October 17-19, 2012
Fort Lauderdale, FL
Hilton Fort Lauderdale Marina Hotel
Professor Mathukumalli Vidyasagar, Cecil & Ida Green Chair in Systems Biology Science, Department of Bioengineering, The University of Texas at Dallas
Time/Location: During Awards Banquet (8pm-10pm, Thursday (Day 2)), Salon A&B&D&E
Bio: Mathukumalli Vidyasagar was born in Guntur, India on September 29, 1947. He received his B.S., M.S., and Ph.D. degrees, all in Electrical Engineering, from the University of Wisconsin, Madison, in 1965, 1967, 1969 respectively. After finishing his Ph.D., he taught for one year at Marquette University, Milwaukee, WI, ten years at Concordia University, Montreal, Canada, and nine years at the University of Waterloo, Waterloo, Canada. In 1989, twenty years after finishing his Ph.D., he returned to his native India to set up a new R&D laboratory called Centre for Artificial Intelligence and Robotics in Bangalore, under the auspices of the Ministry of Defence, Government of India. During this period CAIR grew into a nationally recognized laboratory with more than 40 scientists and about 85 persons in all. In 2000 he moved to the private sector to join Tata Consultancy Services, India's largest software and services company, and now one of the world's top ten such companies. He joined TCS as an Executive Vice President, based in Hyderabad. His responsibilities included setting up the Advanced Technology Center, consisting of more than 80 scientists and engineers, encompassing a wide variety of areas such as e-security, identity management, open source software, computational biology, and quantitative finance. In 2009 he retired from TCS and joined the University of Texas at Dallas as Cecil & Ida Green Chair in Systems Biology Science. In 2010 he became the Founding Head of the newly created Bioengineering Department.
During his professional career of more than four decades, Vidyasagar has worked in a number of areas, including control and system theory applied to both linear as well as nonlinear systems, robotics, neural networks, statistical learning theory, and most recently, the computational biology of cancer. He prefers to change his area of research at regular intervals, and to write a research monograph once he has made some useful contributions to the new area. To date he has published ten books and nearly 140 research papers in peer-reviewed journals.
In recognition of his research Vidyasagar has received a number of awards, including Fellowship of the Royal Society, the oldest scientific society in continuous existence, the IEEE Control Systems Award, and a Distinguished Service Citation from his alma mater, the University of Wisconsin at Madison.
Abstract: Cancer is the most individual of diseases, in that no two manifestations of the disease are alike. For this reason, personalized therapy is not only desirable but imperative. Recent advances in experimental techniques, coupled with dramatic reductions in cost, have enabled the biology community to generate massive amounts of raw data. By turning this data into information and information into knowledge, in principle it is possible for engineers to make solid contributions to cancer biology. However, in order to do so, it is necessary to take into account some fundamental differences between biological data and engineering data. For instance, most biological problems are characterized by a small number of samples (typically a few dozen or at best a few hundred), wherein each sample consists of a very high-dimensional vector (typical in the tens of thousands). Extracting the most informative features from such data sets is a non-standard type of machine learning problem. In this talk we will present a novel algorithm for this type of situation, and its application to two different problems in cancer therapy. The first application is to determine which ovarian cancer patients are likely to be super-responders to platinum chemotherapy, and which patients are likely to be poor responders. The second application is to determine which endometrial cancer patients are at risk of their cancer metastasizing (spreading) to their lymph nodes. Some problems for future research will also be discussed.
Professor Lucy Pao, Richard and Joy Dorf Professor, Electrical, Computer, and Energy Engineering Department, University of Colorado
Time/Location: 8am-9am Wednesday (Day 1, Salon D& E)
Bio: Lucy Pao received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Stanford University, and she is currently the Richard and Joy Dorf Professor in the Electrical, Computer, and Energy Engineering Department at the University of Colorado Boulder. She has been a Visiting Scholar at Harvard University, a Visiting Miller Professor at the University of California at Berkeley, and a Visiting Scholar at the US National Renewable Energy Laboratory. She has interests in the areas of control systems (with applications to flexible structures, atomic force microscopes, disk drives, tape systems, power converters, and wind turbines), multisensor data fusion (with applications to unmanned autonomous vehicles, satellites, and automotive active safety systems), and haptic and multimodal visual/haptic/audio interfaces (with applications to scientific visualization and spatial communication).
Professor Pao has received a number of awards and has been active in many professional society committees and positions. Selected honors include a NSF CAREER Award, an ONR Young Investigator Award, an IFAC World Congress Young Author Prize, and a World Haptics Conference Best Paper Award. Selected current activities include being an IEEE Control Systems Society (CSS) Distinguished Lecturer, a member of the IEEE CSS Board of Governors, and General Chair for the 2013 American Control Conference. She was recently (2012) elevated to IEEE Fellow and was a member of the 2010-2011 US Defense Science Study Group. She was also the founding Scientific Director (2007-2011) for the Center for Research and Education in Wind (CREW), a multi-institutional wind energy center involving the University of Colorado Boulder, the US National Renewable Energy Laboratory, Colorado School of Mines, and Colorado State University, in partnership with the US National Center for Atmospheric Research and the US National Oceanic and Atmospheric Administration.
