Irina Rish is a Research Staff Member (RSM) at IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her primary research interests are in the areas of probabilistic inference, machine learning, and information theory.
Particularly, Irina Rish has 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.
Her 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, She taught several graduate courses at Columbia University as an adjunct professor at the Department of Electrical Engineering: Statistical Pattern Recognition in Spring of 2002 and 2003, and Sparse Signal Modeling in Spring of 2011. In Spring 2007, She also taught a machine-learning class on Learning and Empirical Inference at the Computer Science Department of Columbia.
Irina Rish co-organized several workshops at various machine-learning conferences. She is currently serving on the editorial board of the Artificial Intelligence Journal (AIJ).