Michael Jordan

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, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence 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.

Professor Michael Jordan specializes in artificial intelligence, biosystems and computational biology, control, intelligence systems and robotics, signal processing, and theory. 

• 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.