Dr. Shankar SASTRY
Dean of Engineering
University of California, Berkeley
美国加州大学伯克利分校工程学院院长
http://robotics.eecs.berkeley.edu/~sastry/

BIO:
S. Shankar Sastry is currently the Dean of Enginnering at University of California, Berkeley. From 2004 to 2007 he was the Director of CITRIS (Center for Information Technology in the Interests of Society) an interdisciplinary center spanning UC Berkeley, Davis, Merced and Santa Cruz. In Februrary 2007, he was appointed the faculty co-director of the Blum Center for Developing Economies. He has served as Chairman, Department of Electrical Engineering and Computer Sciences University of California, Berkeley from January, 2001 through June 2004. From 1999-early 2001, he served as Director of the Information Technology Office at DARPA. From 1996-1999, he was the Director of the Electronics Research Laboratory at Berkeley.

Dr. Sastry received his Ph.D. degree in 1981 from the University of California, Berkeley. He was on the faculty of M.I.T. as Asst. Professor from 1980-82 and Harvard University as a chaired Gordon Mc Kay professor in 1994. His areas of personal research are embedded and autonomous software for unmanned systems (especially aerial vehicles), computer vision, computation in novel substrates such as quantum computing, nonlinear and adaptive control, robotic telesurgery, control of hybrid and embedded systems, network embedded systems and software. Most recently he has been concerned with cybersecurity and critical infrastructure protection, and has helped establish an NSF Science and Technology Center, TRUST (Team for Research in Ubiquitous Secure Technologies)

He has coauthored over 350 technical papers and 9 books, including Adaptive Control: Stability, Convergence and Robustness (with M. Bodson, Prentice Hall, 1989) and A Mathematical Introduction to Robotic Manipulation (with R. Murray and Z. Li, CRC Press, 1994), Nonlinear Systems: Analysis, Stability and Control (Springer-Verlag, 1999), and An Invitation to 3D Vision: From Images to Models (Springer Verlag, 2003) (with Y. Ma. S. Soatto, and J. Kosecka). Dr. Sastry served as Associate Editor for numerous publications, including: IEEE Transactions on Automatic Control; IEEE Control Magazine; IEEE Transactions on Circuits and Systems; the Journal of Mathematical Systems, Estimation and Control; IMA Journal of Control and Information; the International Journal of Adaptive Control and Signal Processing; Journal of Biomimetic Systems and Materials. He is currently an Associate Editor of the IEEE Proceedings.

Dr. Sastry was elected into the National Academy of Engineering in 2001 and the American Academy of Arts and Sciences (AAAS) in 2004. He also received the President of India Gold Medal in 1977, the IBM Faculty Development award for 1983-1985, the NSF Presidential Young Investigator Award in 1985 and the Eckman Award of the of the American Automatic Control Council in 1990, the Ragazzini Award for Distinguished Accomplishments in teaching in 2005, an M.A. (honoris causa) from Harvard in 1994, Fellow of the IEEE in 1994, the distinguished Alumnus Award of the Indian Institute of Technology in 1999, and the David Marr prize for the best paper at the International Conference in Computer Vision in 1999.

He has supervised over 50 doctoral students to completion and over 50 M.S. students. His students now occupy leadership roles in several locations and on the faculties of many major universities in the United States and abroad.

Presentation Title: Generalized Principal Component Analysis: An Introduction

Abstract:
There are a large number of problems in which we encounter the problem of modeling large amounts of data, by what is referred to as a “mixture of models”, that is to say that the data can be segmented into finitely many sub components, each of which can be separately modeled. In the context of the identification of hybrid systems it is easy to see how this would arise when the input-output behavior depends on the “discrete state” of the hybrid system. Of course, the applications in computer vision, signal and image processing and indeed more generally in statistics are extremely numerous. This area of work has found a tremendous outpouring of effort and methods in recent years in the signal processing, hybrid systems, statistics and learning systems literature. However, it is our perception that the conceptual and theoretical underpinnings of the bulk of the literature are weak.

In the course of a recent set of papers with Yi Ma of the University of Illinois, Urbana Champaign and Rene Vidal of Johns Hopkins University and their students, we have developed what we believe to be an interesting new approach to simultaneously segmenting and modeling data from mixtures of models. The heart of our approach lies in what is called “Generalized Principal Component Analysis”. This in turn has many connections with such classical problems as Hilbert’s Nullstellensatz and many unsolved problems in statistics. In my talk at this workshop, I will give a brief overview of the approaches and their applications to date. The work is being incorporated into a monograph to appear in 2008 and a preview of this monograph is available at: http://black.csl.uiuc.edu/~yima/psfile/book-VMS.pdf.

The website for the code for GPCA is http://perception.csl.uiuc.edu/gpca/