Christian Shelton @ eHarmony

http://photos3.meetupstatic.com/photos/event/b/a/5/6/event_261047702.jpeg
12:00 lunch served
12:30 talk starts
13:30 Q/A ends

Title: Continuous-Time Models: Why and How

Abstract:

Discrete-time models are abundant in artificial intelligence: hidden Markov models, dynamic Bayesian networks, Markov decision processes, and (most) auto-regressive models assume time passes in discrete jumps. Yet, most processes modeled actually evolve in continuous time. This talk explores the problems inherent in this dichotomy, focusing on Markovian models.

First, I will discuss the theoretic and experimental difficulties when
modeling in discrete time. In doing so, I will present continuous-time Markov processes, drawing analogies to their discrete-time counterparts. Second, I will present the continuous-time analog of a dynamic Bayesian network: a continuous-time Bayesian network (CTBN). The talk will include an overview of the learning and inference literatures for CTBNs, showing how continuous-time aids in the development of efficient inference techniques. Finally, I will show some application results employing CTBNs on real data sets.

Bio:

Christian R. Shelton is an Associate Professor of Computer Science at the University of California at Riverside. He has spent time as a visiting researcher at Intel Research and Children's Hospital Los Angeles. He was the Managing Editor of the Journal of Machine Learning Research and on the editorial board of the Editorial Board of the Journal of Artificial Intelligence Research.

Dr. Shelton received his B.S. in Computer Science from Stanford University and his Ph.D. from MIT. His research interest is in statistical approaches to artificial intelligence, mainly in the areas of machine learning and dynamic processes. He also works at the intersection of learning and topics as varied as computer vision, sociology, game theory, medical informatics, and robotics.

Park in the garage underground and bring your ticket for validation on your way out.