from loss of human life to damages to machines and/or the environment. This is why Federal Aviation Administration (FAA) does not currently allow the use of more than one core in a multicore chip. Academia has paid little attention to non-determin-ism due to uncoordinated I/O communications relatively compared to other resources such as cache or memory, although industry considers it as one of the most troublesome challenges. Hence we focuse on I/O synchronization while assuming unknown Worst Case Execution Time (WCET) that can get impacted by other interference sources. Traditionally, a two-level scheduling, such as Integrated Modular Avionics system (IMA), has been used for providing tem-poral isolation capability. However, such hierarchical approaches in-troduce significant priority inversions across applications, especially in multicore systems, ultimately leading to lower system utilization. To address these issues, we have proposed a novel scheduling mechanism called budgeted generalized rate monotonic analysis (Budgeted GRMS) in which different applications’ tasks are globally scheduled for avoiding unnecessary priority inversions, yet the CPU resource is still partitioned for temporal isolation among applica-tions. Incorporating the issues of unknown WCETs and I/O synchro-nization, this new scheduling paradigm enables the “safe” use of multicore processors in safety-critical real-time systems.

Bio

Jung-Eun Kim is a PhD candidate advised by Prof. Lui Sha in the De-partment of Computer Science at the University of Illinois at Urba-na-Champaign. She received her BS and MS (advised by Prof. Chang-Gun Lee) degrees from the department of Computer Science and Engineering of Seoul National University, Korea in 2007 and 2009, respectively. Her current research interests include real-time sched-uling (schedulability analysis, optimization, hierarchical scheduling) and real-time multicore architecture. The main targeted application is safety-critical hard real-time systems such as avionics systems (In-tegrated modular avionics (IMA) systems). She is a recipient of the Richard T. Cheng Endowed Fellowship for 2015-2016.

Varada KolhatkarPostdoc toral ResearcherPrivacy Analytics Inc. Resolving Shell Nouns

Shell nouns are abstract nouns, such as ‘fact’, ‘issue’, ‘idea’, and ‘problem’, which, among other functions, facilitate efficiency by avoiding repetition of long stretches of text. Shell nouns en-

capsulate propositional content, and the process of identifying this content is referred to as shell noun resolution.

My research presents the first computational work on resolving shell nouns. The research is guided by three primary questions: first, how an automated process can determine the interpretation of shell nouns; second, the extent to which knowledge derived from the linguistics literature can help in this process; and third, the ex-tent to which speakers of English are able to interpret shell nouns.

I start with a pilot study to annotate and resolve occurrences of ‘this issue’ in the Medline abstracts. The results illustrate the feasibility of annotating and resolving shell nouns, at least in this closed domain. Next, I move to developing general algorithms to resolve a variety of shell nouns in the newswire domain. The primary challenge was that each shell noun has its own idiosyncrasies and there was no annotated data available. I developed a number of computation-al methods for resolving shell nouns that do not rely on manually annotated data. For evaluation, I developed annotated corpora for shell nouns and their content using crowdsourcing. The annotation results showed that the annotators agreed to a large extent on the shell content. The evaluation of resolution methods showed that knowledge derived from the linguistics literature helps in the pro-cess of shell noun resolution, at least for shell nouns with strict se-mantic and syntactic expectations.

Bio

Varada Kolhatkar’s broad research area in the past eight years has been natural language processing and computational linguistics. She recently completed her PhD in computational linguistics from the university of Toronto. Her advisor was Dr. Graeme Hirst. Prior to that, she did her Master’s with Dr. Ted Pedersen at the Universi-ty of Minnesota Duluth. During her PhD she focused primarily on the problem of anaphora resolution. Her Master’s thesis explores all-words-sense disambiguation, showing the effect of polysemy, context window size, and sense frequency on disambiguation. At the end of her Ph.D., Varada spent four months at the University of Hamburg, Germany, where she worked with Dr. Heike Zinsmeister on non-nominal anaphora resolution. Currently, Varada is work-ing as a research analyst at a company called Privacy Analytics Inc, where she focuses on the problem of text de-identification, i.e., the process used to protect against inappropriate disclosure of person-al information in unstructured data.


Parisa KordjamshidiPostdoctoral Research Associate U. of Illinois, Urbana-Champaign Saul: Towards Declarative Learning Based Programming

Developing intelligent prob-lem-solving systems for real world applications requires ad-dressing a range of scientific

and engineering challenges. I will present Saul, a learning based programming language designed to address some of the short-comings of programming languages that aim at advancing and simplifying the development of intelligent systems. Such languag-es need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piece-wise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. I will describe the key constructs of Saul and exemplify its use in case studies of developing intelligent applications in the domains of natural language processing and computational biology. I will also argue that, apart from simplifying programming for complex models, one main advantage of such a language is the reusability of the designed inference, learning models and features, henceforth increasing the replicability of research results. Moreover, the mod-els can be extended to use new emerging algorithms, new data re-sources and background knowledge with a minimum effort.

Bio

Parisa Kordjamshidi is a postdoctoral researcher in University of Illinois at Urbana-Champaign, computer science department, in cognitive computation group. She obtained her PhD degree from KULeuven in July 2013. During her PhD research she introduced the first Semantic Evaluation task and benchmark for Spatial Role La-beling (SpRL). She has worked on structured output prediction and relational learning models to map natural language onto formal spatial representations, appropriate for spatial reasoning as well as to extract knowledge from biomedical text. She is also involved in an NIH (National Institute of Health) project, extending her research experience on structured and relational learning to Declarative Learning Based Programming (DeLBP) and performing biological data analysis. DeLBP is a research paradigm in which the goal is to facilitate programming for building systems that require a number of learning and reasoning components that interact with each oth-er. This would help experts in various domains who are not expert in machine learning, to design complex intelligent systems.The re-sults of her research have been published in several international peer-reviewed conferences and journals including ACM-TSLP, JWS, BMC-Bioinformatics, IJCAI.

Ramya Korlakai VinayakPhD CandidateCalifornia Institute of Technology Convex Optimization Based Graph Clustering: Theoretical Guarantees and Practical Applications

Today we are collecting huge amounts of data with the aim of ex-tracting useful and relevant information. Clustering, a widely used technique toward this quest, refers to the grouping of data points that are similar to each other. In many problems, the observed data has a network or graphical structure associated to it, as is the case in social networks, bioinformatics, data mining and other fields. When attempting to cluster massive data, making pairwise comparisons/measurements between all data points is exorbitantly expensive. A major challenge therefore, has been to identify clusters with only partially-observed graphs and to design algorithms with provable guarantees for this task.

In the case of unweighted graphs, we consider two algorithms based on the popular convex optimization approach of the “low-rank plus sparse” decomposition of the adjacency matrix (Robust Principal Component Analysis). We provide sharp performance guarantees for successfully identifying clusters generated by the commonly used Stochastic Block Model in terms of the size of the clusters, the density of edges inside the clusters and the regulariza-tion parameter of the convex programs.

For weighted graphs, where each weighted edge represents the similarity between its corresponding pair of points, we seek to re-cover a low-rank component of the adjacency matrix (also called the similarity matrix). We use a convex-optimization-based algo-rithm which requires no prior knowledge of the number of clusters and behaves in a robust way in the presence of outliers.

Using a generative stochastic model for the similarity matrix, we obtain sharp bounds on the sizes of clusters, strength of similarity compared to noise, number of outliers and the regularization pa-rameter.

We corroborate our theoretical findings with simulated experi-ments. We also apply our algorithms to the problem of crowdsourc-ing inference using real data.

Bio

Ramya Korlakai Vinayak is a PhD candidate in the Department of Electrical Engineering at Caltech. She works with Prof. Babak Has-sibi. Her research interests are broadly in the intersection of Opti-mization and Machine Learning. She received the Schlumberger Foundation Faculty of the Future fellowship for the academic years 2013-15. Prior to joining Caltech, Ramya obtained her undergrad-uate degree in Electrical Engineering from Indian Institute of Tech-nology Madras.


Karla KvaternikPostdoctoral Research Associate Princeton University Consensus Optimization Based Coordination Control Strategies

Consensus-decentralized optimi-zation (CDO) methods, originally studied by Tsitsiklis et al., have

undergone significant theoretical development within the last de-cade. Much of this attention is motivated by the recognized utility of CDO in large-scale machine learning and sensor network appli-cations.

In contrast, we are interested in a distinct class of decentralized coordination control problems (DCCPs) and we aim to investigate the utility and limitations of CDO-based coordination control strat-egies. Unlike prototypical machine learning and sensor network problems, DCCPs may involve a number of networked agents with heterogeneous dynamics that couple to those of a CDO-based co-ordination control strategy, thereby affecting its performance. We find that existing analytic techniques cannot easily accommodate such a problem setting. Moreover, the final desired agent config-uration in general DCCPs does not necessarily involve consensus. This nuanced observation requires a re-interpretation of the vari-ables updated in a standard CDO scheme, and exposes a limitation of CDO-based coordination control strategies.

