Computational research transforms the sciences (physical, mathematical, life or social) not just by empowering them analytically, but mainly by providing a novel and powerful perspective which often leads to unforeseen insights. Examples abound: quantum computation provides the right forum for questioning and testing some of the most basic tenets of quantum physITCS, while statistical mechanITCS has found in the efficiency of randomized algorithms a powerful metaphor for phase transitions. In Mathematics, the P vs. NP problem has joined the list of the most profound and consequential problems, and in economITCS considerations of computational complexity revise predictions of economic behavior and affect the design of economic mechanisms such as auctions. Finally, in biology some of the most fundamental problems, such as understanding the brain and evolution, can be productively recast in computational terms

Biography

I studied in Athens Polytechnic (BS in EE 1972) and Princeton (MS in EE, 1974 and PhD in EECS, 1976). Since then, I have taught at Harvard, MIT, Athens Polytechnic, Stanford, and UCSD. I came to Berkeley in January 1996 (but I was here also in 1978 as a Miller fellow). I am interested in the theory of algorithms and complexity, and its applications to databases, optimization, AI, networks, and game theory.

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