The organizers are proud to announce the following semi-plenary lectures at ECC16.
University of Oxford
Wednesday, June 29, 12.00-12.45
Abstract: Design and control of computer systems that operate in uncertain, competitive or adversarial, environments can be facilitated by formal modelling and analysis. In this paper, we focus on analysis of complex computer systems modelled as turn-based 2 1/2-player games, or stochastic games for short, that are able to express both stochastic and non-stochastic uncertainty. We offer a systematic overview of the body of knowledge and algorithmic techniques for verification and strategy synthesis for stochastic games with respect to a broad class of quantitative properties. These include probabilistic linear-time properties, expected total, discounted and average reward properties, and their branching-time extensions and multi-objective combinations. To demonstrate applicability of the framework as well as its practical implementation in a tool called PRISM-games, we describe several case studies that rely on analysis of stochastic games, from areas such as robotics, and networked and distributed systems.
Biography: Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. Prior to this she was Professor in the School of Computer Science at the University of Birmingham, Lecturer at the University of Leicester and Assistant Professor at the Jagiellonian University in Cracow, Poland. She holds PhD from the University of Leicester. In 2014 she was awarded an honorary doctorate from KTH Royal Institute of Technology in Stockholm. Marta Kwiatkowska spearheaded the development of probabilistic and quantitative methods in verification on the international scene. She led the development of the PRISM model checker, the leading software tool in the area and widely used for research and teaching. Applications of probabilistic model checking have spanned communication and security protocols, nanotechnology designs, power management, game theory, planning and systems biology, with genuine flaws found and corrected in real-world protocols. Marta Kwiatkowska is a member of Academia Europea and Fellow of the BCS. She serves on editorial boards of several journals, including Information and Computation, IEEE Transactions on Software Engineering and Formal Methods in System Design. Her research is supported by the ERC Advanced Grant VERIWARE "From software verification to everyware verification" and the EPSRC Programme Grant on Mobile Autonomy.
Wednesday, June 29, 12:00 – 12:45
Abstract: Online advertising is a US$600 Billion industry where feedback control has come to play a critical role. The control problems are challenging and involve nonlinearities including discontinuities, high dimensionality, uncertainties, non-Gaussian noise, and more. In this paper systems engineering principles are applied to a core optimization problem within online advertising. First we demonstrate how the optimization problem may be decomposed into separate low-level estimation and high-level control modules. Then we derive a plant model from first principle to show how uncertainties and noise propagate through the plant. The plant model reveals challenges of the control problem and provides a framework to assess the impact on the plant behavior from different designs of the low- level estimation module. Thereafter, we describe a bid randomization technique that can be used in various ways to improve the performance and robustness of the system. The bid randomization technique is finally used to develop an algorithm for exploration and exploitation of an auction-based network, furnishing a solution to the above estimation subproblem.
Biography: Dr. Karlsson is Vice President of R&D at AOL Platforms, where he is directing the control research for online ad and content distribution. From 2002 to 2005 he was the principal investigator of navigation and feedback control at Evolution Robotics. He has invented e.g. AdLearn™ Control and ContentLearn™ for control and optimization of online ad and content distribution, and vSLAM™ for simultaneous localization and mapping (used in the autonomous vacuum cleaner Roomba). Dr. Karlsson is the inventor of 27 issued U.S. patents and holds a Ph.D. in Dynamic Systems, Control, and Robotics and an M.A. in Statistics and Applied Probability from University of California at Santa Barbara and an M.S. in Engineering Physics from Lund University. In 2015 he received the Distinguished Alumni Award from the Department of Mechanical Engineering at UCSB in recognition of "outstanding application of systems engineering principles to the field of online advertising.
Imperial College London
Thursday, June 30, 12:00 – 12:45
Abstract: The notion of Input-to-State Stability allows to study the concept of robust stability for nonlinear systems subject to input disturbances. The original notion applies to systems defined on Euclidean space and globally asymptotically stable at the origin. We present extensions that allow much richer dynamical behaviours, such as oscillations, multi-stability and systems defined on manifolds. Two approaches will be discussed and characterized, involving almost global stability notions or global attractivity.
Biography: David Angeli was born in Siena, Italy, in 1971. He received the
B.S. degree in Computer Science Engineering and the Ph.D. in Control
Theory from University of Florence, Florence, Italy, in 1996 and 2000,
Since 2000 he was an Assistant and Associate Professor (2005) with the Department of Systems and Computer Science, University of Florence. He was a visiting Professor with I.N.R.I.A de Rocquencourt, Paris, France, in 2007. Since 2008, he joined as a Senior Lecturer the Department of Electrical and Electronic Engineering of Imperial College London, where he is currently a Reader in Nonlinear Control and the Director of the MSc in Control Systems. He has been a Fellow of the IEEE since 2015.