Starting from this re-interpretation, we address this limitation by proposing the Reduced Consensus Optimization (RCO) method, which is a streamlined variant of CDO particularly well suited to the DCCP context. More importantly, we introduce a novel framework for the analysis of general CDO methods, which is based on the use of interconnected systems techniques, small-gain arguments and the concept of semiglobal, practical, asymptotic stability. This framework allows us to seamlessly study the performance of RCO, as well as problem settings involving dynamic agents. In addition, when applied to a general class of CDO methods themselves, this analytic viewpoint allows us to relax several standard assumptions.

Bio

Karla Kvaternik obtained her B.Sc. in Electrical and Computer En-gineering at the University of Manitoba, her M.Sc. specializing in control theory at the University of Alberta, and her Ph.D. in control theory at the University of Toronto. She was the recipient of the prestigious Vanier Canada Graduate Scholarship in 2010, and the recipient of the Best Student Paper award at the 2009 Multiconfer-ence on Systems and Control in St. Petersburg, Russia. Her research interests span nonlinear systems and control theory, Lyapunov methods, nonlinear programming and extremum-seeking control, but her main interest is the development and application of de-centralized coordination control strategies for dynamic multiagent systems. She is currently a Postdoctoral Research Associate at Princ-eton University, where her research focuses on the development of optimal social foraging models.

Min Kyung LeeResearch Scientist Carnegie Mellon University Designing Human-Centered Algorithmic Technologies

Algorithms are everywhere, acting as intelligent mediators between people and the world around them. Facebook algo-

rithms decide what people see on their news feeds; Uber algorithms assign customers to drivers; robots drive cars on our behalves. Al-gorithmic intelligence offers opportunities to transform the ways people live and work for the better. Yet their opacity can introduce bias into the worlds that people access through such technologies, inadvertently provide unfair choices, blur accountability, or make the technology seem incomprehensible or untrustworthy.

My research examines the social and decision-making implications of intelligent technologies and facilitates more human-centered design. I study how intelligent technologies change work practic-es, and devise design principles and interaction techniques that give people appropriate control over intelligent technologies. In the process, I create novel intelligent products that address critical problems in the areas of on-demand work and robotic service.

In the first line of my research, I studied Uber and Lyft ridesharing drivers to understand the impact of algorithms used to manage hu-man workers in on-demand work. The results suggested that work-ers do not always cooperate with algorithmic management because of the algorithms’ limited assumptions about worker behaviors and the opacity of algorithmic mechanisms. I further examined people’s perceptions of algorithmic decisions through an online experiment, and created design principles around how we can use transparency, anthropomorphization, and visualization to foster trust in algorith-mic decisions and help people make better use of them.

In the second line of my research, I studied three service robots de-ployed in the field over long periods of time: a receptionist robot, a telepresence robot for distributed teams, and an office delivery ro-bot that I helped build from scratch using human-centered design methods. The studies revealed individual and social factors that ro-bots can personalize in order to be more successfully adopted into a workplace.

Bio

Min Kyung Lee is a research scientist in human-computer interaction at the Center for Machine Learning and Health at Carnegie Mellon University. Her research examines the social and decision-making implications of intelligent systems and supports the development of more human-centered machine learning applications. Dr. Lee is a Siebel Scholar and has received several best paper awards, as well as an Allen Newell Award for Research Excellence. Her work has been featured in media outlets such as the New York Times, New Scientist, and CBS. She received a PhD in HCI in 2013 and an MDes in Interaction Design from Carnegie Mellon, and a BS summa cum laude in Industrial Design from KAIST.


Kun (Linda) LiPhD Candidate University of California at Berkeley III-V Compound Semiconductor Lasers for Optical Communication and ImagingMy research projects focus on III-V compound semiconductor lasers to generate and manipu-

late light, with both bottom-up and top-down approaches, for appli-cations in optical communications, biological imaging, ranging and sensing. As microprocessors become progressively faster, chip-scale data transport becomes progressively more challenging. Optical in-terconnects for inter- and intra-chip communications are required to reduce power consumption and increase bandwidth. Lightwave devices have traditionally relied on III-V compound semiconductors due to their capacity for efficient optical processes. Growing III-V ma-terials from the bottom up opens a pathway to integrating superior optoelectronic properties with the massive existing silicon-based infrastructure. Our approach of self-assembling III-V nanostructures on silicon in a novel growth mode has bypassed several roadblocks and achieved excellent single crystalline quality with GaAs and InP based materials. I have developed a methodology to evaluate optical properties of InP nanostructures, and demonstrated its superior sur-face quality, which are critical for optoelectronic devices. I also make another type of micro-scale semiconductor lasers from the top down, which is called vertical-cavity surface-emitting lasers (VCSELs). They are key optical sources in optical communications, with the advan-tages of lower power consumption, lower-cost packaging, and ease of fabrication and testing. Our group has demonstrated a revolution-ary single-layer, high-index contrast sub-wavelength grating (HCG), and implemented it as a reflection mirror in VCSEL. Compared with conventional VCSEL mirrors (DBRs), the seemingly simple-structured HCG provides ultra-broadband high reflectivity, compact size and light weight, high-tolerant and cost-effective fabrication process. I mainly work on the development of wavelength-tunable 850nm and 1060nm HCG-VCSELs. These monolithic, continuously tunable HCG-VCSELs will present extraordinary performance in applications such as wavelength-division-multiplexed (WDM) optical network, light detection and ranging. Its potential wide reflection band and fast tuning speed will also be highly promising for high-resolution, real-time imaging in optical coherent tomography (OCT).

Bio

Kun (Linda) Li is a PhD candidate in the Department of Electrical En-gineering and Computer Sciences at University of California Berke-ley, advised by Prof. Connie Chang-Hasnain. Prior to joining gradu-ate school, she received her B.S. degree from Optical Engineering of Zhejiang University in China (2006-2010). She had one year of exchange experience in University of Hong Kong (2008-2009). Kun’s main research interests focus on III-V nanostructures directly grown on silicon for integrated optoelectronics, and vertical-cavity surface emitting laser (VCSEL) with high-contrast grating (HCG) structure for optical communication and imaging. Her skills include optical char-acterization, semiconductor fabrication, and optoelectronic device modeling. She received Lam Research Graduate Fellowship (2014) to award her performance in the field of semiconductors. Besides research, Kun is also active in a variety of education, outreach, and mentoring programs, including Girl Scouts, Expanding Your Horizon, and Girls in Engineering. Kun has won the Outstanding Graduate Student Instructor Award at UC Berkeley (2014).

Hongjin LiangLimited-Term Associate Researcher University of Science and Technology of China A Program Logic for Concurrent Objects under Fair Scheduling

Existing work on verifying con-current objects is mostly concerned with safety only, e.g., partial correctness or linearizability. Although there has been recent work verifying lock-freedom of non-blocking objects, much less efforts are focused on deadlock-freedom and starvation-freedom, prog-ress properties of blocking objects. These properties are more chal-lenging to verify than lock-freedom because they allow the prog-ress of one thread to depend on the progress of another, assuming fair scheduling.

We propose LiLi, a new rely-guarantee style program logic for verifying linearizability and progress together for concurrent ob-jects under fair scheduling. The rely-guarantee style logic unifies thread-modular reasoning about both starvation-freedom and deadlock-freedom in one framework. It also establishes prog-ress-aware abstraction for concurrent objects, which can be applied when verifying safety and liveness of client code. We have success-fully applied the logic to verify starvation-freedom or deadlock-free-dom of representative algorithms such as ticket locks, queue locks, lock-coupling lists, optimistic lists and lazy lists.

This is joint work with Xinyu Feng at USTC.

Bio

Hongjin Liang is a limited-term associate researcher at University of Science and Technology of China (USTC). She received her Ph.D. in Computer Science from USTC in 2014, under the joint supervision of Prof. Xinyu Feng (USTC) and Prof. Zhong Shao (Yale).

Hongjin is interested in program verification and concurrency the-ory. Her Ph.D. thesis is about refinement verification of concurrent programs and its applications, in which she designed simulations and Hoare-style program logics for concurrent program refinement, and applied them to verify concurrent garbage collectors and prove linearizability of concurrent objects and algorithms. She is currently trying to extend her refinement verification techniques to also rea-son about liveness properties of concurrent algorithms.

For more information, please visit http://staff.ustc.edu.cn/~lhj1018.


Xi LingPostdoctoral Scholar Massachusetts Institute of Technology Seeding Promoter Assisted Chemical Vapor Deposition of MoS2 MonolayerThe synthesis of monolayer MoS2-based dichalcogenides is

an attractive topic because of their promising properties in diverse fields, especially in electronics and optoelectronic. Among the vari-ous methods to get the monolayer MoS2, the chemical vapor depo-sition (CVD) method is considered as the superlative one because of the high efficient, low cost and large-area synthesis. So far, sul-fur and MoO3 are the widely used precursors to grow monolayer MoS2 on the SiO2/Si substrate. Here, by loading the organic aromat-ic molecule on the SiO2/Si substrate as seed, it was found that the large-area and high quality MoS2 can grow out under a much soft condition, such as atmospheric pressure, lowing the temperature from 800°C or higher to 650°C. Raman spectra, photoluminescence spectra and AFM (atomic force microscopy) are used to identify the thickness and quality of MoS2. Furthermore, other kinds of aromat-ic molecules are tried to use as a seed to grow MoS2. Towards the applications in integrated circuits, we developed a method called “selective sowing” of seeds to construct the basic building blocks of metal-semiconductor (e.g. graphene-MoS2), semiconductor-semi-conductor (e.g. WS2-MoS2) and insulator-semiconductor (e.g. hBN-MoS2) heterostructures, through direct and controllable CVD syn-thesis in a large-scale.

