Connecting Computer Science and Statistics Methods in Temporal Data Mining
(Joint Seminar with ECE)

Dr. K.P. Unnikrishnan
General Motors Research

Date:  Thursday, September 29, 2005
Time: 3:00pm-4:00pm
Place: 2250 Engineering

Abstract: Discovering frequent episodes from event streams has applications in areas ranging from automotive manufacturing to bio-informatics and neurobiology. We describe efficient algorithms for frequent episode discovery when the events have durations. We then connect these counting-type methods in Computer Science with Hidden Markov Models (HMMs) in Statistics. This allows us to determine the statistical significance of the discovered frequent episodes. We show use of these methods for throughput improvement and root-cause analysis in automotive assembly plants. We also illustrate their use for analyzing multi-neuronal data.

Biography: Dr K.P. Unnikrishnan received the PhD degree in Physics (biophysics) from Syracuse University, Syracuse, New York, in 1987. He is currently a staff research scientist at the General Motors R&D Center, Warren, Michigan. Before joining GM, he was a postdoctoral member of the technical staff at AT&T Bell Laboratories, Murray Hill, New Jersey. He has also been an adjunct assistant professor at the University of Michigan, Ann Arbor, a visiting associate at the California Institute of Technology (Caltech), Pasadena, and a visiting scientist at the Indian Institute of Science, Bangalore. His research interests concern neural computation in sensory systems, correlation-based algorithms for learning and adaptation, dynamical neural networks, and temporal data mining.