加州大学伯克利分校电气工程与计算机科学系导师教师师资介绍简介-Michael Jordan

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Michael Jordan

Professor

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Research Areas

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

Research Centers

CITRIS People and Robots (CPAR)
Simons Institute for the Theory of Computing (SITC)
Center for Computational Biology (CCB)
Berkeley Artificial Intelligence Research Lab (BAIR)


Biography

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitiveand biological sciences, and have focused in recent years on Bayesiannonparametric analysis, probabilistic graphical models, spectralmethods, kernel machines and applications to problems in distributed computingsystems, natural language processing, signal processing and statisticalgenetics. Prof. Jordan is a member of the National Academyof Sciences, a member of the National Academy of Engineering and amember of the American Academy of Arts and Sciences. He is aFellow of the American Association for the Advancement of Science.He has been named a Neyman Lecturer and a Medallion Lecturer by theInstitute of Mathematical Statistics. He received the IJCAI ResearchExcellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

Education

1985, Ph.D., Cognitive Science, UC San Diego
1980, M.S., Mathematics, Arizona State University
1978, B.S., Psychology, Louisiana State University

Selected Publications

J. D. Lee, M. Jordan, B. Recht, and M. Simchowitz, "Gradient Descent Only Converges to Minimizers," in Proceedings of the 29th Conference on Learning Theory, {COLT} 2016, New York, USA, June 23-26, 2016, 2016, pp. 1246--1257.
X. Pan, M. Lam, S. Tu, D. Papailiopoulos, C. Zhang, M. Jordan, K. Ramchandran, C. Re, and B. Recht, "Cyclades: Conflict-free Asynchronous Machine Learning," in Advances in Neural Information Processing Systems 29, 2016.
X. Pan, D. Papailiopoulos, S. Omyak, B. Recht, K. Ramchandran, and M. Jordan, "Parallel correlation clustering on big graphs," in Advances in Neural Information Processing Systems 28, 2015, pp. 82--90.
X. Pan, S. Jegelka, J. E. Gonzalez, J. K. Bradley, and M. Jordan, "Parallel Double Greedy Submodular Maximization," in Advances in Neural Information Processing Systems 27, 2014.
X. Pan, J. E. Gonzalez, S. Jegelka, T. Broderick, and M. Jordan, "Optimistic concurrency control for distributed unsupervised learning," in Advances in Neural Information Processing Systems 26, 2013, pp. 1403--1411.
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.

Awards, Memberships and Fellowships

IEEE John von Neumann Medal, 2020
Yale University Honorary Doctorate, 2020
IJCAI Award for Research Excellence, 2016
David E. Rumelhart Prize, 2015
International Society for Bayesian Analysis (ISBA) Fellow, 2014
Society for Industrial & Applied Mathematics (SIAM) Fellow, 2012
American Association for the Advancement of Science (AAAS) Fellow, 2011
Association for Computing Machinery (ACM) Fellow, 2010
American Academy of Arts and Sciences Member, 2010
National Academy of Engineering (NAE) Member, 2010
National Academy of Sciences (NAS) Member, 2010
Cognitive Science Society (CSS) Fellow, 2010
ACM-AAI Allen Newell Award, 2009
SIAM Activity Group Optimization Prize, 2008
American Statistical Association (ASA) Fellow, 2007
CIS Neural Networks Pioneer Award, 2007
Diane S. McEntyre Award for Excellence in Teaching Computer Science, 2006
Institute of Electrical & Electronics Engineers (IEEE) Fellow, 2005
Institute of Mathematical Statistics (IMS) Fellow, 2005
Association for the Advancement of Artificial Intelligence (AAAI) Fellow, 2002
NSF Presidential Young Investigator (PYI), 1991