KSHITIJ SHARMA 509-Apt 1, Tartan Circle• Raleigh, NC 27606 • 919-986-1063• ksharma3@ncsu.edu

EDUCATION North Carolina State University, Raleigh, NC GPA: 3.96/4.0 Master of Science in Computer Science Graduated May 2016 National Institute of Technology, Silchar, India Bachelor of Computer Science and Engineering Graduated August 2011

TECHNICAL SKILLS

Languages and DB Java(proficient), C++(proficient), Python, R, C, JS, JQuery, CSS, HTML, Oracle 10g, SQL Server Frameworks and packages

Spark, Storm/Trident, Kafka, Redis, Node.js, Word2vec, Doc2vec, Skip-gram, Elasticsearch, Tensor Flow, iPython, Scikit-learn, Visual Studio, Eclipse, Git, Svn

WORK EXPERIENCE

SAS Institute Inc. , Cary, NC, USA Software Engineer, Intern June 2015 – April 2016

Back-end framework development, Data Visualization Division (C++) Implementation of new SAS Procedures for the SAS Graph Template Language in the C++ graph framework. Migration of Procedures and functionalities from Java to the new C++ platform.

Sapient Corporation, India Software Engineer Nov 2011 - June 2014 Multi channel e-commerce platform development (Java)

▪ Implemented an in-memory cache framework for handling real-time inventory transactions.

▪ Implemented and integrated payment capture billing system from the scratch. Handling an average of 35000

payment instructions and 100,000 orders a day.

▪ Developed order capture framework, marketing preference service (sms/e-mail) for the e-commerce platform.

ACADEMIC PROJECTS

Music Recommender System using Apache Spark: A highly scalable system, implemented using collaborative filtering technique for implicit feedback dataset using the Alternating Least Square algorithm in Python and Spark.

Image and Text Labelling: Embedding images (CNN) and texts (Skip-gram) into a common vector space using Python

Tensor flow library. Application capable of identifying relevant articles/labels, given an input image and vice versa.

Multi-Client chat-room with persistent chat log handling: A multi-client chat room developed using Node.js and Redis, capable of persistent in-memory data handling, populating prior chat logs in real-time.

Querying and processing trending topics on real-time stream: Framework built to query Heavy hitters, top-K, and

frequency of topics, using the Storm/Trident for stream (Tweets) processing. Elasticsearch used for indexing and

querying, Count-Min Sketch for Heavy Hitters, top-K and Bloom filter for maintaining stop words list.

Sentimental Analysis on IMDB reviews using NLP: Used Logistic regression to classify reviews on the features extracted

from two models, doc2Vec and bag of words model. Achieved an accuracy of 83% and 84% respectively. (Python)

Market segmentation using Community Detection on Attributed graph: Implemented the algorithm using Newman

modularity function on Facebook dataset, with influence propagation 20% better than baseline K-means clustering.

URL recommendation using association rule mining: Using the past activity logs of the students, this application

recommended URL’s using the Apriori algorithm in a fast memory efficient way using count-min sketch. (Node.js, R)

RELEVANT COURSEWORK

Graph Theory, Advanced Algorithms, Data Mining, Queuing Theory, Machine Learning, Data Structures, Database

Management Systems, Computer and Network Security, Simulation Techniques, Data Science

PUBLICATION AND ADDITIONAL EXPERIENCE

S. Ranshous, S. Harenberg, K. Sharma, N. F. Samatova, A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations". SIAM International Conference on Data Mining (SDM), 2016 (accepted)

Member, Instructional team, responsible for developing course materials and projects on Adwords problem, for a graduate level course on Algorithms for Business Intelligence, NC State University (Spring 2016)