Bio

Xi Ling is currently a Postdoctoral Associate in the Research Labo-ratory of Electronics at Massachusetts Institute of Technology (MIT) since September 2012, under the supervision of Professors Mildred Dresselhaus and Jing Kong. She obtained her PhD degree in physi-cal chemistry from Peking University in July 2012, under the super-vision of Professor Jin Zhang and Zhongfan Liu. She has a multi-disciplinary background in chemistry, materials science, electrical engineering and physics, with research experience on spectrosco-py, chemical vapor deposition (CVD) and optoelectronic devices.

Fei LiuPostdoctoral FellowCarnegie Mellon University Summarizing Information in Big Data: Algorithms and Applications

Information floods the lives of modern people, and we find it overwhelming. Summarization

systems that identify salient pieces of information and present it concisely can help. I will discuss both algorithmic and application perspectives of summarization. Algorithm-wise, I will describe key-word extraction, sentence extraction, and summary generation, including a range of techniques from information extraction to semantic representation of data sources; application-wise, I focus on summarizing human conversations, social media contents, and news articles.

The data sources span low-quality speech recognizer outputs and social media chats to high-quality content produced by profession-al writers. A special focus of my work is exploring multiple informa-tion sources. In addition to better integration across sources, this al-lows abstraction to shared research challenges for broader impact. Finally, I try to identify the missing links in cross-genre summariza-tion studies and discuss future research directions.

Bio

Fei worked as a Senior Research Scientist at Bosch Research, Palo Alto, California, one of the largest German companies providing intelligent car systems and home appliances. Fei received her PhD in Computer Science from the University of Texas at Dallas in 2011, supported by Erik Jonsson Distinguished Research Fellowship.

Prior to that, she obtained her Bachelors and Masters degrees in Computer Science from Fudan University, Shanghai, China. Feihas published over twenty peer reviewed articles, and she serves as a referee for leading journals and conferences.


Yu-Hsin LiuPhD CandidateUniversity of California at San Diego Silicon p-n Junction Photodetectors

Yu-Hsin’s research focuses on sil-icon p-n junction structures ap-plied to photodetectors, which

are compatible with COMS fabrication process and without involv-ing defects. By using space confinement and heavy doping in na-noscaled p-n junction structures to relax the k-selection rule for Si materials, efficient 1310 nm light detection has been demonstrat-ed. The nanowire and waveguide devices show efficient sub-band-gap bias-dependent photoresponse without involving any defects or surface states.

On the other hand, she also demonstrated high gain in heavily doped and partially compensated p-n junction devices at visible wavelength. Compared to avalanche photodiodes based on impact ionization, her photodetectors using the Cycling Excitation Process (CEP) for signal amplification, experience smaller excess noise and can be operated at very low bias (<4V). CEP also possesses an in-trinsic, phonon-mediated regulation process to keep the device stable without the quenching components required in today’s Gei-ger-mode avalanche detectors.

Bio

Yu-Hsin Liu is now a Ph.D. candidate in Materials Science and Engi-neering program in UCSD. She received her Master degree in Ma-terials Science and Engineering from National Tsing Hua University (NTHU)at Taiwan in 2009. She had worked as research assistant in NTHU for one year and became a Ph.D. student of UCSD in 2010. In the department of Electrical and Computer Engineering, she has been working on optoelectronics and semiconductor devices and has an extensive background in fabrication process development, device characterizations, simulations and modelings. She also has experiences in microfabrication and microfluidics devices devel-opment from internship working with Illumina and Nano3 Facility (Nanoscience, Nanoengineering, and Nanomedicine). Currently her research interests are in cycling excitation process, a new signal am-plification process for Si photodetectors.

Kristen LuriePhD Candidate Stanford University New Optical Imaging Tools and Visualization Techniques for Bladder CancerBladder cancer is the most costly cancer to treat as the high rate of

recurrence necessitates lifelong surveillance in order to detect can-cer as early as possible. White light cystoscopy (WLC) is the standard tool used for these surveillance procedures, but this imaging tech-nique has several limitations. First, WLC cannot accurately detect all tumors, causing some — particularly early stage tumors — to go untreated. Second, WLC cannot gauge the penetration depth of le-sions, the criterion for cancer staging, which requires an excisional biopsy. This follow-up procedure is costly and risky and may ulti-mately be unnecessary if the tumor is incorrectly classified. Third, it is difficult to review the image data, making it easy to overlook signs of cancer due to work flow challenges or insufficient annotations.

To overcome these limitations, I developed targeted techniques to improve the cystoscopy examination. Specifically, I augmented WLC with optical coherence tomography (OCT), a complemen-tary imaging technique whose ability to visualize the subsurface appearance of the bladder wall reveals early stage tumors better than WLC alone and makes it possible to stage cancers. To this end, I developed a miniaturized, rapid-scanning OCT endoscope that facilitates tumor detection and classification during the initial cys-toscopy. Finally, to improve the review of the cystoscopy data, I de-veloped techniques that can enable a more comprehensive review among and between WLC and OCT imaging data. These techniques include (1) a volumetric mosaicing algorithm to extend the field of view of OCT, (2) 3D reconstruction technique to generate models with the shape and appearance of the bladder, and (3) a registration approach that registers OCT data to the 3D bladder model. Taken together, the new OCT endoscope and image reconstruction algo-rithms I describe can have a tremendous impact on the future of cystoscopy for improved management of bladder cancer patients.

Bio

Kristen is a PhD student in Electrical Engineering at Stanford Uni-versity and is advised by Dr. Audrey Ellerbee. Kristen was awarded a Stanford Graduate Fellowship, an NSF Graduation Research Fel-lowship, and a National Defense Science and Engineering Graduate (NDSEG) Fellowships to pursue her doctoral studies. She received her A.B. and B.E. degrees from Dartmouth College in Engineering Sciences and a MS in Electrical Engineering also from Stanford Uni-versity. Her research interests lie at the intersection of optics and computer vision for medical applications. Her dissertation is pri-marily focused on developing endoscopes and 3D computer vision algorithms for applications in urology.


Jelena MarasevicPhD Candidate Columbia University Links between Systems and Theory: Full-Duplex Wireless and BeyondMy research focuses on the op-timization of wireless network performance by using analytical

tools from optimization and algorithms research, and relying on the problem structure. From the theory perspective, the goal is to un-derstand and describe the studied problems with realistic but trac-table mathematical models, and ultimately devise algorithms with provable performance guarantees. From the systems perspective, the goal is to implement the devised algorithms and demonstrate their performance experimentally.

For example, in a cross-disciplinary project on full-duplex wireless communication — simultaneous transmission and reception on the same frequency channel — we have been exploring the inter-actions between the hardware design and the algorithms for me-dium access control (MAC). Our work has resulted in a number of insightful analytical results that characterize and quantify achiev-able throughput gains from full-duplex, based on realistic models of the full-duplex hardware. Moreover, we have obtained power allocation algorithms that are applicable to quite general hardware models and to both single-channel and multi-channel settings. Our algorithms maximize the sum of the rates over a full-duplex pair of users and over (possibly multiple) frequency channels, and are provably near-optimal. The algorithms provide output that agrees well on the modeled and the measured full-duplex hardware pro-file.

My work in the area of full-duplex currently involves design of adaptive algorithms for self-interference cancellation in full-duplex circuits, design and analysis of scheduling algorithms with fairness guarantees, and a testbed development.

Apart from full-duplex, I am also working on the design and analysis of fast iterative optimization methods for large-scale problems with fairness objectives.

Bio

Jelena Marasevic is a PhD student at Columbia University. Her re-search focuses on algorithms for fair and efficient resource alloca-tion problems, with applications in wireless networks. She received her BSc degree from University of Belgrade, School of Electrical Engineering, in 2011, and her MS degree in electrical engineering from Columbia University in 2012. For her MS degree, she received the M.S. Award of Excellence.

In Spring 2012, Jelena organized the first cellular networking hands-on lab for a graduate class in wireless and mobile network-ing. For this work, she received the Best Educational Paper Award at the 2nd GENI Research and Educational Experimentation workshop (GREE2013), and was also awarded the Jacob Millman Prize for Ex-cellence in Teaching Assistance from Columbia University.

Earlier this year, Jelena was in a two-student team that won the Qualcomm Innovation Fellowship for a cross-disciplinary project on full-duplex wireless.

Ghita MezzourAssistant ProfessorInternational University of Rabat A Socio-Technical Approach to Cyber-Security

Cyber security has both techni-cal and social dimensions. Un-derstanding and leveraging the interplay of these dimensions

can help design more secure systems and more effective policies. However, the majority of cyber security research has only focused on the technical dimension. In my work, I study cyber-security using a socio-technical approach that combines data science techniques, computational models, and network science techniques.

