Anouncement of plenary lectures

• Marco Claudio CAMPI, is Professor of Automatic Control at the University of Brescia, Italy.

  In 1988, he received the doctor degree in electronic engineering from the Politecnico di Milano, Milano, Italy. From 1988 to 1989, he was a Research Assistant at the Department of Electrical Engineering of the Politecnico di Milano. From 1989 to 1992, he worked as a Researcher at the Centro di Teoria dei Sistemi of the National Research Council (CNR) in Milano and, in 1992, he joined the University of Brescia, Brescia, Italy. He has held visiting and teaching positions at many universities and institutions including the Australian National University, Canberra, Australia; the University of Illinois at Urbana-Champaign, USA; the Centre for Artificial Intelligence and Robotics, Bangalore, India; the University of Melbourne, Australia; the Kyoto University, Japan.

   

  Prof. Campi is an Associate Editor of Systems and Control Letters, and a past Associate Editor of Automatica and the European Journal of Control. From 2002 to 2008, he served as Chair of the Technical Committee IFAC on Stochastic Systems (SS) and he is currently vice-chair for theTechnical Committee IFAC on Modeling, Identification, and Signal Processing (MISP). Moreover, he has been a distinguished lecturer of the Control Systems Society. Marco Campi's doctoral thesis was awarded the "Giorgio Quazza" prize as the best thesis for year 1988. In 2008, he received the IEEE CSS George S. Axelby outstanding paper award for the article "The Scenario Approach to Robust Control Design", co-authored with G. Calafiore.

   

  The research interests of Marco Campi include: system identification, learning and classification, adaptive and data-based control, randomized methods, robust convex optimization.

   

• Carl Rasmussen, University of Cambridge, UK
  
  Carl Edward Rasmussen received the MSc in Electronics Engineering in 1993 from the Technical University of Denmark, and the PhD in Computer Science from University of Toronto, 1996. Since then he has been a Senior Research Fellow at the Gatsby Computational Neuroscience Unit at University College London, and Junior Research group Leader at

  the Max Planck Institute of Biological Cybernetics, Tübingen,Germany. He is currently Reader in Information Engineering at theDepartment of Engineering at the University of Cambridge.

   

  Carl has broad interests in Bayesian inference in machine learning, including supervised, unsupervised and reinforcement learning, and has contributed strongly in the area of non-parametric models. He is co-author of Rasmussen and Williams "Gaussian Processes for Machine Learning", winner of the 2009 DeGroot prize.

  • Title lecture: Machine Learning, Probabilistic Inference, System Identification and Control
    
     

• Daniel E. Rivera, Arizona State University Tempe, Arizona, USA
  
  Daniel E. Rivera received the B.S. degree in chemical engineering from the University of Rochester, New York in 1982, the M.S. degree in chemical engineering from the University of Wisconsin-Madison in 1984, and the Ph.D in chemical engineering from the California Institute of Technology, Pasadena, California in 1987.

   

  He is professor of chemical engineering in the School for Engineering of Matter, Transport, and Energy at Arizona State University in Tempe, Arizona. Prior to joining ASU he was a member of the Control Systems Section of Shell Development Company in Houston, Texas. His research interests span the topics of system identification, robust process control, and applications of control engineering to problems in supply chain management and behavioral health.

   

  Daniel is chair of the IEEE-CSS technical committee on system identification and adaptive control, and has served as an associate editor for the IEEE Control Systems Magazine (2003-2007) and IEEE Transactions in Control Systems Technology (2003 – 2011). He is the recipient of the 1994-1995 Outstanding Undergraduate Educator Award by the ASU student chapter of AIChE and the 1997-1998 Teaching Excellence Award from the ASU College of Engineering and Applied Sciences. In 2007 he was awarded a K25 Mentored Quantitative Research Career Development Award from the National Institutes of Health (US) to study how control engineering approaches can be used for improving interventions for the prevention and treatment of drug abuse.
  

  • Title lecture: Optimized behavioral interventions: what does system identification and control engineering have to offer?        

