Irina Rish

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. Particlarly, I have done work on approximate inference in graphical models, information-theoretic experiment design and, more recently, active learning, with applications are in the area of autonomic computing - automated management of complex distributed systems, which includes varuois diagnosis, prediction and online decision-making problems. My recent research interests include applications of machine learning to fMRI data analysis.


In 2002 and 2003, I taught Statistical Pattern Recognition (ELEN E6880) as an adjunct professor at the Department of Electrical Engineering of Columbia University. In Spring 2007, I also taught a machine-learning class on Learning and Empirical Inference at the Computer Science Department of Columbia.

My current research interests include:

• Machine learning, pattern recognition, and data mining
  
• Inference and learning in probabilistic graphical models (Bayesian Networks)
  
• Approximation algorithms: efficiency versus accuracy trade-off
  
• Properties of real-life networks: scale-free and small-world topologies
  
• Applications: learning and inference in complex distributed computer systems