I will start by presenting my work on empirically identifying factors behind international variation in cyber attack exposure and host-ing. I use data from 10 million computers worldwide provided by a key anti-virus vendor. The results of this work indicate that reducing attack exposure and hosting in the most affected countries requires addressing both social and technical issues such as corruption and computer piracy. Then, I will present a computational methodology to assess cyber warfare capabilities of all the countries in the world. The methodology captures political factors that motivate countries to develop these capabilities and technical factors that enable such development. Together, these projects show that bridging the so-cial and technical dimensions of cyber security can improve our un-derstanding of the dynamics of cyber security and have a real-world impact.

Bio

Ghita Mezzour received her PhD degree from Carnegie Mellon University (CMU) in May 2015. She was part of both the School of Computer Science and the Electrical and Computer Engineering Department at CMU. Ghita is currently an Assistant Professor at the International University of Rabat in Morocco. Her research interests are at the intersection of cyber security, big data, and socio-tech-nical systems. Ghita holds a Master and a Bachelor in Communica-tion Systems from the Ecole Polytechnique Federale de Lausanne in Switzerland.


Jamie MorgensternWarren Postdoctoral Fellow University of Pennsylvania Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)

Vaishnavi Nattar RanganathanPhD CandidateUniversity of Washington Limb Reanimation with Fully Wireless Brain Computer Interface

We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an as-ymptotic dominant strategy for every student to report their true preferences to the mechanism. Our main tool in this endeavor is differential privacy: we give an algorithm that coordinates a sta-ble matching using differentially private signals, which lead to our truthfulness guarantee. This is the first setting in which it is known how to achieve nontrivial truthfulness guarantees for students when computing school optimal matchings, assuming worst- case preferences (for schools and students) in large markets.

Bio

Jamie Morgenstern is a Warren Fellow at the University of Penn-sylvania. She received her PhD in Computer Science at Carnegie Mellon University, advised by Avrim Blum. Her research interests include mechanism design, learning theory, and applications of differential privacy to questions in economics. During her PhD, she received a Simons Award for Graduate Students in Theoretical Com-puter Science, an NSF Graduate Research Fellowship, and a Micro-soft Graduate Women’s scholarship.

The primary aim of my research is to develop a completely wire-

less and implantable brain-computer-spinal interface (BCSI) to re-animate patients with paralysis caused by injury in the spinal cord. Advancement in technology has led to state-of-the-art solutions for neural signal acquisition and stimulation for limb reanimation. The major challenge lies in combining them for autonomous stimula-tion. Another concern associated with long term implantation of these devices is the power and communication cables that exit the skin surface and pose a risk of infection.

To address these two issues, my contributions are to implement an algorithm to enable stimulation based on recorded signal and designing the analog circuits for simultaneous wireless communi-cation and power transfer to increase the implanted operational lifetime.

I have implemented a fully wireless printed circuit design of the BCSI system as a part of a NSF funded program. Using low-power wire-less communication protocols, like radio frequency (RF) backscat-ter at 915MHz, high data-rate communication can be established between the implant and external devices. I have also designed and implemented a digital controller for this protocol using 65nm CMOS technology.

With respect to wireless power transfer, we have successfully demonstrated inductive power delivery across tissue using opti-mally designed coupled resonators operating at 13.56MHz. This system is capable of achieving efficiencies greater than 70% with low temperature rise (less than 2°C).

The current focus is to utilize inherently low power on-chip digital computation to minimize dependence on external control, thereby closing the loop in the BCSI system. My goal is to contribute with my experience to the advancement of engineering in medicine by developing affordable miniaturized biomedical implants that can facilitate diagnosis for treatment and improve the quality of life for individuals with disabilities.

Bio

Vaishnavi Ranganathan is a PhD student in the Sensor Systems lab-oratory at the University of Washington (UW), Seattle, WA. Her main research interests are wireless power transfer and brain-computer interface applications, including low-power computation and com-munication solutions for implantable devices. She is a member of the Center for Sensorimotor Neural Engineering which is an NSF funded research center at the UW.

She received the BTech degree in Electronics and Instrumentation Engineering from Amrita Vishwavidyapeetham University, TN, In-dia, in 2011, and a M.S degree in Electrical Engineering, specializing in NEMS, from Case Western Reserve University, Cleveland, OH, in 2013. As an undergraduate she gained experience in robotics and sensor design. She has also worked as a research intern in the Nano-bios lab at Indian Institute of Technology, Mumbai, India, where her focus was MEMS for biomedical sensors.


Xiang NiPhD Candidate University of Illinois at Urbana-Champaign Mitigation of Failures in High Performance Computing via Runtime Techniques

Dessislava NikolovaPostdoctoral Research Scientist Columbia University Silicon Photonics for Exascale Systems

Parallel computing is a powerful tool to solve large complex prob-

lems in a timely manner. The most powerful supercomputer in the US today, named Titan, consists of 300,000 cores along with over 18,000 general purpose GPUs. At its peak, Titan can perform over 17 quadrillion floating-point operations per second! While the num-ber of components assembled to create a supercomputer keeps increasing beyond these values, the reliability and the capacity of each individual component has not increased proportionally. As a result, the machines of today fail frequently and hamper smooth execution of high performance applications. The slow increase in memory capabilities has thwarted efficient use of the state-of-the-art methods for containing such failures. My research strives to de-velop runtime system techniques that can be deployed to make large scale parallel executions robust and fail-safe. In particular, I have worked on answering the following questions: how can a run-time system provide fault tolerance support efficiently with mini-mal application intervention? What are the effective ways to detect and correct silent data corruptions? Given the limited memory re-source, how do we enable the execution and checkpointing of data intensive applications?

Bio

Xiang Ni is a final-year PhD candidate in the Department of Com-puter Science at the University of Illinois at Urbana-Champaign. She is interested in developing runtime system techniques for scalable parallel executions on supercomputers. Performance optimization of irregular applications and application oblivious fault tolerance are her primary areas of research. At Illinois, she is part of the Par-allel Programming Laboratory which develops Charm++ and its applications. She has closely worked with researchers from Disney Research on developing a first of its kind parallel software for Cloth Simulation. She has also done two summer internships at the Law-rence Livermore National Laboratory. Xiang got her master’s degree at Illinois in 2012 for her work on asynchronous protocols for low overhead checkpointing. Prior to that she got a bachelor’s degree in Computer Science at Beihang University in Beijing, China.

Driven by the increasing use and adoption of cloud computing and big data applications, data centers and high-performance

computers are envisioned to reach Exascale dimensions. This will require highly scalable and energy efficient means for data ex-change. Optical data movement is one of the promising means to address the bandwidth demands of these systems. Optical links are being increasingly deployed in data centers and high-performance computers but to facilitate the increased bandwidth demand and provide flexibility and high link utilization, wavelength routing and optical switching is necessary. Silicon photonics is a particularly promising and applicable technology to realize various optical components due to its small footprint, high bandwidth density, and the potential for nanosecond scale dynamic connectivity.

My research focusses on the design, modelling and demonstration of novel silicon nanophotonic devices and systems for energy ef-ficient optical data movement. In particular I aim to provide solu-tions for highly scalable silicon photonic switch fabrics. Within this work several architectures for spatial and wavelength switches with silicon photonic microrings are proposed and demonstrated. Ana-lytical modeling and simulations show that the proposed architec-tures are highly scalable. By developing simple but accurate device models critical for the switching performance device parameters are identified and their optimal values are derived. Experimental demonstrations confirm the feasibility of these silicon photonic switches. The proposed devices can form the building blocks of the future flexible, low-cost and energy efficient optical networks that can deliver large volumes of data with time-of-flight latencies.

Bio

Dessislava Nikolova is a postdoctoral scientist at Columbia Univer-sity where her current research is on photonic systems for optical interconnects, networks and quantum communications. Prior to that Dessislava was awarded the prestigious European wide Marie Curie Fellowship to study magneto-plasmonics at the University College London. She received her PhD in Computer Science from Antwerp University with research in the area of optical networks. Her work was recognized by a Best Paper award at the SPECTS sym-posium and led to a patent. She has also worked for Alcatel-Lucent, researching passive optical networks. Her future research goal is to design accessible, easily controllable, multifunctional photonic sys-tems thereby opening the field of active nanophotonics to comput-er scientists and network engineers.


Farnaz NirouiPhD CandidateMassachusetts Institute of Technology Nanoscale Engineering with Molecular Building Blocks

Idoia OchoaPhDStanford University Genomic Data Compression and Processing

Mechanical properties of materi-als at the nanoscale can lead to unique physical phenomena and

devices with improved performance and novel functionalities. My research utilizes the mechanical behavior and structural deforma-tions of few-nanometer-thin molecular films to achieve precise na-noscale force control. This combined with deformation-dependent changes in the electrical and optical properties of matter creates a platform enabling development of novel device concepts. Based on these principles, I have developed electromechanically tunable nanogaps composed of self-assembled compressive organic films sandwiched between conductive contacts. An applied voltage across these electrodes provides an electrostatic force that causes mechanical compression of the molecular layer to modulate the gap size. Through modifying the molecular film by chemical syn-thesis and thin-film engineering, the nanogap dimensions and the extent of compression can be precisely controlled. The compressed molecules also provide the elastic force necessary to control the surface adhesive forces to avoid permanent adhesion of the elec-trodes (defined as stiction) as the gap is mechanically tuned. Utiliz-ing these nanogaps, I have designed nanoelectromechanical (NEM) switches that operate through a tunneling switching mechanism. In this scheme, the electrostatic compression of the molecules leads to a decrease in the tunneling gap and an exponential increase in the tunneling current. With sub-5 nm switching gaps and nanoscale force control, these low-voltage and stiction-free devices overcome the challenges faced by the current contact-based NEM switches giving rise to promising applications in low-power electronics. Be-yond electromechanical switches, these mechanically active nan-ogaps exhibit applications as molecular metrological tools for prob-ing nanoscale mechanical and electrical properties, and can enable dynamically tunable optical and plasmonic systems.

