Michael I. Jordan

For other people named Michael Jordan, see Michael Jordan (disambiguation).

Michael I. Jordan is a leading researcher in machine learning and artificial intelligence.^[1][2][3] Jordan was a prime mover behind popularising Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics. Jordan was also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation-maximization algorithm in machine learning.

Jordan was a student of David E. Rumelhart and a member of the PDP Group at the University of California, San Diego in the 1980s. During this time he developed recurrent neural networks as a cognitive model. In recent years, though, his work is less driven from a cognitive perspective and more from the background of traditional statistics.

Jordan is currently a full professor at the University of California, Berkeley where his appointment is split across the Department of Statistics and the Department of EECS.

Jordan received numerous awards, including a best paper award (with X. Nguyen and M. Wainwright) at the International Conference on Machine Learning (ICML 2004), a best paper award (with R. Jacobs) at the American Control Conference (ACC 1991), the ACM/AAAI Allen Newell Award, the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. In 2010 he was named a Fellow of the Association for Computing Machinery "for contributions to the theory and application of machine learning."^[4]

It is notable that many of Jordan's graduate students and postdocs continue to strongly influence the machine learning field after their PhDs. Zoubin Ghahramani, Tommi Jaakkola, Andrew Ng, Lawrence Saul and David Blei (all former students or postdocs of Jordan) have all continued to make significant contributions to the field.

References

1. ^ Jacobs, R.A.; Jordan, M.I.; Nowlan, S.J.; Hinton, G.E. (1991). "Adaptive Mixtures of Local Experts". Neural Computation 3 (1): 79–87. doi:10.1162/neco.1991.3.1.79. 
2. ^ David M. Blei, Andrew Y. Ng, Michael I. Jordan. Latent Dirichlet allocation. The Journal of Machine Learning Research, Volume 3, 3/1/2003
3. ^ Michael I. Jordan, ed. Learning in Graphical Models. Proceedings of the NATO Advanced Study Institute, Ettore Maiorana Centre, Erice, Italy, September 27-October 7, 1996
4. ^ http://www.acm.org/press-room/news-releases/2010/fellows-2010

External links

• Homepage (at University of California, Berkeley)
• Published papers (chronological)

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