Kumar Sricharan currently focuses on statistical machine learning and data mining methods for anomaly detection and pattern recognition in multivariate, temporal, and relational data. His research interests include statistics, machine learning, data mining, and signal processing with specific focus on ensemble methods and large sample estimation theory. He has particular interest in applications concerning anomaly detection and structure discovery in data.
Prior to PARC, Kumar was a research engineer at NASA Ames, where he conducted research on mining aviation data for anomalies with regard to fuel consumption efficiency and aviation safety. He also completed a R&D internship at General Motors, with research on classifying driving behavior based on statistical analysis of headway time-series data.
Dr. Sricharan earned his Ph.D. in Electrical Engineering, Systems in 2012, M.A. in Statistics in 2011, and M.S. in Electrical Engineering: Systems in 2009, all from the University of Michigan, Ann Arbor. His doctoral work on efficient estimation of probability density functionals using neighborhood graphs has resulted in publications in esteemed peer-reviewed conferences and journals, and is currently nominated for the best dissertation award at the University of Michigan. He also received his B.Tech degree in Electrical Engineering from IIT Madras in 2006.