Abstract: Wind energy is recognized worldwide as cost-effective and environmentally friendly and is among the world's fastest-growing sources of electrical energy. Despite the amazing growth in global wind power installations in recent years, science and engineering challenges still exist. Megawatt wind turbines are large, flexible structures that operate in uncertain, time-varying wind and weather conditions and lend themselves nicely to advanced control solutions. Advanced controllers can help achieve the overall goal of decreasing the cost of wind energy by increasing the efficiency, and thus the energy capture, or by reducing structural loading and increasing the lifetimes of the components and turbine structures.
In this talk, we will first provide an overview of wind energy systems. We will describe the main components of wind turbines, the sensors and actuators, the different operating regions, and we will outline the current state of the art in wind turbine modeling and control. We will then discuss our recent work in developing combined feedforward and feedback controllers for wind turbines using novel wind inflow sensing technologies. Model-inverse based controllers, H-infinity controllers, and model predictive controllers can be designed to take advantage of preview wind measurements to yield significant reductions in structural loading while maintaining the power capture levels of the wind turbine. We shall close by discussing a number of continuing challenges and highlighting topics of growing interest, including coordinated control of arrays of turbines on wind farms, modeling and control of floating offshore wind turbines, and the ability of wind turbines to provide active power control services to help stabilize the frequency of the utility grid.
Professor Neville Hogan, Sun Jae Professor of Mechanical Engineering, Professor of Brain and Cognitive Sciences, Director of the Newman Laboratory, Massachusetts Institute of Technology
Time/Location: 8am-9am Friday (Day 3), Salon D&E
Bio: Neville Hogan is Sun Jae Professor of Mechanical Engineering and Professor of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. His research is in robotics, motor neuroscience, and rehabilitation engineering. He is Director of the Newman Laboratory for Biomechanics and Human Rehabilitation and a founder and director of Interactive Motion Technologies, Inc. Awards include Honorary Doctorates from Delft University of Technology and Dublin Institute of Technology, the Silver Medal of the Royal Academy of Medicine in Ireland, the Henry M. Paynter Outstanding Investigator Award from the American Society of Mechanical Engineers (ASME), and the Rufus T. Oldenburger Medal from the Dynamic Systems and Control Division of ASME.
Abstract: Emerging therapeutic and assistive robotic technologies are “flagship” applications of neuro-mechanical engineering—the intersection of neuroscience with dynamic systems and control engineering. Both technologies require skillful control of physical interaction. Robot-aided neuro-motor therapy—now clinically proven to be effective—requires forceful but sensitive physical interaction with a patient. Motorized amputation prostheses must manage physical interaction with objects in the world and also with the amputee. This talk will review how mimicry of natural control provides the gentleness required for robot-aided therapy and enables seamless coordination of natural and prosthetic limbs. Controlling mechanical impedance is the key to managing physical interaction. I will argue that knowledge of the human neuro-mechanical system is a pre-requisite for success in these applications and present recent studies of the human wrist and ankle mechanical impedance.
Professor Manfred Morari, ETH Zurich
Time/Location: 8am-9am Thursday (Day 2), Salon D&E
Bio: Manfred Morari was head of the Department of Information Technology and Electrical Engineering at ETH Zurich from 2009 to 2011. He was head of the Automatic Control Laboratory from 1994 to 2008. Before that he was the McCollum-Corcoran Professor of Chemical Engineering and Executive Officer for Control and Dynamical Systems at the California Institute of Technology. He obtained the diploma from ETH Zurich and the Ph.D. from the University of Minnesota, both in chemical engineering. His interests are in hybrid systems and the control of biomedical systems. In recognition of his research contributions he received numerous awards, among them the Donald P. Eckman Award, the John R. Ragazzini Award and the Richard E. Bellman Control Heritage Award of the American Automatic Control Council, the Allan P. Colburn Award and the Professional Progress Award of the AIChE, the Curtis W. McGraw Research Award of the ASEE, Doctor Honoris Causa from Babes-Bolyai University, Fellow of IEEE, IFAC and AIChE, the IEEE Control Systems Technical Field Award, and was elected to the National Academy of Engineering (U.S.). Morari has held appointments with Exxon and ICI plc and serves on the technical advisory boards of several major corporations.
Abstract: In the 1980s Model Predictive Control (MPC) became the algorithm of choice in the process industries for demanding multi-variable applications involving constraints. Today's vastly more powerful computational resources and a series of new algorithms have made these tools suitable for problems of essentially any size and time scale. I will describe the road taken and illustrate the effectiveness with industrial examples from the automotive and power electronics domains and the industrial energy sector.