Francesco Borrelli
Howard Penn Brown Professor, Department of Mechanical Engineering
University of California, Berkeley

Forecasts, Uncertainty and Control in Autonomous Systems

Abstract

Forecasts will play an increasingly important role in the next generation of autonomous and semi-autonomous systems. Applications include transportation, energy, manufacturing and healthcare systems. Predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the resulting system. However, constraint satisfaction, performance guarantees and real-time computation are challenged by the growing complexity of the engineered system, the human/machine interaction and the uncertainty of the environment where the system operates. In this talk I will first provide an overview the theory and tools that we have developed over the past ten years for the systematic design of predictive controllers for uncertain linear and nonlinear systems. Then, I will focus on our recent results on learning predictive controllers. Throughout the talk I will use two applications to motivate our research and show the benefits of the proposed techniques: Safe Autonomous Cars and Green Autonomous Buildings.

Biography

Francesco Borrelli received the 'Laurea' degree in computer science engineering in 1998 from the University of Naples 'Federico II', Italy. In 2002 he received the PhD from the Automatic Control Laboratory at ETH-Zurich, Switzerland. He is currently a Professor at the Department of Mechanical Engineering of the University of California at Berkeley, USA. He is the author of more than one hundred publications in the field of predictive control. He is author of the book Predictive Control published by Cambridge University Press, the winner of the 2009 NSF CAREER Award and the winner of the 2012 IEEE Control System Technology Award. In 2016 he was elected IEEE fellow. In 2017 he was awarded the Industrial Achievement Award by the International Federation of Automatic Control (IFAC) Council.
Since 2004 he has served as a consultant for major international corporations. He was the founder and CTO of BrightBox Technologies Inc, a company focused on cloud-computing optimization for autonomous systems. He is the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at UC Berkeley. He is the CTO of software of NEXTracker, Inc, the world leader company in photovoltaic trackers.
His research interest are in the area of model predictive control and its application to automated driving and energy systems.

Michael I. Jordan
Pehong Chen Distinguished Professor, Department of EECS, Department of Statistics
AMP Lab, Berkeley AI Research Lab
University of California, Berkeley

On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex

Abstract

Many new theoretical challenges have arisen in the area of gradient-based optimization for large-scale statistical data analysis, driven by the needs of applications and the opportunities provided by new hardware and software platforms. I discuss several recent, related results in this area: (1) a new framework for understanding Nesterov acceleration, obtained by taking a continuous-time, Lagrangian/Hamiltonian/symplectic perspective, (2) a discussion of how to escape saddle points efficiently in nonconvex optimization, and (3) the acceleration of Langevin diffusion.

Biography

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

Nancy A. Lynch
NEC Professor of Software Science and Engineering, Department of EECS
Theory of Distributed Systems Group, CSAIL
Massachusetts Institute of Technology

Robust Ant Colony Algorithms: Density Estimation and House-hunting

Abstract

My research group has been studying Biological Distributed Algorithms for around four years, mainly algorithms for insect colonies but also for some other biological systems such as brain networks. These algorithms have many interesting characteristics: They tend to be simple to describe, but hard to analyze. They are typically probabilistic, and solve problems only approximately. They are flexible (work in different environments), robust (to failures), and adaptive (to changes during operation). These are interesting features for distributed algorithms—not just biological algorithms but also engineered distributed algorithms. We are studying these algorithms for two reasons: in order to understand the behavior of biological systems, and in order to extract ideas from biological systems that may help in designing and analyzing algorithms for wireless networks and other engineered systems. Another issue of interest is composition of algorithms. We would like to understand how one can combine (probabilistic, approximate) biological distributed algorithms for simple problems to obtain algorithms for more complex problems. This talk is mostly about a particular example: An ant colony density estimation algorithm recently developed by Lynch, Musco, and Su, and an ant colony house-hunting algorithm from Mira Radeva’s thesis. I will describe these algorithms and their guarantees separately, and they show how they can be combined. This suggests many new directions for further research.

Biography

Nancy Lynch is the NEC Professor of Software Science and Engineering in the EECS department and heads the Theory of Distributed Systems research group at the Computer Science and Artificial Intelligence Laboratory. She has written numerous research articles about distributed algorithms and impossibility results, and about formal modeling and validation of distributed systems. She is the author of the graduate textbook "Distributed Algorithms" and a co-author of the monograph "The Theory of Timed I/O Automata". She is a member of the National Academy of Engineering, an ACM Fellow, and a winner of several prizes for contributions to distributed computing theory. Prof. Lynch's academic training was in mathematics, at Brooklyn College and MIT. She served on the mathematics and computer science faculty at several other universities, including the University of Southern California and Georgia Tech, prior to joining the MIT faculty in 1982. Since then, she has been working on applying mathematics to the tasks of understanding and constructing complex distributed systems. Her current projects involve designing algorithms for mobile wireless networks, and analyzing timed and hybrid systems and security protocols.

Gilbert Strang
Professor, Department of Mathematics
Massachusetts Institute of Technology

Linear Algebra and Learning from Data

Abstract

I plan to speak about two math topics and a new course at MIT. First, I will talk about rapidly decaying singular values (Vandermonde and Hilbert examples). This property of a matrix can be linked to a Sylvester equation. Second, I will talk about matrix factorizations as the key to theory as well as computation. Finally, I will describe a new course at MIT. The new course 18.065 (Spring 2018) will aim to provide a background in linear algebra and optimization leading to applications including neural nets and gradient descent. A textbook is under way.

Biography

Gilbert Strang was an undergraduate at MIT and a Rhodes Scholar at Balliol College, Oxford. His Ph.D. was from UCLA and since then he has taught at MIT. He has been a Sloan Fellow and a Fairchild Scholar and is a Fellow of the American Academy of Arts and Sciences. He is a Professor of Mathematics at MIT, an Honorary Fellow of Balliol College, and a member of the National Academy of Sciences. Professor Strang has published eleven books. He was the President of SIAM during 1999 and 2000, and Chair of the Joint Policy Board for Mathematics. He received the von Neumann Medal of the US Association for Computational Mechanics, and the Henrici Prize for applied analysis. The first Su Buchin Prize from the International Congress of Industrial and Applied Mathematics, and the Haimo Prize from the Mathematical Association of America, were awarded for his contributions to teaching around the world.