Meet the Team

Parvez has an extensive background in computer vision, machine learning, image processing and signal processing. He possesses several years of experience in working with multi-disciplinary cross-functional teams and translating real-world problems into a mathematical setting. He is experienced in diverse application domains such as medical imaging, bioinformatics, neuroscience, multimedia delivery, remote sensing and camera networks.
Parvez obtained his Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley.

Jennifer is a Research Fellow in the Department of Biomedical Engineering at the University of Texas, Austin. Jennifer’s bioinformatics and computational work is centered on taking very large, complex data sets and applying data visualization techniques, iterative corrections and machine learning algorithms to derive meaningful interpretation. Jennifer holds a PhD in Tumor Biology from Georgetown University in Washington DC, and possesses 10+ years experience using statistical methods and modeling for cancer research.

Elissa’s interests include using smart grid big data, predictive analytics, and machine learning to create data-driven solutions to energy efficiency and electrical grid management. She currently works as software engineer with the SilverLink Sensor Network team at Silver Spring Networks. Elissa received her M.S. in Energy Tech. Engineering from Stanford University and her B.S. in Civil and Environmental Engineering from Carnegie Mellon University.

Roman is a Senior Sceintist with Bing query understanding team at Microsoft. He currently works on research problems in spoken language understanding to deliver robust, natural and scalable solutions across a rich set of scenarios and languages. Previously he has worked as a researcher for Bing data mining team as a Researcher.
Roman obtained his Ph.D. in Computer Science from University of British Columbia. He worked on Monte Carlo framework method based on Markov chain and sequential Monte Carlo for efficient sampling from high-dimensional distributions. Roman specializes in Monte Carlo methods (MCMC, SMC), Bayesian statistics, machine learning, natural language understanding

Yuan has extensive statistical modeling experience in linear models, logistic models, time series models, decision trees, model regularization, and cross validation. Yuan has a PhD in Economics from the University of California at Davis.

John is a Senior Security Researcher at HP Security Research. John is constantly amazed by the amounts of data available around us and thinks about how to squeeze the maximum amount of meaning from those data. He also thinks about how to offload as much work as possible to machines without losing accuracy. John holds a BS in Electrical Engineering and Computer Science and a BA in Cognitive Science from UC Berkeley. John worked on automatic computer virus classification using machine learning and collaborative filtering using social networks.