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2004 World Technology Awards Winners & Finalists
Please describe the work that you are doing that you consider to be the most innovative and of the greatest likely long-term significance.
My main research focus is on dealing with complex domains that involve large amounts of uncertainty. My work builds on the framework of probability theory, decision theory, and game theory, but uses techniques from artificial intelligence and computer science to allow us to apply this framework to complex real-world problems.
Most of my work is based on the use of probabilistic graphical models such as Bayesian networks, influence diagrams, and Markov decision processes. Within that topic, my work touches on many areas: representation, inference, learning, and decision making. One main focus has been the extension of the representational power of the probabilistic graphical modeling language, to encompass a much richer set of domains. For example, work in my group includes:
incorporating hierarchical and object-relational structure in our object-oriented Bayesian networks (OOBNs) and probabilistic relational models (PRMs); extensions to temporal domains using dynamic Bayesian networks; hybrid Bayesian networks involving both discrete and continuous variables; factored MDPs that represent sequential decision problems in a factored way; structured representations for utility functions; multi-agent influence diagrams for representing multi-agent decision problems with incomplete information; and more.
I believe that a good representation must also support effective inference and learning algorithms. Hence, the work done in my group is also highly focused on these topics. We have worked on exact and approximate inference algorithms for these representations, and on approaches for learning these models from data. On the inference side, we have done a lot of work on inference in dynamic Bayesian networks, inference in hybrid Bayesian networks, decision making in factored MDPs, and inference for large scale models such as those generated by a PRM or an OOBN. On the learning side, we have done a lot of work on learning probabilistic models from relational databases, on active learning of probabilistic models (where the learner can query for particular types of instances), and on learning utility functions from data.
Our work spans the range from concepts to theory to applications. Some of our work is conceptual: defining new representation schemes and exploring their expressive power. Some of it is theoretical and algorithmic: designing new inference and learning algorithms and proving that they achieve certain properties. And some is applied: experimenting with our approaches on both synthetic and real problems. Some of the applications that we are particularly interested in right now are: learning models from rich heterogenous biomedical databases, which can include clinical, genomic, genetic, and epidemiological data; fault diagnosis for complex hybrid systems; and tracking at the symbolic level from low-level visual data.
I am an associate professor at the Computer Science Department at Stanford University. I joined the department in September 1995. I am primarily affiliated with the Robotics Laboratory, but am also in contact with the Theory Group.
Returning to Stanford was a homecoming for me, since in 1993 I completed my PhD at Stanford under the supervision of Joe Halpern. In between, I was a postdoctoral researcher with Stuart Russell's research group at the Computer Science Division at UC Berkeley.
I did my masters and undergraduate degrees at the Hebrew University of Jerusalem, Israel. My bachelors was in Mathematics and Computer Science, and my masters degree in Computer Science.
Here is a slightly more formal bio:
September 2001 -- present: Associate Professor, Department of Computer Science, Stanford University September 1995 -- September 2001: Assistant Professor, Department of Computer Science, Stanford University October 1993 -- August 1995: Postdoctoral Researcher, Computer Science Division, University of California, Berkeley with Professor Stuart J. Russell Ph.D Computer Science, Stanford University 1989-1993 Dissertation: From Knowledge to Belief Advisor: Professor Joseph Y. Halpern
M.Sc. Computer Science, summa cum laude Hebrew University of Jerusalem 1985-1986 Dissertation: Token Survival -- Resilient Token Algorithms Advisor: Professor Danny Dolev
B.Sc. Mathematics & Computer Science, cum laude Hebrew University of Jerusalem 1982-1985
Awards and Honors
IJCAI 2001 Computers and Thought Award. If you want to see a version of my talk (without the animations) click here. (I'm sorry to say that some of the slides only show up well on advanced versions of Internet Explorer.) Presidential Early Career Award for Scientists and Engineers (PECASE), 1999. (I got to visit the White House when I got this award :-) Office of Naval Research Young Investigator Award, 1999. Sloan Foundation Research Fellowship, 1996 Arthur L. Samuel Award for best thesis in the Computer Science Department, Stanford University, 1994 University of California President's Postdoctoral Fellowship, 1993--1995 Rothschild Graduate Fellowship, 1989-1990
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