Theodore Perkins - Ottawa Hospital Research Institute

Feb. 1, 2013, 1 p.m. - Feb. 1, 2013, 2 p.m.

MC103

In analyzing a stochastic dynamical system, one of the most natural questions we may ask is: What is the most probable path the system takes between a point A and a point B? This basic question is at the core of many applied computing problems, including speech recognition, motion tracking, error correction, etc. When time is discrete, the Viterbi algorithm provides an efficient and optimal solution. When time is continuous, however, the problem has not been adequately addressed. I will present several new results and ongoing work in the area of path inference for continuous-time Markov chains -- a class of continuous-time discrete-state stochastic systems. A key observation is that the right solution to path inference in these systems is not simply to discretize time and apply the Viterbi algorithm. Rather, one must reformulate the problem appropriately for the continuous-time case, and develop new solutions to that problem. I will describe these solutions and demonstrate them on applications in fault diagnosis, HIV virus evolution, and the dynamics of neural ion channels. Theodore Perkins obtained his PhD from the University of Massachusetts Amherst in 2002, in Computer Science. From 2002 to 2005 he was a Postdoctoral Fellow at McGill, in the School of Computer Science and the Department of Physiology. From 2006 to 2008 he was an Assistant Professor in the School of Computer Science at McGill. In 2009, he moved to Ottawa, where he is a Scientist at the Ottawa Hospital Research Institute, an Assistant Professor with the University of Ottawa, and Director of the Ottawa Bioinformatics Core Facility. His research spans bioinformatics, computational biology, mathematical biology and machine learning. Much of his work focuses on developing methods for the analysis of high-throughput sequencing data, and using that data to unravel gene regulatory networks in stem cells. However, he also is interested in understanding information processing at the cellular level, and in general computational methods for analyzing stochastic dynamical systems.