

2013/02/01, MC103, 14:30  15:30
Path analysis of continuoustime stochastic systems
Theodore
Perkins
, Ottawa Hospital Research Institute
Abstract:
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 continuoustime Markov chains  a class of
continuoustime discretestate 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 continuoustime
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 highthroughput 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.


