Michael Jordan

Professor

Research Areas

• Artificial Intelligence (AI)
• Biosystems & Computational Biology (BIO)
• Control, Intelligent Systems, and Robotics (CIR)
• Signal Processing (SP)
• Statistical Machine Learning

Research Centers

• Center for Intelligent Systems (CIS)
• Reliable, Adaptive and Distributed systems Laboratory (RAD Lab)

Teaching Schedule (Spring 2009)

• CS 294-38. Bayesian Modeling and Inference, MW 10:30A-12:00P, 330 Evans

Biography

Michael Jordan is Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters from Arizona State University, and earned his PhD in 1985 from the University of California, San Diego. He was a professor at the Massachusetts Institute of Technology from 1988 to 1998. He has published over 300 research articles on topics in computer science, statistics, electrical engineering, molecular biology and cognitive science. His research in recent years has focused on probabilistic graphical models, kernel machines, nonparametric Bayesian methods and applications to problems in information retrieval, signal processing and bioinformatics. Prof. Jordan was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2006. He is a Fellow of the IMS, a Fellow of the IEEE and a Fellow of the AAAI.

Selected Publications

• E. P. Xing, M. Jordan, and R. Sharan, "Bayesian haplotype inference via the Dirichlet process," Journal of Computational Biology, vol. 14, pp. 267-284, June 2007.
• B. Taskar, S. Lacoste Julien, and M. Jordan, "Structured prediction, dual extragradient and Bregman projections," J. Machine Learning Research, vol. 7, pp. 1627-1653, Dec. 2006.
• F. R. Bach and M. Jordan, "Learning spectral clustering, with application to speech separation," J. Machine Learning Research, vol. 7, pp. 1963-2001, Dec. 2006.
• Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," J. American Statistical Association, vol. 101, no. 476, pp. 1566-1581, Dec. 2006.
• M. Jordan, "Graphical models," Statistical Science: Special Issue on Bayesian Statistics, vol. 19, no. 1, pp. 140-155, Feb. 2004.
• D. M. Blei, A. Y. Ng, and M. Jordan, "Latent Dirichlet allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, Jan. 2003.
• M. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, "An introduction to variational methods for graphical models," Machine Learning, vol. 37, no. 2, pp. 183-233, Nov. 1999.
• D. Wolpert, Z. Ghahramani, and M. Jordan, "An internal forward model for sensorimotor integration," Science, vol. 269, pp. 1880-1882, Sep. 1995.
• M. Jordan and R. A. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Computation, vol. 6, no. 2, pp. 181-214, March 1994.