Bio

Farnaz Niroui is currently a PhD candidate in the Department of Electrical Engineering and Computer Science at Massachusetts In-stitute of Technology. She is a recipient of the Natural Sciences and Engineering Research Council of Canada Scholarship for graduate studies. Farnaz received her Master of Science degree in Electrical Engineering from MIT in 2013, working with Professors Vladimir Bu-lovic and Jeffrey Lang. She completed her undergraduate studies in Nanotechnology Engineering at University of Waterloo in Canada. Her research interest is at the interface of device physics, materials science and nanofabrication to enable study, manipulation and en-gineering of systems with unique functionalities at the nanoscale.

One of the highest priorities of modern healthcare is to identify genomic changes that predis-pose individuals to debilitating

diseases or make them more responsive to certain therapies. This effort is made possibly due to the generation of a massive DNA se-quencing data that must be stored, transmitted and analyzed. Due to the large size of the data, storage and transmission represent a huge burden, as the current solutions are costly and space/time de-manding. Finally, due to imperfections on the data and the lack of theoretical models that describe it, the analysis tools do not gener-ally have theoretical guarantees, and different approaches exist for the same task.

Thus it is important to develop new tools and algorithms to facil-itate the transmission and storage of the data and to improve the inference performed on it. This is exactly the focus of my research. Part of it consists on developing compression schemes, which range from compression of single genomes to compression of the raw data outputted by the sequencing machines. Part of this data (the reliability of the outputted nucleotides) is normally lossy com-pressed, as it is inherently noisy and therefore difficult to compress. Moreover, it has been shown that lossy compression can potential-ly reduce the storage requirements while improving the inference performed on the data. Further understanding this effect is part of my ongoing research, together with characterizing the statistics of the noise, such that denoisers tailored to them can be designed. I have also worked on developing compression schemes for da-tabases such that similarity queries can still be performed on the compressed domain. This is of special interest in large biological databases, where retrieving genomic sequences similar to others is necessary in several applications. Finally, I designed a tool for iden-tifying disease driver genes associated with molecular processes in cancer patients.

Bio

Idoia is currently in her 5th year of PhD in the Electrical Engineering department at Stanford University, working with Prof. Tsachy Weiss-man. She also received her MSc from the same department in 2012. Previous to Stanford, she got a BS and MSc from the Telecommuni-cations Engineering (Electrical Engineering) department at Univer-sity of Navarra, Spain. During her time at Stanford, she conducted internships at Google and Genapsys, and she served as a technical consultant for the HBO TV show “Silicon Valley.”

Her main interests are in the field of compression, bioinformatics, information theory, coding and signal processing. Her research fo-cuses mainly on helping the bio community to handle the massive amounts of genomic data that are being generated, for example by designing new and more effective compression programs for genomic data. Part of her effort also goes into understanding the statistics of the noise presented in the data under consideration, so that denoisers tailored to them can be generated, thus improving the subsequent analysis performed on the data.

Her research was/is founded by a La Caixa fellowship, a Basque Gov-ernment fellowship and a Stanford Graduate fellowship.


Eleanor O’RourkePhD Candidate University of Washington Educational Systems for Maximizing Learning Online and in the Classroom

Amanda ProrokPostdoctoral Researcher University of Pennsylvania Heterogeneous Robot Swarms

The goal of my research is to create computing systems that maximize student learning both

online and in the classroom. Specifically, my dissertation explores the design, implementation, and evaluation of novel educational systems that increase motivation, provide personalized learning ex-periences, and support formative assessment. As part of this work, I have created an incentive structure that promotes the growth mindset in an educational game, developed a framework for au-tomatically generating instructional scaffolding, and evaluated a system that visualizes student data in real-time to assist classroom teachers. In the development of these systems, I combine ideas from computer science, psychology, education, and the learning sciences to develop novel technical methods of integrating learn-ing theory into computational tools. In addition to evaluating my work through classroom studies with students and teachers, I have also conducted large-scale online experiments with tens of thou-sands of students. My findings provide new insights into how stu-dents learn and how computing systems can support the learning process. The ultimate goal of my research is to build personalized data-driven systems that transform how we teach, assess, commu-nicate, and collaborate in learning environments.

Bio

Nell is a PhD candidate in Computer Science and Engineering at the University of Washington, advised by Zoran Popović in the Cen-ter for Game Science. She received a B.A. in Computer Science and Spanish at Colby College in 2007, and an M.S. in Computer Science from the University of Washington in 2012. Her research lies at the intersection of human-computer interaction and educational tech-nology with a focus on creating novel learning systems to support motivation, personalization, and formative assessment. Nell has won several awards and scholarships, including the Google Anita Borg Scholarship and the Microsoft Research Graduate Women’s Scholarship.

As we harness swarms of autono-mous robots to solve increasingly challenging tasks, we must find ways of distributing robot capa-

bilities among distinct swarm members. My premise is that that one robot type is not able to cater to all aspects of a given task, due to constraints at the single-platform level. Yet, it is an open question how to engineer heterogeneous robot swarms, since we lack the foundational theories to help us make the right design choices and understand the implications of heterogeneity.

My approach to designing swarm robotic systems considers both top-down methodologies (macroscopic modeling) as well as bot-tom-up (single-robot level) algorithmic design. My first research thrust targeted the specific problem of indoor localization for large robot teams, and employed a fusion of ultra-wideband and infrared signals to produce high accuracy. I developed the first ultra-wide-band time-difference-of-arrival sensor model for mobile robot localization, which, when used collaboratively, achieved centime-ter-level accuracy. Experiments with ten robots illustrated the effect of distributing the sensing capabilities heterogeneously through-out the team. This bottom-up approach highlighted the compro-mise between homogenous teams that are very efficient, yet ex-pensive, and heterogeneous teams that are low-cost.

My second research thrust, which aims at formally understanding this compromise, targets the general problem of distributing a heterogeneous swarm of robots among a set of tasks. My strategy is to model the swarm macroscopically, and subsequently extract decentralized control algorithms that are optimal given the hetero-geneous swarm composition and underlying task requirements. I developed a dedicated diversity metric that identifies the relation-ship between performance and heterogeneity, and that provides a means with which to control the composition of the swarm so that performance is maximized. This top-down approach comple-ments the bottom-up method by providing high-level abstraction and foundational analyses, thus shaping a new way of exploiting heterogeneity as a design paradigm.

Bio

Amanda Prorok is a Postdoc in the General Robotics, Automation, Sensing and Perception (GRASP) Lab at the University of Pennsyl-vania, where she works with Prof. Vijay Kumar on multi-robot sys-tems. Prior to moving to UPenn, she spent one year working on cutting-edge sensor technologies at Sensirion, during which period her team launched the world’s first multi-pixel miniature gas sen-sor onto the market. She completed her PhD at EPFL, Switzerland, where she addressed the topic of indoor localization for large-scale, cooperative systems. Her dissertation was awarded the Asea Brown Boveri (ABB) award for the best thesis at EPFL in the fields of Com-puter Sciences, Automatics and Telecommunications. Before start-ing her doctorate, she spent two years in Japan working for Mitsub-ishi in the robotics industry, as well as for the Swiss government in a diplomatic role, on a full scholarship that was awarded to her by the Swiss-Japanese Chamber of Commerce.


Elina RobevaPhD CandidateUniversity of California at Berkeley Super-Resolution without Separation

Deblina SarkarPostdoctoral Associate , MIT2D Steep Transistor Technology: Overcoming Fundamental Barriers in Low-Power Electronics and Ultra-Sensitive Biosensors

This is joint work with Benjamin Recht and Geoffrey Schiebinger at UC Berkeley.

We provide a theoretical analysis of diffraction-limited super-res-olution, demonstrating that arbitrarily close point sources can be resolved in ideal situations. Given a lo-resolution blurred signal of M point sources of light, super-resolution imaging aims to recover the correct locations of the point sources and the intensity of light at each of them. Caused by the imaging device (telescope, micro-scope, camera, or others), every point source of light is blurred by a given point spread function. We assume that the incoming signal is a linear combination of M shifted copies (centered at each of the M point sources) of a known point spread function with unknown shifts (the locations of the point sources) and intensities, and one only observes a finite collection of evaluations of this signal.

To recover the locations and intensities, practitioners solve a convex program, which is a weighted version of basis pursuit over a con-tinuous dictionary. Despite the recent success in many empirical disciplines, the theory of super-resolution imaging remains limited. More precisely, our aim is to show that the true point source loca-tions and intensities are the unique optimal solution to the above mentioned convex program. Much of the existing proofs to date rely heavily on the assumption that the point sources are separated by more than some minimum amount. Building on polynomial inter-polation techniques and tools from compressed sensing, we show that under some reasonable conditions on the point spread func-tion, arbitrarily close point sources can be resolved by the above convex program from 2M+1 observations. Moreover, we show that the Gaussian point spread function satisfies these conditions.

