Abstracts and Biographies
"Wireless" has been one of the great technological success stories of the 20th century. At a high level, there are two qualitatively distinct resources that are used by wireless systems: technology and spectrum. The available spectrum is a fixed (though renewable!) resource given by nature while the technology has improved greatly with time. The current regulatory approach of sharing spectrum by allocating bands to exclusive uses dates from an earlier era of technology and so it is natural to wonder what a more modern approach to spectrum sharing should look like. In this talk, I take a first principles look at some of the fundamental issues in spectrum sharing, with a particular focus on those relevant to the coexistence of advanced cognitive radio systems with legacy users.
1884 book, Flatland, shows how unexpectedly different life would
be in a flat world. This talk will discuss a similar thought
experiment: what would the Internet look like if we had flat
Embedded software has traditionally been thought of as "software on small computers." In this traditional view, the principal problem is resource limitations (small memory, small data word sizes, and relatively slow clocks). Solutions emphasize efficiency; software is written at a very low level (in assembly code or C), operating systems with a rich suite of services are avoided, and specialized computer architectures such as programmable DSPs and network processors are developed to provide hardware support for common operations. These solutions have defined the practice of embedded software design and development for the last 25 years or so. However, thanks to the semiconductor industry's ability to follow Moore's law, the resource limitations of 25 years ago should have almost entirely evaporated
today. Why then has embedded software design and development changed so little? It may be that extreme competitive pressure in products based on embedded software, such as consumer electronics, rewards only the most efficient solutions. This argument is questionable, however, since there are many examples where functionality has proven more important than efficiency. In this talk, we argue that resource limitations are not the only defining factor for embedded software, and may not even be the principal factor. Instead, the dominant factors are much higher reliability requirements than for desktop software, greater concurrency, and tighter timing requirements. These differences drive the technology towards different techniques than those that have been applied in conventional computer software. In this talk, we explore those techniques and map out a research agenda for embedded software.
Many large-scale engineering problems are naturally described by graphical models, in which local interactions among different subsystems are captured by edges in the graph. These types of models have already made a significant impact in various areas, including applications in signal and image processing, machine learning, sensor networks, and communication systems. As a particular example, over the past decade, the field of channel coding has been revolutionized by the use of turbo codes and low-density parity check (LDPC) codes, both of which are based on underlying graphs. As we discuss in this talk, one reason underlying the success of graphical models are “message-passing” algorithms, in which nodes in the graph exchange statistical information. Remarkably, although these algorithms operate in a purely local manner (and hence scale to very large problems), they can nonetheless can yield near-optimal answers for inherently difficult problems (e.g., decoding of error-control codes). We discuss a number of remaining challenges that arise in applying these models and algorithms, including theoretical guarantees on performance, efficient implementation in hardware, and the design of novel algorithms.
Commercial CMOS chips routinely operate up to 5 GHz and exciting new opportunities exists in higher frequency bands such as 3-10 GHz, 17 GHz, 24 GHz, and 60 GHz. The Berkeley Wireless Research Center (BWRC) has demonstrated that standard digital 130nm CMOS technology is capable of operation up to 60 GHz, enabling a host of new mm-wave applications such as Gb/s WLAN and compact radar imaging. How did we go from 5 GHz to 60 GHz? This presentation will highlight the design and modeling challenges in moving up to these higher frequencies. A merger of RF and microwave design perspectives will be used to offer insight into the problem. The architecture for a 60 GHz multi-antenna phased array will be discussed, enabling a low cost robust high data rate system to be integrated into a compact package.
Simulation is often called the "third pillar of science," along
with theory and experimentation. Simulation of the human body would
enable a virtual experimental setup that would have applications
in biology and medicine. While a full simulation of the human body
is far from possible today, individual models exist of many of
the organs within the body. One class of problems that arise in
such simulations is the modeling of fluid flow within an organ,
often when that fluid contains immersed elastic structures such
as muscle, membrane, or other tissue. The computational cost of
modeling the fluid dynamics even within a single organ is very
high, requiring the use of today's fastest parallel machines.
