Seminars/Colloquia/Invited Talks

Seminars

Liam Paninski

Three Statistical Problems from Neural Data Analysis

PLACE:	Clark 110 (Video broadcast in Traylor 709)
EVENT:	CIS Seminar Series
DATE:	August 30, 2005
TIME:	1:00 - 2:00

Abstract

Neuroscience has recently proven to be a rich source of interesting statistical problems. This talk will address three such problems (with relative emphasis depending on audience interest):

1) Nonparametric estimation of information-theoretic quantities (especially the Shannon entropy and mutual information) from sparsely-sampled data.

2) Optimal, adaptive experimental design (how do we select stimuli online to learn the most about the brain in the least amount of time).

3) Minimax theory for estimating sparsely-sampled discrete distributions under Kullback-Leibler loss.

Brief biography

Liam Paninski received the B.S. degree in Neuroscience from Brown University in 1999 and his Ph.D. on Neural Science from the New York University in 2003. Currently he is an Assistant Professor at the University of Columbia. He also worked as Senior Research Fellow at the Gatsby Computational Neuroscience Unit of the University College London.