ECC brings Europe's systems control experts together to share their work through presentations and workshops.
Plenary Speakers |
Semi-Plenary Speakers |
Tutorial Organizers |
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Glenn Vinnicombe |
Colin Jones Daniel Kuhn Ian Hiskens Alexandre Bayen Henrik Sandberg Heinz Koeppl |
Plenary Speakers |
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Dimitris Bertsimas is currently the Boeing Leaders for Global Operations Professor of Management and the co-director of the Operations Research Center at the Massachusetts Institute of Technology. He has received a BS in Electrical Engineering and Computer Science at the National Technical University of Athens, Greece in 1985, a MS in Operations Research at MIT in 1987, and a Ph.D. in Applied Mathematics and Operations Research at MIT in 1988. Since 1988, he has been in the MIT faculty. His research interests include optimization, stochastic systems, data mining, and their applications. |
Dimitris Bertsimas:A computationally tractable theory of performance analysis in stochastic systemsWatch Video We propose a new approach to analyze stochastic systems based on robust optimization. The key idea is to replace the Kolmogorov axioms as primitives of probability theory, with some of the asymptotic implications of probability theory: the central limit theorem and law of large numbers and to define appropriate robust optimization problems to perform performance analysis. In this way, the performance analysis questions become highly structured optimization problems (linear, conic, mixed integer) for which there exist efficient, practical algorithms that are capable of solving truly large scale systems. We demonstrate that the proposed approach achieves computationally tractable methods for (a) analyzing multiclass queueing networks, (b) characterizing the capacity region of network information theory and associated coding and decoding methods generalizing the work of Shannon, (c) pricing multi-dimensional financial contracts generalizing the work of Black, Scholes and Merton, (d) designing multi-item, multi-bidder auctions generalizing the work of Myerson. This is joint work with my doctoral student at MIT Chaithanya Bandi. |
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Tariq Samad is Corporate Fellow at Honeywell Automation and Control Solutions, in Minneapolis, U.S.A. During his 27 years with Honeywell he has contributed to and led automation and control developments for applications in electric power systems, the process industries, building management, automotive engines, unmanned aircraft, and clean energy. His research interests relate broadly to automation, intelligence, and autonomy for complex engineering systems. |
Tariq Samad:Control for smart grids: Applications and opportunities in the customer domain
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Ron Weiss Ron Weiss is an Associate Professor in the Department of Biological Engineering and the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. Weiss received his PhD from MIT in 2001 and currently serves as the Director of MIT's newly formed Synthetic Biology Center. His research focuses on synthetic biology, where he programs cell behavior by modeling and experimentally constructing artificial cellular pathways. A major thrust of his work is the synthesis of gene networks that are engineered to perform in vivo analog and digital logic computation. He is also interested in programming cell aggregates to perform coordinated tasks using engineered cell-cell communication. He has constructed and tested several novel in vivo biochemical logic circuits and intercellular communication systems in bacteria and yeast. More recently, the Weiss lab has focused on mammalian synthetic biology and several therapeutic application areas including programmed tissue engineering, diabetes, and cancer. |
Ron Weiss:Synthetic biology: From parts to modules to therapeutic systems Watch Video |
Semi-Plenary Speakers |
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Duncan Callaway |
Duncan Callaway:Demand-side modeling, estimation and control in electric power systems
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Marco Campi |
Marco Campi:The scenario approach to stochastic optimizationMany design problems in control, identification and signal processing can be expressed as optimization problems. In many cases, the environment in which the optimization is performed contains uncertain elements, and the designer acquires knowledge about uncertainty through experience, that is, by looking at previous cases, or “scenarios”, of the same problem. This is the set-up in which the scenario approach operates. The scenario approach has a practical appeal due to its simplicity. On the other hand, it is also a mathematically solid method whose justification is grounded on a rigorous generalization theory. In the talk, we shall introduce the scenario approach, and explore some of its potentials and properties. |
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Hartmut Geyer |
Hartmut Geyer:
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Holger Hermanns |
Holger Hermanns:
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Damon Vander Lind |
Damon Vander Lind:
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Glenn Vinnicombe Glenn Vinnicombe graduated with a BA in Engineering from Cambridge in 1984. From 1984 to 1987 he was with British Aerospace, working primarily on the design and flight test of control systems on the Airbus A320. He returned to Cambridge in 1987 as a College Lecturer at Churchill College, obtaining the PhD degree in 1993. He has held faculty positions at the Department of Mechanical Engineering, University of Minnesota, and in the Department of Aeronautical Engineering, Imperial College, London and is currently a Reader in Control Engineering at the University of Cambridge, Department of Engineering. His current research is primarily concerned with design principles for feedback regulation in networks (particularly communication and power distribution) and biological systems. |
Glenn Vinnicombe:
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