In this talk I will describe a scalable parallel algorithm for the immersed boundary method. The method, due to Peskin and McQueen, has been used to simulate blood flow in the heart, blood clotting, the motion of bacteria and sperm, embryo growth, and the response of the cochlea to sound waves. Our parallel implementation uses a novel programming language called Titanium, which is a high performance extension of Java. I will describe the Titanium language and compiler as well as our computational framework for the immersed boundary method, which is designed to be extensible and is publicly available along with the Titanium compiler. I will also talk about some of the remaining open problems in Computer Science, and how this type of interdisciplinary work can lead to new areas of research.
With so much text data available, natural language problems are everywhere: information extraction, text summarization, machine translation, and so on. So why aren't practical NLP solutions more widespread? This talk will discuss some of the primary challenges and recent advances in component tools of language processing systems. One obstacle is that state-of-the-art tools are supervision-hungry. They require large amounts of human-annotated training data and degrade when applied out of domain. For example, newswire-trained parsers have trouble with medical text and conversational speech. However, it is infeasible to create a training set for each language, domain, and problem that arises. I'll discuss several solutions, from adaptation methods, which can blunt the effects of domain change, to unsupervised methods, which require no labeled training data whatsoever. A second general barrier is that most deep linguistic processing is too time consuming to be applied over huge document collections. I'll briefly discuss where and why issues of scale arise from linguistic complexity, and how simplified models and representations can lead to substantially faster analysis methods. Finally, I will talk about some future directions and challenges for NLP research here at Berkeley.
Edward A. Lee is a Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His research interests center on design, modeling, and simulation of embedded, real-time computational systems. He is a director of the Berkeley Center for Hybrid and Embedded Software Systems (CHESS), and is the director of the Berkeley Ptolemy project. He is co-author of five books and numerous papers. His bachelors degree (B.S.) is from Yale University (1979), his masters (S.M.) from MIT (1981), and his Ph.D. from U. C. Berkeley (1986). From 1979 to 1982 he was a member of technical staff at Bell Telephone Laboratories in Holmdel, New Jersey, in the Advanced Data Communications Laboratory. He is a co-founder of BDTI, Inc., where he is currently a Senior Technical Advisor, and has consulted for a number of other companies. He is a Fellow of the IEEE, was an NSF Presidential Young Investigator, and won the 1997 Frederick Emmons Terman Award for Engineering Education.
Dan Klein is an Assistant Professor in the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his bachelor’s degree summa cum laude from Cornell University where he triple-majored in computer science, linguistics, and math. He then went to Oxford University on a Marshall Scholarship, where he earned a master’s degree in linguistics, and finally to Stanford University for his master’s and PhD in computer science. His current research focuses on the automatic organization of natural language information.
Ali Niknejad is an Assistant Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his bachelor’s degree from UCLA, then came to Berkeley, “the best place on earth to do IC research.” It was his passion for research that brought him back to Berkeley after finishing his PhD in 2000 and working in industry for two years. His primary research interests include analog integrated circuits, RF and microwave circuits and systems, device modeling, electromagnetics, communication systems, and scientific computing. Currently he is working with the Berkeley Wireless Research Center (BWRC) and the BSIM Research Group.
James O’Brien is an Assistant Professor in the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his doctorate in Computer Science from the Georgia Institute of Technology. His general research interests are in most areas of computer graphics and animation. His primary area of research involves the physically based simulation of complex deformable systems to generate motion for use in computer generated animation.
Anant Sahai is an Assistant Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His undergraduate work was in EECS at UC Berkeley from 1990-1994 and he was a graduate student at MIT studying Electrical Engineering and Computer Science (Course 6 in MIT-speak) based in the Laboratory for Information and Decision Systems under Prof. Sanjoy Mitter. Before joining the faculty at Berkeley in 2002, he spent 2001 at the startup Enuvis, Inc. where he was on the theoretical/algorithmic side of a team that developed new techniques for GPS detection in very low SNR environments (such as those encountered indoors in urban areas). His current areas of interest are communications, control, and signal processing. Within that range, his focus is on the communications theory side, particularly in the areas of wireless and information theory.
