I am a research scientist in the Laboratory for Computational Physiology (LCP) at the MIT Institute for Medical Engineering & Science (IMES). My research focuses on the use of machine learning techniques to derive insights from physiological and clinical data for informed treatment decision making. My interests include representation learning, structure discovery, generative probabilistic models, switching state-space models, Bayesian non-parametric learning of disease phenotypes, and, more recently, off-policy reinforcement learning and causal inference. I received my Master’s degree in Computer Science from Georgia Institute of Technology, and my Ph.D. from Massachusetts Institute of Technology in June 2005.
Research/Post-doc position available: I am looking for a highly-motivated post-doc to join my research team.
Local organizing committee of the Computing in Cardiology Conference held in Boston, September 2014.
Member of the IEEE and the IEEE Engineering in Medicine and Biology Society.
Closing plenary presentation in the 2014 Computing in Cardiology Conference on “Uncovering Clinical Significance of Vital Sign Dynamics in Critical Care,” Boston, September 2014.
Closing plenary presentation in the 2010 Computers in Cardiology Conference on “Hypotension as a risk factor for acute kidney injury in ICU patients,” Belfast, September 2010.
Best poster presentation award, Computers in Cardiology Conference, September 2007.
Recipient of Schoettler Fellowship, awarded to outstanding incoming graduate students, MIT.
Appointed as Head TA for 1.00, Introduction to Computers and Engineering Problem Solving, an MIT course comprising 200 students and 18 student teaching staff, 2001.
MIT class awards: (1) algorithm & implementation in C++ won 2nd place in final project contest in MIT course 1.124; (2) voted one of the top 3 term papers in MIT course 6.853, Computer Systems.