Professor and Willett Faculty Scholar, Department of Computer Science,
University of Illinois at Urbana Champaign
Title: Adapting to the Human in Cyber-physical Applications
This talk outlines emerging directions in cyber-physical systems such
as enhancing sustainability, streamlining transportation, and
introducing automation into healthcare and medical applications. A
common property of these applications, compared to traditional
embedded system examples, such as factory automation, is that humans
play a much more prominent role as an integral part of the overall
cyber-physical system whose performance is being optimized. It
therefore becomes of interest to understand the role of humans "in the
loop" and explore the challenges of accommodating and adapting to
human behavior in the design of cyber-physical applications. The talk
uses examples of recent research in the area to distill these
challenges into a prospective interdisciplinary research agenda on
human-centric cyber-physical systems.
received his B.Sc. and M.Sc. degrees in
Electrical and Computer Engineering from Ain Shams University, Cairo,
Egypt, in 1990 and 1994 respectively. He received his Ph.D. from the
University of Michigan in 1999 on Quality of Service Adaptation in
Real-Time Systems. He has been an Assistant Professor at the
University of Virginia, where he founded the Software Predictability
Group. He is currently a Professor and Willett Faculty Scholar at the
Department of Computer Science, the University of Illinois at Urbana
Champaign. He has authored/coauthored more than 170 refereed
publications in real-time computing, distributed systems, sensor
networks, and control. He is an Editor-in-Chief of the Journal of
Real-Time Systems, and has served as Associate Editor of the IEEE
Transactions on Mobile Computing, IEEE Transactions on Parallel and
Distributed Systems, IEEE Embedded Systems Letters, the ACM
Transaction on Sensor Networks, and the Ad Hoc Networks Journal. He
chaired (as Program or General Chair) several conferences in his area
including RTAS, RTSS, IPSN, Sensys, DCoSS, ICDCS, and ICAC.
Abdelzaher's research interests lie broadly in understanding and
controlling performance and temporal properties of networked embedded
and software systems in the face of increasing complexity,
distribution, and degree of embedding in an external physical
environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding
Technical Achievement and Leadership Award in Real-time Systems
(2012), the Xerox Award for Faculty Research (2011), as well as
several best paper awards. He is a member of IEEE and ACM.
Director of Academic Marketing, National Instruments
A veteran of National Instruments, Dave has held leadership positions in field engineering, academic and product marketing, and international sales and marketing and has delivered more than 50 keynotes about the application of next-generation technologies in 30 countries. He has met with the ministers of education in both Russia and Kosovo to discuss ways to adopt new generation technologies for science and engineering in university curricula. He has also authored numerous articles and interviewed with multiple domestic and international publications including EE Times Asia, Bits & Chips, Evaluation Engineering, Desktop Engineering, and Sensors.
Charles R. Farrar
Engineering Institute Leader, Los Alamos National Laboratory
Charles “Chuck” Farrar received a Ph.D. in civil engineering from the University of New Mexico in 1988. He has 30 years of experience at Los Alamos National Laboratory (LANL) where he is currently The Engineering Institute Leader (the Eng. Institute is a research and education collaboration between LANL and the University of California, San Diego). His research interests focus on developing integrated hardware and software solutions to structural health monitoring problems. The results of this research are documented in more than 330 publications and numerous keynote lectures at international conferences. He teaches a graduate course in structural health monitoring at UCSD and has development of a short course entitled Structural Health Monitoring: A Statistical Pattern Recognition Approach that has been offered more than 22 times to industry and government agencies in Asia, Australia, Europe and the U.S. His course material has been captured in a book entitled Structural health Monitoring: A Machine Learning Perspective. In 2003 he received the inaugural Structural Health Monitoring Lifetime Achievement Award at the International Workshop on Structural Health Monitoring; in 2007 he was elected a Fellow of the American Society of Mechanical Engineers and in 2012 he was elected a Los Alamos National Laboratory Fellow.
José M. F. Moura
Carnegie Mellon University
Title: Signal Processing for Graphs
Data, big or small, in social networks, evolutionary dynamics, the world-wide-web, or citation networks are indexed by social agents, individuals of a population, web sites, or authors all very different from time marks or image pixels. The relations among these data are captured by a graph and not as simple as with data samples in traditional time series, nor pixels in images. We extend traditional discrete signal processing (DSP) tools and concepts to signals defined in graphs.
: Prof. José M. F. Moura
was elected University Professor at Carnegie Mellon University to recognize his professional achievement as well as his breadth of interests and competence. This title is conferred on faculty members with exceptional national or international distinction. He was inducted in the National Academy of Engineering in 2013. Prior to joining CMU in 1986, he was on the faculty at Instituto Superior Técnico (IST), the Engineering School of the Technical University of Lisbon (Portugal).
He has had visiting faculty appointments at MIT: in 1984-86 as Genrad Associate Professor of Electrical and Computer Engineering (visiting) and in 1999-2000 and 2006-2007 as visiting Professor of Electrical Engineering. He was also a visiting Research Scholar at the University of Southern California in the Summers of 1979-1981. He received his D.Sc. in Electrical Engineering and Computer Science from MIT where he also received his MSc. in Electrical Engineering and the Electrical Engineering degree. He holds a Licenciatura em Engenharia Electrotécnica from IST.
Ali H. Sayed
Title: Inference and Optimization over Networks: It Matters How Information Flows
Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network to solve estimation, inference, and optimization problems in a distributed manner. Some surprising phenomena arise when information is processed in a decentralized fashion over networks. For example, the collection of more information by the agents is not always beneficial to the inference task and even minor variations in how information is processed by the agents can lead to catastrophic error propagation across the graph. In this talk, we elaborate on such phenomena. In particular, we examine the performance of stochastic-gradient learners for global optimization problems. We consider two well-studied classes of distributed schemes including consensus strategies and diffusion strategies. We quantify how the mean-square-error and the convergence rate of the network vary with the combination policy and with the fraction of informed agents. It will be seen that the performance of the network does not necessarily improve with a larger proportion of informed agents. A strategy to counter the degradation in performance is presented. We also examine how the order by which information is processed by the agents is critical; minor variations can lead to catastrophic failure even when the agents are able to solve the inference task individually on their own. To illustrate this effect, we will establish that diffusion protocols are mean-square stable regardless of the network topology. In contrast, consensus networks can become unstable even if all individual nodes are agents. These results indicate that information processing over networks leads to richer dynamics than originally thought with some revealing learning phenomena.
: Ali H. Sayed
is professor and former chairman of electrical engineering at the University of California, Los Angeles, where he directs the UCLA Adaptive Systems Laboratory
. An author of over 400 scholarly publications and five books, his research involves several areas including adaptation and learning, network science, information processing theories, and biologically-inspired designs. His work received several recognitions including the 2012 Technical Achievement Award from the IEEE Signal Processing Society, the 2005 Terman Award from the American Society for Engineering Education, a 2005 Distinguished Lecturer from the IEEE Signal Processing Society, the 2003 Kuwait Prize, and the 1996 IEEE Fink Prize. He has also been awarded several Best Paper Awards from the IEEE and is a Fellow of both the IEEE and the American Association for the Advancement of Science.