I am a Research Staff Member (RSM) at IBM T.J. Watson Research Center. I received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. My primary research interests are in the areas of probabilistic inference, machine learning, and information theory. Particularly, I have done work on approximate inference in graphical models, information-theoretic experiment design and active learning, with applications are in the area of autonomic computing - automated management of complex distributed systems, which includes various diagnosis, prediction and online decision-making problems. My current research is in the area of machine-learning applications to computational biology and neuroscience, with a particular focus on statistical analysis of brain imaging data such as fMRI. In the past years, I taught several graduate courses at Columbia University as an adjunct professor at the Department of Electrical Engineering: Statistical Pattern Recognition (ELEN E6880) in Spring of 2002 and 2003, and Sparse Signal Modeling (ELEN E6898) in Spring of 2011. In Spring 2007, I also taught a machine-learning class on Learning and Empirical Inference (COMS 6998-4) at the Computer Science Department of Columbia. I co-organized several workshops at various machine-learning conferences.
Here is my personal webpage.
12/8/2016 Learning About the Brain: Neuroimaging and Beyond. Plenary talk at NIPS-2016, Barcelona, Spain (talk slides)
11/2015 MLconf @ San Francisco
IBM Research scientists James Kozloski, Cliff Pickover, and Irina Rish's US Patent 9177257, or "Non-transitory article of manufacture and system for providing a prompt to user for real-time cognitive assistance" could help those with chronic memory loss remember, via a cognitive digital assistant.
Collaboration with U. of Alberta: IBM News Release (24 June 2015)