Bio

Elina Robeva is a fourth-year graduate student in mathematics at UC Berkeley advised by Bernd Sturmfels. Originally from Bulgaria, Elina’s career as a mathematician started in middle school when she took part in many competitions in mathematics and computer sci-ence. After winning two silver medals from the international mathe-matical olympiad in high-school, she started her undergraduate de-gree at Stanford University in 2007. There she pursued her interests in mathematics and wrote two combinatorics papers with Professor Sam Payne. She received the Dean’s award, the Sterling award, the undergraduate research award, and an honorable mention for the Morgan prize. Elina completed software engineering internships at Facebook and Google and decided to pursue a PhD where she could apply her mathematical skills to problems in computer sci-ence and other applied disciplines. She commenced her PhD at Harvard University in 2011 and transferred to UC Berkeley in 2012 to work with Professor Bernd Sturmfels. Her papers are focused on the interplay between algebraic geometry statistics and optimiza-tion. They include work on mixture models and the EM algorithm, orthogonal tensor decomposition, factorizations through the cone of positive semidefinite matrices, and super-resolution imaging.

Aggressive technology scaling has resulted in exponential increase in power dissipation levels due to

the degradation of device electrostatics as well as the fundamental thermionic limitation in subthreshold swing of conventional Field-Ef-fect Transistors (FETs). My research, explores novel two-dimensional (2D) materials for obtaining improved electrostatic control and Tun-neling-Field-Effect-Transistors (TFETs), employing a fundamentally different carrier transport mechanism in the form band-to-band tunneling (BTBT) for overcoming the fundamental limitations of conventional FETs. This tailoring of both material and device tech-nology can lead to transistors with super steep turn-on character-istics, which is crucial for obtaining high energy-efficiency and ul-tra-scalability. My research, also establishes, for the first time, that the material and device technology which have evolved, mainly with an aim of power reduction in digital electronics, can revolutionize a completely diverse arena of bio/gas-sensor technology. The unique advantages of 2D semiconductors for electrical sensors is demon-strated and it is shown that they lead to ultra-high sensitivity, and also provide an attractive pathway for single molecular detectability- the holy grail for all biosensing research. Moreover, it is theoretically illustrated that steep turn-on, obtained through novel technology such as BTBT, can result in unprecedented performance improve-ment compared to that of conventional electrical biosensors, with around 4 orders of magnitude higher sensitivity and 10x lower de-tection time. With the aim towards building ultra-scaled low power electronics as well as highly efficient sensors, my research achieves a significant milestone, furnishing the first experimental demonstra-tion of TFETs based on 2D channel material to beat the fundamental limitation in subthreshold swing (SS). This device comprising of an atomically thin channel exhibits record average SS at ultra-low sup-ply voltages, thus, cracking the long-standing issue of simultaneous dimensional and power supply scalability and hence, can lead to a paradigm shift in information technology as well as healthcare.

Bio

Deblina Sarkar completed her M.S. and PhD in the ECE department at UCSB in 2010 and 2015, respectively. Her doctoral research, which com-bined the interdisciplinary fields of engineering, physics and biology, included theoretical modeling and experimental demonstration of en-ergy-efficient electronic devices and ultra-sensitive biosensors. She is currently a postdoctoral researcher in the Synthetic Neurobiology group at MIT and is interested in exploring novel technologies for mapping and controlling the brain activity. Ms. Sarkar is the lead author of numerous publications including several eminent journals such as Nature, Nano Lett., ACS Nano, TED as well as prestigious conferences such as IEDM, DRC and has authored/coauthored more than 30 papers till date. Sev-eral of her works have appeared in popular press and her research on novel biosensors, has been highlighted by Nature Nanotechnology. She is the recipient of numerous awards and recognitions, including being awarded Presidential Fellowship and Outstanding Doctoral Candidate Fellowship for pursuing doctoral research (2008), one of three research-ers worldwide to receive the prestigious IEEE EDS PhD Fellowship Award (2011), one of the 4 young researchers from USA honored as “Bright Mind” and invited to speak at the KAUST-NSF Conference (2015), and one of three winners of the Falling Walls Lab Young Innovator’s competi-tion at UC San Diego (2015).


Melanie SchmidtPostDoc Carnegie Mellon University Algorithmic Techniques for Solving the k-Means Problem on Big Data Sets

Claudia SchulzPhD CandidateImperial College London Explaining Logic Programming with Argumentation

Algorithm theory consists of de-signing and analyzing methods to solve computational problems.

The k-means problem is a computational problem from geometry. The input consists of points from the d-dimensional Euclidean space, i.e. vectors. The goal is to group these into k groups and to find a representative point for each group. Clustering is a major tool in machine learning: Imagine that the vectors represent songs in a music collection or handwritten letters. The clustering can show which objects are similar, and the representatives can be used to classify newly arriving objects.

There are many clustering objectives and the k-means objective might be the most popular among them. It is based on the Euclid-ean distance. The representative of a group is the centroid, i.e. the sum of the points in the group divided by their number. A grouping is evaluated by computing the squared Euclidean distance of ev-ery point to its representative and summing these up. The k-means problem consists of finding a grouping into k groups that minimizes this cost function.

The algorithmic challenges connected to the k-means problem are numerous. The problem is NP-hard, but it can be solved approxi-mately up to a constant factor. What is the best possible approxi-mation factor? Can we prove lower bounds? A different approach is to fix a parameter to lower the complexity. If the number of groups k is fixed, then the problem can be approximated to an arbitrary precision. This assumption also allows us to approximately solve the problem by algorithms that only read the input data once and in a given order — a main tool to deal with big data. How small can we make the memory need of such a streaming algorithm, and will the algorithm be efficient in practice? We see different answers to this question.

Bio

Melanie Schmidt obtained a master’s degree with distinction in computer science (with minor in mathematics) from TU Dortmund University in 2009. In her undergraduate studies, she focused on network flow theory, a topic that lies in the intersection between theoretical computer science and discrete mathematics. During her PhD time, her main focus became clustering algorithms, in particu-lar for large data sets. In 2012, Melanie Schmidt received the Google Anita Borg Memorial Scholarship that supports women that excel in technology. She graduated with distinction with her PhD theses on “Coresets and streaming algorithms for the k-means problem and related clustering objectives” in 2014. Then, she was awarded with a merit-scholarship by the German Academic Exchange Service (DAAD) to spend a year as a visiting PostDoc at the Carnegie Mellon University in Pittsburgh, where she visits Anupam Gupta.

Argumentation Theory and Log-ic Programming are two prom-inent approaches in the field of

knowledge representation and reasoning, a sub-field of Artificial Intelligence. One of the applications of such approaches are rec-ommendation systems, to be used for example for making medical treatment decisions. The main difference between Argumentation Theory and Logic Programming is that the former focuses on hu-man-like reasoning, thus sometimes neglecting the efficiency of the reasoning procedure, whereas the latter is concerned with the efficient computation of solutions to a reasoning problem, result-ing in a less human-understandable process. In recent years, Logic Programming has been frequently applied for the computation of reasoning problems in Argumentation Theory and has been found an efficient method for determining solutions to those problems. My research is concerned with the opposite direction, i.e. with using ideas from Argumentation Theory to improve Logic Programming techniques. One of the shortcomings of Logic Programming is that it does not provide any explanation of the solution computed for a given problem. For recommendation systems based on Logic Pro-gramming, this means that there is no explanation for a recommen-dation made by the system. I thus created a mechanism to explain Logic Programming solutions in a human-like argumentative style by applying ideas from the field of Argumentation Theory. A medical treatment recommendation can thus be automatically explained in the style of two physicians arguing about the best treatment.

Bio

Claudia Schulz received her B.Sc. in Cognitive Science from the University of Osnabrück in 2011. She then decided to specialise in Artificial Intelligence, receiving an M.Sc. in Artificial Intelligence from Imperial College London in 2012. Since 2012, Claudia is a Ph.D. candidate at Imperial College London interested in logic-based formalisms in Artificial Intelligence used for the representation of knowledge and for decision making based on the represented knowledge. Claudia is a keen lecturer and teaching assistant, which won her Imperial College’s Best Graduate Teaching Assistant Award in 2015. She was also involved in setting up the Imperial College ACM Student Chapter and served as its chair in 2014/15. Apart from academia, Claudia enjoys the outdoors and is an enthusiastic climb-er and runner.


Mahsa ShoaranPostdoctoral FellowCalifornia Institute of Technology Low-Power Circuit and System Design for Epilepsy Diagnosis and Therapy

Eva SongPhD candidate Princeton University A New Approach to Lossy Compression and Applications to Security

Epilepsy is a common neurolog-ical disorder affecting over 50 million people in the world. Ap-

proximately one third of epileptic patients exhibit seizures that are not controlled by medication. The development of new devices ca-pable of performing a rapid and reliable seizure detection followed by brain stimulation holds great promises for improving the quality of life of millions of people with epileptic seizures worldwide.

