Jin Tian
Assistant Professor

Office: 107-1 Atanasoff Phone: (515) 294-8433 Fax: (515) 294-0258 Email: jtian@cs.iastate.edu Homepage: http://www.cs.iastate.edu/~jtian/

Research Interests

Bayesian networks, probabilistic reasoning, causal reasoning and learning

Research Areas

Artificial Intelligence, Intelligent Agents and Multiagent Systems, Machine Learning and Data Mining

Education

Ph.D.   Computer Science, UCLA   2002
M.S.   Physics, UCLA   1997
B.S.   Physics, Tsinghua University, P. R. China   1992

Honors and Awards

Faculty Early Career Development Award  National Science Foundation, 2004

Current Grants

CAREER: Reasoning with Cause and Effect: Model Testing, Axiomatization, and Identification. Jin Tian. NSF (2004-2009). $455,452.

Representative Publications

Refereed Journal and Conference Publications

J. Tian. Identifying Dynamic Sequential Plans. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki, Finland, AUAI Press. pp. 554-561, 2008.

C. Kang and J. Tian. Inequality Constraints in Causal Models with Hidden Variables. Conference on Uncertainty in Artificial Intelligence (UAI), Cambridge, Massachusetts, AUAI Press. pp. 233-240, 2006.

J. Tian. Identifying Direct Causal Effects in Linear Models. the National Conference on Artificial Intelligence (AAAI), Pittsburgh, Pennsylvania, AAAI Press. pp. 346-352, 2005.

J. Tian. Identifying Linear Causal Effects. Proceedings of the National Conference on Artificial Intelligence (AAAI), San Jose, California, AAAI Press. pp. 104-110, 2004.

J. Tian and J. Pearl. On the Testable Implications of Causal Models with Hidden Variables. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Edmonton, Alberta, Canada., Morgan Kaufmann Publishers. pp. 519-527, 2002.

J. Tian and J. Pearl. A general identification condition for causal effects. Proceedings of the National Conference on Artificial Intelligence (AAAI), Edmonton, Alberta, Canada, AAAI Press. pp. 567-573, 2002.

J. Tian and J. Pearl. Probabilities of causation: bounds and identification. Annals of Mathematics and Artificial Intelligence. Vol. 28. pp. 287-313, 2000.