Dheeraj KalmekolanCurriculum Vitae

InterestsNatural Language Processing, Machine Learning, Big data, Cryptography, Number Theory

Education2012–2016 Bachelor of Technology Computer Science, Indian Institute of Technology,

Mumbai, GPA – 8.39/10.Also completed Honors program

2010–2012 Higher Secondary School, Narayana Junior College, Hyderabad, Score – 95.7%.2010 Matriculation, Narayana Olympiad School, Hyderabad, Score – 94.16%.

Work ExperienceInternships

Summer 2015 Software Engineering Intern, LinkedIn, Bengaluru.Worked on an efficient search engine for searching through code for various programminglanguages. Added several useful features to give the programmer an experience of an IDE.

Detailed achievements:{ Devised an automated system that would resemble a classic producer-consumer system

so that changes to the code are made visible as soon as the updates happen using Kafka{ Designed a Java Parser to extract necessary metadata for IDE like experience.{ Implemented a tri-gram tokenization technique which can be extended to any IR engine

to handle regular expression queries common among programmers{ Propounded and implemented an innovative scoring mechanism which would order the

queries more importantly in regular expression searches to minimize the length of query{ Devised a data structure approach to reduce both the data to be indexed and the search

time{ Received a Pre-Placement Offer for my work

Summer 2014 Software Engineering Intern, Housing.com, Mumbai.Web Traffic Funneling to detect user drop rate in a website

{ Devised a linear time algorithm to track user navigation from page to page, implementedit in Python

{ Developed a flexible web application to visualize user navigation behavior on the websiteusing Django

{ Used highcharts APIś to implement a stacked representation of user flow the websitewith respect to various aspects like user id/session id, source of user visiting website

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B.Tech Technical ProjectAutumn 2015 Improving Telugu OCR

Supervisor Professor Saketha NathDescription Telugu is an Indian language which has very complicated script to process. I am

working on various machine learning techniques to improve the accuracy of the OCRbased on open source software Tesseract. Main techniques that are being used areDeep learning, Hierarchial clustering and Language Modelling.Convoluted NeuralNetworks are being used for glyph detection. Another technique of improvisationwas to use Temporal Classifiation. Developed an RNN and used forward-backwardapproach to train Neural networks to scribe in different fonts given text data usingonline and offline approaches.

ProjectsSpring 2016 Smart IrrigationSupervisor Professor Kavi AryaDescription India has a large number of small-scale farms where there is a water scarcity. To

tackle this issue, we would like water the plants only when it is needed. For this, weused a TIVA microcontroller which is connected to water level sensor, soil moisture,and temperature sensors. Using data from these sensors we will water the plantsusing machine learning algorithms like KNN and corresponding data prediction isstored in a remote client sent via a wifi module

Spring 2016 Zero Knowledge Proofs in AuthenticationSupervisor Professor Bernard MenezesDescription Traditional Authentication systems have several weaknesses which leads to passwords

being compromised. Zero Knowledge Proofs are an attempt to avoid this.In this auser displays a knowledge of knowing the secret key than the actual key itself whichis generally a hard problem (NP-complete problems).We have used Elliptic curvesfor the Authentication.

Spring 2016 Robust Point set registration using Kernel methodsSupervisor Professor Ajit RajwadeDescription Image alignment is one of the significant problems in computer vision. Kernel

methods are known to be robust to noise for this. Tested this for PLY files forknown transformations like Translation,Rotation and Affine. This can be used alongwith SIFT for various applications like Video Stabilization.

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Autumn 2014 Suggesting the relevant words for expanding Wordnet ontology using WikipediaSupervisor Professor Pushpak BhattacharryaDescription Designed a method of refining wordnet using wikipedia’s data. Followed the research

paper "Mapping wordnet word to wikipedia articles" and designed a way of suggestingpossible additions. Performed title similarity and Information Retrieval to shortlistto a few articles. Out of the shortlisted candidates ideal one is picked using varioussimilarity measures. In this new article top 5 most relevant words of the article aresuggested as relevant words

Spring 2014 Grapheme to Phoneme interconvertionSupervisor Professor Pushpak BhattacharryaDescription Using data from CMU dictionary identified all the words whose length of grapheme

and phoneme sequence length is same. Designed a Hidden Markov Model and usedViterbi algorithm. Observed an accuracy of 80% for grapheme to phoneme and 70%for phoneme to grapheme.Designed a neural network which can be used to avoidthe confusion among the vowels.

