Rudolf E. Kalman is professor emeritus (ad personam chair) at the Swiss Federal Institute of Technology in Zurich (ETHZ) as well as at the University of Florida (Gainesville, FL). Professor Kalman is a control theorist who is "without a doubt the most influential researcher in the field," (National Academy of Sciences citation). He is known world-wide for his linear filtering technique. The Kalman filter, which revolutionized the field of (real-time) estimation, is widely used in a huge range of applications.

Born in Hungary, Kalman received BS and MS degrees from the Massachusetts Institute of Technology and a DSci in engineering from Columbia University. In the early years of his career he held research positions at IBM and at the Research Institute for Advanced Studies in Baltimore. From 1962 to 1971, he was at Stanford University. In 1971, he became a graduate research professor and director of the Center for Mathematical System Theory at the University of Florida, followed by a special appointment at ETHZ. Kalman's contributions to control theory and to applied mathematics and engineering in general have been widely recognized. He is a recipient of the IEEE Medal of Honor (highest award), the Kyoto Prize (Japanese Nobel prize) and the American Mathematical Society's Steele Prize. He is a member of the French, Hungarian, and Russian Academies of Sciences and of the National Academy of Engineering and the National Academy of Sciences as well as a Fellow of the American Academy of Arts and Sciences.

During the 1960s, Kalman was the leader in the development of a rigorous theory of control systems. Among his many outstanding contributions were the formulation and study of most fundamental state-space notions (including controllability, observability, minimality, realizability from input/output data, matrix Riccati equations, linear-quadratic control, and the separation principle) that are today ubiquitous in control. The paradigms formulated by Professor Kalman have become an intrinsic part of the foundations of control and systems theory and are standard tools. During the 1970s Kalman played a major role in the introduction of algebraic and geometric techniques in the study of linear and nonlinear control systems. His work since the 1980s has focused on a system-theoretic approach to the foundations of statistics, scientific modeling in econometrics and identification of systems from real data, as a natural complement to his earlier studies of minimality and realizability.

TO BE GIVEN

Day 1, 13 September 2006, 9:00 - 10:00

Neil Gordon obtained a BSc in Mathematics and Physics from Nottingham University, UK in 1988 and a PhD in statistics from Imperial College, University of London, 1993.

From 1988 to 2002 he was with various research groups within DERA and QinetiQ working in the areas of missile guidance, target tracking and statistical data processing.

Since August 2002 he has been with the Tracking and Sensor Fusion group at DSTO in Australia. He has been co-editor/co-author of two books on particle filtering.

TO BE GIVEN.

Day 2, 14 September 2006, 9:00 - 10:00

Jun Liu received a BS degree in mathematics in 1985 from Peking University, Beijing, China, and a Ph.D. in statistics in 1991 from the University of Chicago, USA. He is currently Professor of Statistics at Harvard University, with a courtesy Professor appointment at Harvard Biostatistics Department. Before that, he held Assistant, Associate, and full professor positions at Stanford University from 1994 to 2003. Dr. Liu was the recipient of the 2002 COPSS Presidents' Award (given annually by five leading statistical associations to one individual under age 40), the recipient of the Mitchell Prize from the International Society of Bayesian Analysis in 2000, and one of the recipients of the CAREER Award from the National Science Foundation in 1995. He was selected as a Terman Fellow by Stanford University in 1995, as a Medallion Lecturer by the Institute of Mathematical Statistics (IMS) in 2002, and as a Bernoulli Lecturer by the International Bernoulli Society in 2004. He was to Fellow of the Institute of Mathematical Statistics in 2004 and Fellow of the American Statistical Association in 2005.

Dr. Liu's research interests include bioinformatics and computational biology, statistical genetics and genetic epidemiology, stochastic dynamic systems, signal processing and wireless communication, Bayesian modeling, and Monte Carlo methods. His current publication list includes nearly 100 research articles appeared in peer-reviewed journals and conference proceedings, a research monograph, and more than 20 book chapters, discussions, and reports. Dr. Liu is well-known for his theoretical and methodological work in both Markov chain and sequential Monte Carlo methods. He is also responsible for developing several popular algorithms for biological sequence analysis and motif discovery. Dr. Liu has served on numerous editorial boards of leading statistical journals and has frequented grant review panels of the NSF and the NIH.

I will describe some of our recent efforts in the development of Monte Carlo strategies (both MCMC and SMC) for simulating and optimizing molecular structures. I will illustrate these ideas using examples from Hydrophobic-Hydrophilic (HP) protein model (both 2-D and 3-D) optimization and near-native structure (NNS) simulations.

By applying the new SMC and MCMC schemes, we were able to achieve the best results for all the 2-D and 3-D HP structural optimization examples we can find in the literature. In particular, the new approach achieved better results for these HP models than a modified PERM algorithm and the equi-energy Sampler (Kou et al. 2006). For the NNS problem, we can characterize accurately many important ensemble properties of NNS, including the size of NNS set, the probability of randomly sampling one NNS structure, and the occurrence of native contacts in NNS. We also found that widely used pairwise potential functions behaved surprisingly badly for stabilizing near native protein structures. Based on the joint work with Junni Zhang, Jinfeng Zhang, and Sam Kou.

Day 3, 15 September 2006, 9:00 - 10:00