In this context, low-power circuit and system design techniques for data acquisition, compression and seizure detection in multichan-nel cortical implants are presented in the current research work. Compressive sensing is utilized as the main data reduction method in the proposed system. The existing microelectronic implementa-tions of compressive sensing are applied in a single-channel basis. Therefore, these topologies incur a high power consumption and large silicon area. As an alternative, a multichannel measurement scheme and an appropriate recovery scheme are proposed which encode the entire array into a single compressed data stream.

The first fully-integrated circuit that addresses the multichannel compressed-domain feature extraction for epilepsy diagnosis is proposed. This approach enables the real-time, compact, low-pow-er and low hardware complexity implementation of the seizure detection algorithm, as a part of an implantable neuroprosthetic device for the treatment of epilepsy. The developed methods in this research can be employed in other applications than epilepsy diag-nosis and neural recording, which similarly require data recording and processing from multiple nodes.

Bio

Mahsa received her B.Sc. and M.Sc. degrees in Electrical Engineer-ing and Microelectronics from Sharif University of Technology, Teh-ran, Iran in 2008 and 2010. In April 2015, she received her PhD from Swiss Federal Institute of Technology in Lausanne (EPFL) with hon-ors, working on implantable neural interfaces for epilepsy diagno-sis. She is currently a postdoctoral scholar in Mixed-mode Integrat-ed Circuits and Systems Lab at Caltech. Her main research interest is low-power IC design for biomedical applications, innovative system design for diagnosis and treatment of neurological disorders, im-plantable devices for cardiovascular diseases and neuroscience. She has received a silver medal in Iran’s National Chemistry Olympiad competition in 2003.

Rate-distortion theory is studied in the context of lossy compres-sion networks with and without

security concerns. A new source coding technique using the “likeli-hood encoder” is proposed that achieves the best known compres-sion rate in various lossy compression settings. It is demonstrated that the use of the likelihood encoder together with the Wyner’s soft-covering lemma yields simple achievability proofs for classical source coding problems. The cases of the point-to-point rate-dis-tortion function, the rate-distortion function with side information at the decoder (i.e. the Wyner-Ziv problem), and the multi-termi-nal source coding inner bound (i.e. the Berger-Tung problem) are examined. Furthermore, a non-asymptotic analysis is used for the point-to-point case to examine the upper bound on the excess distortion provided by this method. The likelihood encoder is also compared, both in concept and performance, to a recent alternative technique using properties of random binning. Also, the likelihood encoder source coding technique is further used to obtain new re-sults in rate-distortion based secrecy systems. Several secure source coding settings, such as using shared secret key and correlated side information, are investigated. It is shown mathematically that the rate-distortion based formulation for secrecy fully generalizes the traditional equivocation based secrecy formulation. The extension to joint source-channel security is also considered using similar en-coding techniques. The rate-distortion based secure source-chan-nel analysis has been applied to optical communication for reliable and secure delivery of an information source through an insecure multimode fiber channel.

Bio

Eva Song received her master’s and PhD degree in the Electrical En-gineering from Princeton University in 2012 and 2015, respectively. She received her B.S. degree in Electrical and Computer Engineer-ing from Carnegie Mellon University, Pittsburgh, PA, in 2010. In her PhD work, she studied lossy compression and rate-distortion based information-theoretic secrecy in communications. She is the recipi-ent of Wu Prize for Excellence in 2014. During 2012, she interned at Bell Labs, Alcatel-Lucent, NJ, to study secrecy in optics communi-cations. Her general research interests include: information theory, security, compression and machine learning.


Veronika Strnadova-NeeleyPhD Candidate University of California at Santa Barbara Efficient Clustering and Data Reduction Methods for Large-Scale Structured Data

The necessity for efficient algorithms in large-scale data analysis has become clear in the past few years, as unprecedented scales of information have become available in a variety of domains, from bioinformatics to social networks to signal processing. In many cas-es, it is no longer sufficient to use even quadratic-time algorithms for such data, and much of recent computer science research has focused on developing efficient methods to analyze vast amounts of information. My contribution to this line of research focuses on new algorithms for large-scale clustering and data reduction, by ex-ploiting inherent low-dimensional structure to overcome the chal-lenges of significant amounts of missing and erroneous entries. In particular, over the past few years, together with collaborators from Lawrence Berkeley National Lab, UC Santa Barbara, UC Berkeley, and the Joint Genome Institute, I have developed a fast algorithm for the linkage-group finding phase of genetic mapping, as well as a novel data reduction method for analyzing genetic mapping data. The efficiency of these algorithms has helped to produce accurate maps for large, complicated genomes, such as wheat, by relying on assumptions on the underlying ordered structure of the data. The efficiency and accuracy of these methods suggests that in order to further advance state-of-the-art clustering and data reduction methods, we should be looking closer at the structure of the data from a given application of interest. Assumptions on this structure may lead to much faster algorithms without losing much in terms of solution quality, even with high amounts of missing or erroneous data entries. In ongoing and future research, I will explore algorith-mic techniques which exploit inherent data structure for faster di-mensionality reduction methods in order to identify important and meaningful features of the data.

Bio

I am a PhD Candidate with a Computational Science and Engineer-ing emphasis at UC Santa Barbara, working with adviser John R. Gilbert in the Combinatorial Scientific Computing Lab. For the past few years I have been collaborating with researchers at Lawrence Berkeley National Lab, UC Berkeley and the Joint Genome Institute to design scalable algorithms for genetic mapping. Broadly, my research interests include scalable clustering algorithms, bioinfor-matics, graph algorithms, linear algebra and scientific computing. I completed my BS in applied mathematics at the University of New Mexico.

Huan Sun Graduate StudentUniversity of California at Santa Barbara Intelligent and Collaborative Question Answering

The paradigm of information search is undergoing a significant transformation with the popular-

ity of mobile devices. Unlike traditional search engines retrieving numerous webpages, techniques that can precisely and directly answer user questions are becoming more desired. We investigate two strategies: (1) Machine intelligent query resolution, where we present two novel frameworks: (i) Schema-less knowledge graph querying. This framework directly searches knowledge bases to answer user queries. It successfully deals with the challenge that answers to user queries could not be simply retrieved by exact key-word and graph matching, due to different information representa-tions. (ii) Combining knowledge bases with the Web. We recognized that knowledge bases are usually far from complete and informa-tion required to answer questions may not always exist in knowl-edge bases. This framework mines answers directly from large-scale web resources, and meanwhile employs knowledge bases as a sig-nificant auxiliary to boost question answering performance; (2) Hu-man collaborative query resolution. We made the first attempt to quantitatively analyze expert routing behaviors, i.e., how an expert decides where to transfer a question when she could not solve it. A computational routing model was then developed to optimize team formation and team communication for more efficient prob-lem solving. Future directions of my research include leveraging both machines and humans for better question answering and de-cision making in various domains such as healthcare and business intelligence.

Bio

Huan Sun is a PhD candidate in the Department of Computer Sci-ence at the University of California, Santa Barbara, and is expected to graduate in September 2015. Her research interests lie in data mining and machine learning, with emphasis on text mining, net-work analysis and human behavior understanding. Particularly, she has been investigating how to model and combine machine and human intelligence for question answering and knowledge discov-ery. Prior to UCSB, Huan received her BS in EE from the University of Science and Technology of China in 2010. She received the UC Regents’ Special Fellowship and the CS PhD Progress Award in 2014. She did summer internships at Microsoft Research and IBM T.J. Wat-son Research Center. Huan will join the Department of Computer Science at the Ohio State University as an assistant professor in July 2016.


Ewa SytaPhD Candidate Yale University Certificate Cothority: Towards Trustworthy Collective CAs

Rabia Tugce YazicigilPhD Candidate Columbia University Enabling 5/Next-G Wireless Communications with Energy-Efficient, Compressed Sampling Rapid Spectrum SensorsCertificate Authorities (CAs) sign

certificates attesting that the holder of a public key legitimate-

ly represents a name such as google.com, to authenticate SSL/TLS connections. Only if a server can produce a certificate signed by a trusted CA, will the client’s browser accept it and establish a secure connection.

Current web browsers directly or indirectly trust hundreds of CAs, any one of which can issue fake certificates for any domain. Conse-quently, it takes only one compromised or malicious CA to threaten the security of the entire PKI and in turn, everyone on the Internet. Due to this “weakest-link” security, hackers have stolen the “master keys” of CAs such as DigiNotar and Comodo and successfully gener-ated fake certificates for website spoofing and man-in-the-middle attacks.

We propose to replace current, high-value certificate authorities with a certificate cothority (CC) — a practical system, which em-bodies strongest-link security by allowing all participants to vali-date certificates before they are issued and endorsed, and therefore proactively prevent their misuse.

We build certificate cothorities using an instantiation of a collective authority (cothority), an architecture we propose to enable thou-sands of participants to witness, validate, and co-sign an authority’s public actions, with moderate delays and costs.

Each of potentially thousands of hosts comprising a certificate co-thority independently validates each new batch of certificates, ei-ther contributing a share of a collective digital signature or with-holding it and raising an alarm if misbehavior is detected. This collective signature attests to the client that not just one but many (ideally thousands) well-known servers independently checked and signed off on a certificate. Therefore, a certificate cothority guaran-tees strongest-link security whose strength increases as the col-lective grows, instead of decreasing to weakest-link security as in today’s CA system.