Autumn 2015 Texture Synthesis using non-parametric samplingSupervisors Professor Ajit Rajwade, Prof Suyash AwateDescription The texture synthesis process grows a new image outward from an initial seed, one

pixel at a time. A Markov random field model is assumed, and the conditionaldistribution of a pixel given all its neighbors synthesized so far is estimated byquerying the sample image and finding all similar neighborhoods. The degree ofrandomness is controlled by a single perceptually intuitive parameter. The methodaims at preserving as much local structure as possible and produces good results fora wide variety of synthetic and real-world textures.

Spring 2013 An Encrypted application for texting and file transferSupervisor Professor Amitabha SanyalDescription Designed a layer above TCP like SSL which provides security in MIT-Scheme. Used

RSA-2048 for key exchange and used SDES and RC4 for encryption. This layer isused for secure data transfer and file transfer without any compression. Implementeda multi-threaded server and client for multiple connections

Spring 2014 Implementing virtual memory for an Operating SystemSupervisor Professor Dhananjay DhamdhereDescription Designed a virtual memory for an Operating system called the Geek OS. De-

signed several components like Multi level page tables,TLB,system calls for schedul-ing,sleep,super pages etc.

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Scholastic Achievements{ Secured All India Rank 45 in IIT JEE 2012 among 5,00,000 students{ Secured All India Rank 1 in VITEEE 2012 among 1,50,000 students{ Secured State Rank 4 in EAMCET 2012 among 2,70,000 students{ Secured All India Rank 27 in AIEEE 2012 among 10,00,000 students{ Stood 4th in country for the South Indian Mathematics Olympiad conducted in 2009-2010{ Stood 3rd in country for the South Indian Chemistry Olympiad conducted in 2009-2010{ Received KVPY Fellowship offered by Government of India, stood 50th in the country{ Attended Orientation and Selection Camp for Top 35 of country for AstroPhysics in 2011-2012

and Science 2009-2010

Programming skills{ Programming languages: C, C++, Java, Python, Matlab{ Web and Web Frameworks: HTML, CSS, Java Script, Django, Play, Dust{ Databases Related: PostgreSQL, mongoDB, Hadoop, Pig{ Scripting: Bash,Sed, Awk

Talks DeliveredAutumn 2014 Almost Sure Convergence of K-means Clustering algorithm

Supervisor Professor Rajani JoshiDescription McQueen was able to show that the K-means clusters converge almost surely

but there was a much more stronger condition that the cluster centers also infact converge almost surely. This was later proved by Pollard by a very rigorousmathematical proof in his seminal work using Hausdorff metrics. This paper waspresented in a seminar for Statistical inference course

Spring 2014 An HMM based approach to Haptic Human interactionSupervisor Professor Pushpak BhattacharyyaDescription Haptic interactions are those which we perceive by touch. Most of them are

subjective and compliance of the interaction is really important for us to feel them.The algorithms must also be online and Real-Time for this. One of the essentialhaptic skills is a handshake. It can be modelled using a Linear Time invariantModel(LTI) of a spring block system with resistance(friciton). Every stage of thehandshake is treated as a symbol and thus we model this using a HMM withthese symbols as hidden states and observation as handshake. The parameters areestimated online.

Spring 2015 Influence of Religion on MarriageSupervisor Professor Parameshwar BhatDescription Most of the religions give importance to marriage and we can see the effect of

religion and culture in marriages today. However given that the marriage is whentwo people like each other we traced the reasons how marriage evolved and howreligion had involved itself in this tradition.

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Teaching{ Teaching Assistant for the course Machine Learning for the Spring semester 2016 in IIT Bombay

under Prof.Ganesh Ramakrishnan

Positions of Responsibility{ Worked as a co-ordinator for Techfest a technical event for the team Lecture series and organized

talks of several eminent speakers

Extra Curricular Activities{ Completed NSO in aerobics at IIT Bombay in an year long program{ Participated is Samwad where we discuss social issues in India{ Participated in chess events in institute in a club called Dark Knight{ Interviewed various people in institute of various ethnic backgrounds on the topic of Religion{ Made a Stop Motion Film on snake vs prey.

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