Electrical and Computer Engineering, UCSD

**Monday, January 24, 2:30-3:30PM, CEPSR 414**

**Abstract:**
Active sequential hypothesis testing problem arises in a broad spectrum
of applications in cognition, communications, design of experiments, and
sensor management. In all of these applications, a decision maker is
responsible to take actions dynamically so as to enhance information
about an underlying phenomena of interest in a speedy manner while
accounting for the cost of communication, sensing, or data collection.
In particular, due to the sequential nature of the problem, the decision
maker relies on his current information state to constantly
(re-)evaluate the trade-off between the precision and the cost of
various actions.

In this work, we first discuss active sequential hypothesis testing as a
partially observable Markov decision problem. In particular, we provide
a brief survey of the design of experiment literature and the dynamic
programming interpretation of information utility introduced by De
Groot. Using Blackwell ordering, we, then, connect this stochastic
control theoretic notion of information utility to the concept of
stochastic degradation and uncertainty reduction in information theory.

Finally, we discuss the dynamics and expected drift of log-likelihood,
entropy, and probability of error as well as their connection to
Kullback-Leibler divergence and mutual information in order to
approximate the optimal value function (i.e. the solutions to the DP).
We then utilize these value function approximations (lower bounds) to
provide simple sequential test strategies (heuristics) whose performance
is numerically compared to the optimal policies. Finally, we prove the
asymptotic optimality of one class of these heuristic test strategies
and, as a special case, recover Burnashev's coding scheme in the context
of variable-length block coding over memoryless channels with feedback.
Time permitting, we will compare and contrast our approach with recent
results in Bayesian active learning literature.

This is joint work with Mohammad Naghshvar and Ofer Shayevitz.

**Speaker Biography**:
Tara Javidi studied electrical engineering at Sharif University of
Technology, Tehran, Iran from 1992 to 1996. She received the MS degrees
in electrical engineering (systems), and in applied mathematics
(stochastics) from the University of Michigan, Ann Arbor, in 1998 and
1999, respectively. She received her Ph.D. in electrical engineering and
computer science from the University of Michigan, Ann Arbor, in 2002.
From 2002 to 2004, she was an assistant professor at the Electrical
Engineering Department, University of Washington, Seattle. She joined
University of California, San Diego, in 2005, where she is currently an
associate professor of electrical and computer engineering.

Tara Javidi ranked first in Iran's national university entrance exam in
1992, was a Barbour Scholar during 1999-2000 academic year, and received
an NSF CAREER Award in 2004. Her research interests are in communication
networks, stochastic resource allocation, stochastic control theory, and
wireless communications.