Bio

Ewa Syta is a PhD candidate in the Computer Science Department at Yale University. She is co-advised by Professors Michael Fischer and Bryan Ford. Prior to joining Yale, she earned her B.S. and M.S. in Computer Science and Cryptology from Military University of Technology in Warsaw, Poland. Her research interests lie in comput-er security. She is particularly interested in the security and priva-cy issues users face as a result of engaging in online activities. She has been working on developing stronger anonymous communi-cation technologies, privacy-preserving biometric authentication schemes, anonymous and deniable authentication methods as well as different ways to generate good and verifiable randomness in a distributed setting.

Future 5G networks will drastically advance the way we interact with each other and machines and how machines interact with each oth-er. The data storm driven by emerging technologies like “Internet of Things”, “Digital Health”, machine-to-machine communications, and video over wireless, leads to a pressing spectrum scarcity. Future cognitive radio systems employing multi-tiered, shared-spectrum access are expected to deliver superior spectrum efficiency over existing scheduled-access systems. We focus on lower tiered ‘smart’ devices that evaluate the spectrum dynamically and opportunisti-cally use the underutilized spectrum. These smart devices require spectrum sensing for interferer avoidance. The integrated interferer detectors need to be fast, wideband and energy efficient. We are developing quadrature analog-to-information converters (QAIC), a novel compressed sampling (CS) technique for bandpass signals. With a QAIC the wideband spectrum can be sampled at a substan-tially lower rate set by the information bandwidth, rather than the much higher Nyquist rate set by the instantaneous bandwidth. As a result, innovative spectrum sensor RF ICs can be designed to si-multaneously deliver a very short scan time, a very wide span and a high frequency resolution, while requiring only modest hardware and energy resources. This is not possible with existing spectrum scanning solutions. Our first QAIC RF IC demonstration scans a wide-band 1GHz span with a 20MHz resolution bandwidth in 4.4μsecs, offering 50x faster scan time compared to traditional sweeping spectrum scanners and 6.3x compressed aggregate sampling rate compared to traditional concurrent Nyquist rate approaches. The unique QAIC bandpass architecture is 50x more energy efficient compared to traditional spectrum scanners and 10x more energy efficient compared to existing lowpass CS spectrum sensors.

Bio

Rabia Tugce Yazicigil received the BS degree in electronics engineer-ing from Sabanci University, Istanbul, Turkey, in 2009, and the M.S. degree in electrical and electronics engineering from École Poly-technique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, in 2011. She is currently a PhD candidate in the electrical engineering department at Columbia University, New York, advised by Prof. Pe-ter Kinget and co-advised by Prof. John Wright. Her interdisciplin-ary research work focuses on developing and implementing novel spectrum sensing architectures exploiting compressed sampling for future cognitive radio systems. She collaborated and presented her research on “A 2.7-3.7GHz Rapid Interferer Detector Exploiting Compressed Sampling with a Quadrature Analog-to- Information Converter” together with a live demo of the system at the presti-gious 2015 IEEE International Solid-State Circuits Conference (ISS-CC), which was supported by the National Science Foundation EARS program in collaboration with Interdigital Communications. She has been a recipient of a number of awards, including the second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015) and 2014 Millman Teaching Assistant Award of Columbia University.


Qi (Rose) YuPhD Candidate University of Southern California Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning

Zhou YuPhD StudentCarnegie Mellon University Engagement in Multimodal Interactive Conversational Systems

Many data are spatiotemporal by nature, such as climate mea-surements, road traffic and user

checkins. Complex spatial and temporal dependencies pose new challenges to largescale spatiotemporal data analysis. Existing models usually assume simple interdependence and are compu-tationally expensive. In this work, we propose a unified lowrank tensor learning framework for multivariate spatiotemporal analysis, which can conveniently incorporate different properties in the data, such as spatial clustering, temporal periodicity and shared structure among variables. We demonstrate how the framework can be ap-plied to two central tasks in spatiotemporal analysis: cokriging and forecasting. We develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantees. Em-pirical evaluation shows that our method is not only significantly faster than existing methods but also more accurate.

Bio

Qi (Rose) Yu is a fourth year PhD candidate at the University of Southern California with a particular interest in Machine Learning and Data Mining. Rose’s research focuses on largescale spatiotem-poral data analysis where she designs algorithms to perform pre-dictive tasks in applications including climate informatics, mobile intelligence, and social media. Her work is supported by USC An-nenberg Graduate Fellowship. She has interned in Microsoft R&D, Intel Lab, Yahoo Labs, and IBM Watson Research Center. She was selected and funded as one of 200 outstanding young computer scientists and mathematicians all over the world to participate the Heidelberg Laureate Forum.

Prior to enrolling at USC, Rose earned her Bachelors Degree in Com-puter Science from Cho Kochen Honors College at Zhejiang Uni-versity. Before beginning her graduate studies, she was awarded Microsoft Research Asia Young Fellowship. Outside the lab, she is the technical cofounder of NowMoveMe, a neighborhood discov-ery startup.

Autonomous conversational sys-tems, such as Apple Siri, Google Now, Microsoft Cortana, etc. act

as personal assistants who set alarms, mark events on calendars, etc. Some systems provide restaurant or transportation information to users. Despite the capability of completing these simple tasks through conversations, they still act according to pre-defined task structures and do not sense or react to their human interacts’ non-verbal behaviors or internal states such as the level of engagement. This problem can also be found in other interactive systems.

Drawing knowledge from human-human communication dynam-ics, I use multimodal sensors and computational methods to un-derstand and model user behaviors when interacting with a system that has conversational abilities (e.g. spoken dialog systems, virtual avatars, humanoid robots). By modeling the verbal and nonverbal behaviors, such as smiles, we infer high-level psychological state of the user, such as attention and engagement. I focus on main-taining engaging conversations by modeling users’ engagement states in real-time and making conversational systems adapt to their users via techniques, such as adaptive conversational strate-gies and incremental speech production. I apply my multimodal engagement model in both non-task-oriented social dialog frame-work and task-oriented dialog framework that I designed. I devel-oped an end-to-end, non-task-oriented multimodal virtual chatbot, TickTock, which serves as a framework for controlled multimodal conversation analysis. TickTock can carry on free-form everyday chatting conversations with users in both English and Chinese lan-guages. Together with ETS Speech and Dialog team, I developed task-oriented system, HALEF, which is also a distributed web-based system. HALEF has both visual and audio sensing capabilities for human behavior understanding. Users can access the system via a web browser, which in turn reduces the cost and effort in data col-lections. HALEF can be easily adapted to different tasks. We imple-mented an application so that the system acts as an interviewer to help users prepare for job interviews.

For demos, please visit my webpage: http://www.cs.cmu.edu/~zhouyu/

Bio

Zhou is a fifth-year PhD student in the Language Technology In-stitute, School of Computer Science, Carnegie Mellon University, where she works with Prof. Alan Black and Prof. Alex Rudnicky. Zhou creates end-to-end interactive conversational systems that are aware of their physical situation and their human partners via real time multimodal sensing and machine learning techniques. Zhou holds a B.S. in computer science and a B.A. in English language with linguistic focus from Zhejiang University in 2011. Zhou also interned at Microsoft Research with Eric Horvitz and Dan Bohus, at Education Testing Service with David Suendermann-Oeft, and at Institute for Creative Technologies in USC with Louis-Philippe Morency. Zhou is also a receiver of the Quality of Life Fellowship.

Rising Stars 2015 35

Rising Stars 2015 Committee

Workshop ChairAnantha Chandrakasan, Workshop Chair Vannevar Bush Professor of Electrical Engineering and Computer Science Department Head, MIT Electrical Engineering and Computer Science

Program ChairsRegina Barzilay, Workshop Technical Co-Chair Professor of Electrical Engineering and Computer Science, MIT

Dina Katabi, Workshop Technical Co-Chair Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, MIT

Asu Ozdaglar, Workshop Technical Co-Chair Professor of Electrical Engineering and Computer Science, MIT Director, Laboratory for Information and Decision Systems

36 Rising Stars 2015

Debroah Hodges-PabónWorkshop Administrative Manager Microsystems Technology Laboratories, MIT 617.253.5264 [email protected]

Dorothy Curtis Research ScientistComputer Science and Artificial Intelligence Lab, MIT 617.253.0541 [email protected]

Audrey ResutekCommunications OfficerElectrical Engineering and Computer Science, MIT617.253.4642 [email protected]

Rising Stars 2015 Sponsors

WorkshopAdministration

Rising Stars 2015 37

“The Rising Stars workshop was an amazing opportunity — to chat with absolutely top professors about my research, to learn from insiders about how to thrive in an academic career, and to meet the next wave of world-class researchers in EECS.”

— Tamara Broderick, 2013 Rising Stars alumna Assistant Professor, Electrical Engineering and Computer Science Massachusetts Institute of Technology

“The Rising Stars workshop helped me understand thoroughly all the aspects of the job application process, so I can do my best at every step of this process.”

— Raluca Ada Popa, Rising Stars 2013 alumna Assistant Professor University of California at Berkeley

2 Rising Stars 2015

Contact: Anantha P. Chandrakasan, Department Head, EECS Vannevar Bush Professor of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Ave., 38-403 Cambridge, MA 02139-4307

Phone: 617.253.4601 Fax: 617.253.0572 617.258.7619 Email: [email protected]

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