[Ml-stat-talks] mike jordan at at&t on october 5

David Blei blei at CS.Princeton.EDU
Thu Sep 15 18:22:28 EDT 2011


hi ml-stat-talks

mike jordan is speaking at AT&T in october. i've been asked to pass along the announcement. for those of you who haven't seen mike speak, he is worth the trip.

best
dave


> Subject: AT&T Labs Research - Distinguished Speaker Seminar: October 5, 2011 - Michael I. Jordan
> 
> -------------------------------------------------------------------------------------------------------- Announcement 
>  AT&T Labs Research - DISTINGUISHED SPEAKER SEMINAR 
> 
> Title: Completely Random Measures for Bayesian Nonparametrics 
> 
> Speaker: Michael I. Jordan 
> 
> Affiliation: Department of Electrical Engineering and Computer Science
>  Department of Statistics
> University of California, Berkeley
> 
> 
> 
> Time and Place: Wednesday, October 5, 2011 3:30-4:30pm EDT
> 
> FP C050 (Florham Park Auditorium)
>  Research Website: http://www.research.att.com/talks_and_events/2011_distinguished_speaker/m_jordan/m_jordan 
> 
> 
> Directions: http://www.research.att.com/evergreen/about_us/fp_directions.html 
> 
>  Wine and Cheese Reception: 4:30-5:30pm at Florham Park Cafeteria
> 
> ABSTRACT
> 
> Computer Science has historically been strong on data structures and weak on inference from data, whereas Statistics has historically been weak on data structures and strong on inference from data. One way to draw on the strengths of both disciplines is to pursue the study of “inferential methods for data structures”; i.e., methods that update probability distributions on recursively-defined objects such as trees, graphs, grammars and function calls. This is accommodated in the world of “Bayesian nonparametrics”, where prior and posterior distributions are allowed to be general stochastic processes. Both statistical and computational considerations lead one to certain classes of stochastic processes, and these tend to have interesting connections to combinatorics. I will focus on Bayesian nonparametric modeling based on completely random measures, giving examples of how recursions based on these measures lead to useful models in several applied problem domains, including protein structural modeling, natural language processing, computational vision and speech recognition.
> 
> 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. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research in recent years has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, variational methods, kernel machines and applications to problems in statistical genetics, signal processing, computational biology, information retrieval and natural language processing. 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 is a Fellow of the ACM, the IMS, the IEEE, the AAAI and the ASA.




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