Shankar Sastry is a Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences and from 2000 to 2004 served as Chairman of EECS. He is also a Professor of Bioengineering at UC Berkeley. He received his Ph.D. degree in 1981 from the University of California, Berkeley and was on the faculty of MIT as Assistant Professor from 1980-82 and Harvard University as a chaired Gordon Mc Kay professor in 1994. He has held visiting appointments at the Australian National University, Canberra, the University of Rome, Scuola Normale and University of Pisa, the CNRS laboratory LAAS in Toulouse (poste rouge), Professor Invite at Institut National Polytechnique de Grenoble (CNRS laboratory VERIMAG), and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are embedded and autonomous software, computer vision, computation in novel substrates such as DNA, nonlinear and adaptive control, robotic telesurgery, control of hybrid systems, embedded systems, sensor networks and biological motor control.
Scott Shenker is a Professor in the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received the Sc.B. degree from Brown University, Providence, RI, and the Ph.D. degree from the University of Chicago, Chicago, IL, both in theoretical physics. After a postdoctoral year in the Physics Department, Cornell University, in 1983, he joined Xerox's Palo Alto Research Center (PARC). He left PARC in 1999 to head up a newly established Internet research group at the International Computer Science Institute (ICSI), Berkeley. His research over the past 15 years has spanned the range from computer performance modeling and computer networks to game theory and economics. Most of his recent work has focused on the Internet architecture and related issues. Dr. Shenker received the ACM SIGCOM Award in 2002.
Umesh Vazirani is a Professor in the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received an NSF Presidential Young Investigator Award in 1987 and the Friedman Mathematics Prize in 1985. He has written the book, ``An Introduction to Computational Learning Theory'' (with Michael Kearns), and currently is at the forefront of research in the area of quantum computing.
Martin Wainwright is an Assistant Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences and the Statistics Department at UC Berkeley. He received his doctorate in Electrical Engineering and Computer Science from MIT in 2002. He received a Fellowship from the Natural Sciences and Engineering Research Council of Canada, and the George M. Sprowls award for best Ph.D. thesis from the EECS department at MIT. His research interests are centered on issues of modeling, analysis and computation in large-scale stochastic systems, and their applications to problems including statistical signal processing, sensor networks, and error-control coding.
Ming Wu is a Professor in the Electrical Engineering Division of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his B.S. degree from National Taiwan University, and M.S. and Ph.D. degrees from UC Berkeley in 1983, 1985, and 1988, respectively, all in Electrical Engineering. Before joining the faculty of UC Berkeley, Dr. Wu was a Member of Technical Staff at AT&T Bell Laboratories, Murray Hill (now Lucent Technologies), from 1988 to 1992, and Professor of Electrical Engineering at UCLA from 1993 to 2004. He also held the position of Director of Nanoelectronics Research Facility and Vice Chair for Industrial Relations during his tenure at UCLA. In 1997, Dr. Wu co-founded OMM in San Diego, CA, to commercialize MEMS optical switches. He is a David and Lucile Packard Foundation Fellow, and an IEEE Fellow. Dr. Wu was the founding Co-Chair of IEEE LEOS Summer Topical Meeting on Optical MEMS (1996), the predecessor of IEEE/LEOS International Conference on Optical MEMS. His research interests include optical MEMS (micro-electro-mechanical systems), semiconductor optoelectronics, and biophotonics.
Kathy Yelick is a Professor in the Computer
Science Division of
the Department of Electrical
Engineering and Computer Sciences at UC Berkeley. She received
her Bachelors (1985), Masters (1985), and PhD (1991) degrees in Electrical
Engineering and Computer Science from the Massachusetts Institute
of Technology. Her research interests include parallel computing,
memory hierarchy optimizations, programming languages and compilers.
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