     

•   Maarten Steinbuch, (1960) received the MSc degree and PhD from Delft University of Technology, Delft, The Netherlands, in 1984 and 1989 resp. From 1987-1998 he was with Philips Research Labs., Eindhoven as a Member of the Scientific Staff. From 1998-1999 he was manager of the Dynamics and Control group at Philips Center for Manufacturing Technology. Since 1999 he is full professor in Systems and Control, and head of the Control Systems Technology group of the Mechanical Engineering Department of Eindhoven University of Technology. He was an Associate Editor of the IEEE Transactions on Control Systems Technology, of IFAC Control Engineering Practice, and of IEEE Control Systems Magazine. He was Editor-at-Large of the European Journal of Control. Currently, he is Editor-in-Chief of IFAC Mechatronics and Associate Editor of Int. Journal of Powertrains. He is programleader of the TU/e Master of Science Automotive Technology, member of the dutch Formule E team, Chairman of the Stichting Techniekpromotie, and co-founder of MI-Partners. Since july 2006 he is also Scientific Director of the Centre of Competence High Tech Systems of the Federation of Dutch Technical Universities. In 2003, 2005 and 2008 he obtained the 'Best-Teacher' award of the Department of Mechanical Engineering, TU/e. His research interests are modelling, design and control of motion systems, robotics, automotive powertrains and control of fusion plasmas. 

  • Title lecture: Advanced Control of High Tech Systems
    

• Mario Sznaier, Dr. Mario Sznaier is currently the Dennis Picard Chaired Professor at the Electrical and Computer Engineering Department, Northeastern University. Prior to joining Northeastern University, Dr. Sznaier was a Professor of Electrical Engineering at the Pennsylvania State University and also held visiting positions at the California Institute of Technology. His research interest include robust identification and control of hybrid systems, robust optimization, and dynamical vision. Dr. Sznaier is currently serving as an associate editor for the journal Automatica and as a member of the Board of Governors of the IEEE Control Systems Society. Additional recent service includes CSS Executive Director (2007-2011), Program Chair of the 2009 IFAC Symposium on RobustControl Design, and Program vice-chair of the 2008 IEEE Conf. on Decision and Control. Dr. Sznaier was a plenary speaker at the 2009 and 2010 Int. Conference on the Dynamics of Information Systems,and will deliver a plenary lectures at the 2011 IFAC on Robust Control Design. A list of publications and currently funded publications can be found at http://robustsystems.ece.neu.edu.

  • Title lecture: Compressive Information Extraction: A Dynamical Systems Approach

  • Abstract: This talk addresses the problem of extracting actionable information that is very sparsely encoded in high dimensional data streams. The central theme of our approach is the realization that actionable information can be often represented with a small number of invariants associated with an underlying dynamical system. Thus, in this context, the problem of actionable information extraction can be reformulated as identifying these invariants from (high dimensional) noisy data, and thought of as a generalization of sparse signal recovery problems to a dynamical systems framework. While in principle this approach leads to generically nonconvex, hard to solve problems, computationally tractable relaxations (and in some cases exact solutions) can be obtained by exploiting a combination of elements from convex analysis and the classical theory of moments. In the second part of the talk we will illustrate the application of these results to some challenging problems such as video, image and genomic data segmentation, where the goal is to detect changes, for instance in scenes, activities, texture, or gene promoter expressions. We will conclude the talk by exploring the connection between hybrid systems identification, information extraction, and machine learning, and discuss new research directions in identification and systems theory motivated by these problems.
    
    
• Alan S. Willsky is the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science and Director of the Laboratory for Information and Decision Systems at MIT. Dr. Willsky was a founder, member of the Board of Directors, and Chief Scientific Consultant of Alphatech, Inc. Dr. Willsky has held visiting positions at several institutions in England and France. He has authored more than 200 journal papers and 350 conference papers, as well as two books, including the widely used undergraduate text Signals and Systems. Prof. Willsky has received numerous awards, including the 1975 American Automatic Control Council Donald P. Eckman Award, the 1980 IEEE Browder J. Thompson Memorial Award, the 2004 IEEE Donald G. Fink Prize Paper Award, and an honorary doctorate from Université de Rennes. Prof. Willsky recently received the 2009 Technical Achievement Award from the IEEE Signal Processing Society and in 2010 was elected to the National Academy of Engineering.

   

  Prof. Willsky is the leader of MIT’s Stochastic Systems Group (http://ssg.mit.edu). His early work on methods for failure detection in dynamic systems is still widely cited and used in practice, and his more recent research on multiresolution methods for large-scale data fusion and assimilation has found application in fields including target tracking, object recognition, oil exploration, oceanographic remote sensing, and groundwater hydrology. Dr. Willsky’s present research interests are in problems involving multidimensional and multiresolution estimation and imaging, inference algorithms for graphical and relational models, statistical image and signal processing, data fusion and estimation for complex systems, image reconstruction, discovery of models for complex interacting phenomena, and computer vision

   

  • Title lecture: Learning and Inference for Graphical and Hierarchical Models: A Personal Journey