Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

   

Joint Colloquium Distinguished Lecture Series

Biology as Computation

photo of Leslie Valiant Wednesday, October 25th
306 Soda Hall
4:00 - 5:00 pm

Leslie Valiant
T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics, Harvard University

Abstract:

We argue that computational models have an essential role in uncovering the principles behind a variety of biological phenomena. In particular we consider recent results relating to the following three questions: How can brains, given their known resource constraints such as the sparsity of connections and slow elements, do any significant information processing at all? How can evolution, in only a few billion years, bring about such complex mechanisms as it has? How can cognitive systems manipulate large amounts of such uncertain knowledge and get usefully reliable results? We show that each of these problems can be formulated as a quantitative question for a computational model, and argue that solutions to these formulations provide some understanding of these biological phenomena.

Biography:

Leslie Valiant was educated at King's College, Cambridge, Imperial College London, and Warwick University where he received his Ph.D. in computer science in 1974. He started teaching at Harvard University in 1982 and is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the Division of Engineering and Applied Sciences. Prior to 1982 he taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh.

Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of Sharp-P-completeness to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow. He also works in computational neuroscience focusing on understanding memory and learning.


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