He has authored more than 80 journal papers in the areas of stability of nonlinear systems, dynamical systems, control of constrained systems, and chemical reaction networks.
Thursday, June 30, 12:00 – 12:45
Abstract: The Danish energy system has been a front- runner within integration of renewables for the last decades. Historically this has led to needs and new developments within design and control of production and supply systems, on individual plant level as well as on portfolio level. These developments include applications within adaptive control, multivariable control, portfolio balancing control and load planning systems. In future the needs for more detailed utilization of plant and system knowledge will grow further due to increased market and technology complexity. Improved utilization of hybrid knowledge, i.e. modelling on different timescales and modelling with diverse fidelity, contains large potential improvements in the design phase as well as in the operational phase. These challenges should be taken up in research and eventually be applied commercially.
Biography: Dr. Mølbak is co-owner of Added Values, where he is focusing on specialized optimization of energy plants and systems including integrated design, on-line optimization and control. Bridging university R&D with commercial interests has been a key element through his career. This has lead to a number of practical acheivements within energy plants and systems, for instance multivariable control of combined heat and power plants, predictive control of boilers, balance control of power plant port folios and model-based on-line performance monitoring of production systems. Until 2013 he was with DONG Energy, where he among other things was responsible for R&D within operational optimization and smart grid. Since 2013 he has been with Added Values, a consultant and software company supporting the energy business in Denmark and internationally. Since 2003 he has been Affiliated Professor at Department of Electronic Systems, Aalborg University. He holds a PhD and a MSc in control of energy plants from the same department.
University of California, San Fransisco
Friday, July 1, 12:00 – 12:45 Radiosalen
Abstract: Biological systems must sense and adapt to changes in their environment. Molecular networks capable of such adaptation belong to two well-known classes, feedforward and feedback structures, but the fundamental limitations and tradeoffs of these two classes remains unknown. Here we study the advantages and limitations of the feedforward class using three-node circuits representative of these architectures. The feedforward model we investigate displays a tradeoff between the sensitivity of the response (its peak response) and its precision (its error in its return to steady-state). We suggest two ways in which this tradeoff can be alleviated: (1) by introducing a nonlinearity in the production of a specific node in the network, or (2) by adding a feedback loop to the input. We present analytical and numerical examples to support our findings.
Biography: Hana El-Samad is a faculty member in the department of Biochemistry and Biophysics at the University of California, San Francisco and the California Institute for Quantitative Biosciences (QB3), where she holds the Grace Boyer Junior Endowed Chair in Biophysics. She holds many awards and honors, including a 2010 Packard Fellow, an Allen Distinguished Investigator and the 2011 Donald P. Eckman Award. Dr. El-Samad joined UCSF after obtaining a doctorate degree in Mechanical Engineering from the University of California, Santa Barbara. Her research group emphasizes the role of control theory and dynamical systems in the study of biological networks. Her research interests include the investigation of stress responses, synthetic biology, and biological stochastic phenomena, in addition to the establishment of computational and technological infrastructures that allow for their quantitative probing in single cells.
University of Minnesota
Friday, July 1, 12:00 – 12:45
Abstract: This review describes the design of controller architectures that achieve a desired tradeoff between performance of distributed systems and controller complexity. The approach consists of two steps. First, we design controller architecture by incorporating sparsity-promoting functions into the optimal control problem, where the added regularizers penalize the number of communication links in the distributed controller. Second, we optimize distributed controller subject to structural constraints determined by the identified communication patterns. In the first step, the controller architecture is identified using the method of multipliers algorithm. This method exploits separability of the sparsity-promoting regularization terms and transforms the augmented Lagrangian into a form that can be efficiently minimized using proximal methods. The problem is, in general, nonconvex, but we identify classes of convex problems that arise in the design of sparse undirected networks, optimal sensor/actuator selection, and decentralized control of positive systems. In this case, the synthesis problem is a convex program whose globally optimal solution can be computed efficiently. Several examples are provided to demonstrate the effectiveness of the developed framework.
Biography: Mihailo R. Jovanovic received the Ph.D. degree from the University of California, Santa Barbara, in 2004, under the direction of Bassam Bamieh. Currently, he is a Professor of Electrical and Computer Engineering at the University of Minnesota, Minneapolis. His expertise is in modeling, dynamics, and control of large-scale and distributed systems and his current research focuses on sparsity-promoting optimal control, dynamics and control of fluid flows, and fundamental performance limitations in the design of large dynamic networks. Prof. Jovanovic received a Career Award from the National Science Foundation in 2007, the George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society in 2013 and the Distinguished Alumni Award from the Department of Mechanical Engineering at UC Santa Barbara in 2014.