Speaker/Affiliation: Shyamnath Gollakota/Massachusetts Institute of Technology, Cambridge MA
Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Students and Outside Interested Parties
Date/Time: Tuesday, May 1, 2012, 11:00 am
Location: ACES 2.302
Host: Lili Qiu
Talk Title: Embracing Interference in Wireless Systems
Abstract:
The wireless medium is a shared resource. If nearby devices transmit at the same
time, the transmitted signals interfere, resulting in a collision. In
traditional networks, collisions cause the loss of the transmitted information.
For this reason, wireless systems have been designed with the assumption that
interference is intrinsically harmful and must be avoided.
My research takes an alternate approach: Instead of viewing interference as an
inherently counterproductive phenomenon that should to be avoided, I design
practical systems that can successfully reconstruct the transmitted information
even in the presence of collisions hence, rendering the interference harmless.
Moreover, these new systems can exploit interference constructively to increase
throughput and improve security.
In the talk, I will present the first WiFi receiver that decodes colliding
packets, rendering WiFi interference harmless. I will also show how to inject
useful interference that increases network throughput using a system called
analog network coding. Then, I will talk about the role of interference in
improving security. I will use interference to secure insecure medical
implants, and establish secure wireless connections without having users enter
passwords or use pre-shared secret keys.
Bio:
Shyamnath Gollakota is a PhD candidate in Electrical Engineering and Computer
Science at the Massachusetts Institute of Technology. His research in
networking focuses on addressing wireless interference and security. He has
been awarded the ACM SIGCOMM 2008 Best paper award for ZigZag decoding, ACM
SIGCOMM 2011 Best Paper Award for securing medical implants, and AT&T Applied
Security Award for password-free wireless security. His work has appeared in
venues like Slashdot, BBC Radio, Forbes, and Network World. He received his
masters in Electrical Engineering and Computer Science at MIT, and a bachelors
degree in Computer Science and Engineering at IIT Madras.
Speaker/Affiliation: Professor Erik Winfree/California Institute of Technology
Talk Audience: UTCS faculty, graduate students, undergraduate students and outside interested parties
Date/Time: Tuesday, May 8, 2012, 10:00 am
Location: ACES 2.302
Host: Peter Stone
Talk Title: Computational Tools for Molecular Programming: Design, Simulation, and Analysis
Talk Abstract:
"Inspired by the information processing core of biological organisms and its ability to fabricate intricate machinery from the molecular scale up to the macroscopic scale, research in synthetic biology,
molecular programming, and nucleic acid nanotechnology aims to create information-based chemical systems that carry out human-defined molecular programs that input, output, and manipulate molecules and molecular structures. For chemistry to become the next information technology substrate, we will need improved tools for designing, simulating, and analyzing complex molecular circuits and systems. Using DNA nanotechnology as a model system, I will discuss how programming languages can be devised for specifying molecular systems at a high level, how compilers can translate such specifications into concrete molecular implementations, how both high-level and low-level specifications can be simulated and verified according to behavioral logic and the underlying biophysics of molecular interactions, and how Bayesian analysis techniques can be used to understand and predict the behavior of experimental systems that, at this point, still inevitably contain many ill-characterized components and interactions."
Speaker Bio:
Professor Winfree's research involves theoretical and experimental aspects of molecular programming. Models of computation are developed that incorporate essential features of molecular folding, molecular self-assembly, biochemical circuits, and molecular robotics. These models are studied to determine their expressiveness for programming behaviors such as memory, behavior, and morphogenesis. Methods for compiling abstract molecular programs into actual molecules are developed and tested in the laboratory.
Degrees and Appointments
B.S., University of Chicago, 1991 Ph.D., Caltech, 1998. Assistant Professor, 1999-2006 Associate Professor of Computer Science and Computation and Neural Systems, 2006-07 Associate Professor, 2007-10 Professor, 2010-.
List of Research Areas
molecular programming, DNA nanotechnology, DNA Computing, nucleic acid systems, biophysics, complexity theory, programming languages and compilers, evolutionary systems
Speaker/Affiliation: Doug Smith/Kestrel Institute & Kestrel Technology
Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Students and Outside Interested Parties
Date/Time: Monday, April 30, 2012, 3:00 pm
Location: ACES 2.302
Host: William Cook
Talk Title: Synthesis of Concurrent Garbage Collectors
Talk Abstract:
Garbage collection is an attractive target for exploring the issues in synthesizing concurrent algorithms. The basic algorithms are tricky to get correct, and become even trickier when implemented on modern multi-core architectures. We are using Kestrel's Specware system to synthesize garbage collectors, starting from a formal specification of collection requirements. Novel features include a mixed algebraic/coalgebraic style of specification, and coalgebraically-oriented transformations that generate correct-by-construction refinements. Dijkstra's on-the-fly concurrent garbage collector algorithm falls out from a straightforward application of fixpoint-iteration algorithm design and a finite differencing transformation. Other transformations include refinement of high-level types to more complicated, but efficient low-level types, globalization to transform from functional to shared-memory programs, and so on. Most of the transformations work by performing equational calculations using theorems from the application domain, and current efforts are augmenting the transformations to generate checkable proofs at the same time as they refine the source specification - in effect to generate proof-carrying code.
Bio:
Dr. Douglas R. Smith is a Principal Scientist at Kestrel Institute and serves as President of Kestrel Technology LLC (both in Palo Alto). He is a Fellow of the American Association of Artificial Intelligence (AAAI) and an ASE Fellow (Automated Software Engineering). During 1986-2000, he periodically taught an advanced graduate course on knowledge-based software development at Stanford University. Dr. Smith served as Chairman of IFIP Working Group 2.1 on Algorithmic Languages and Calculi during 1994-2000.
Dr. Smith's research interests have centered around formal specifications, automated inference, and program synthesis. He has led the development of a series of software synthesis systems, including KIDS (Kestrel Interactive Development System), Specware, Designware, and Planware. Applications have included a variety of complex high-performance schedulers for the US Air Force. Recent projects focus on the synthesis of concurrent garbage collectors and high-performance constraint solvers.
Talk Title: The Future of Cloud-Server Architecture: Looking at the end of multi-core and beyond
Talk Abstract:
Cloud computing has emerged as a dominant platform for delivering online services and resulted in a constantly-growing demand for datacenter performance. Following Moore‚s Law, popular belief holds that the number of cores on chip will grow at an exponential rate, leading to a commensurate increase in server performance. However, while demand for cloud infrastructure continues to grow, the semiconductor manufacturing industry has reached physical limits, unable to improve performance due to rising chip power. Continuing to increase performance of the cloud while staying within physical constraints therefore mandates optimizing server efficiency. In this talk, I will summarize our recent work on quantifying the inefficiencies of cloud software on modern processors and describe Proactive Instruction Fetch, a technique to address one of the dominant inefficiencies that we identified. I will then conclude the talk by describing a projection of server architecture in future technologies, where an increasing fraction of the chip is dark silicon that we cannot afford to power.
Speaker Bio:
Mike Ferdman is a Ph.D. candidate at Carnegie Mellon University. His research interests are in the area of computer architecture, with emphasis on the design of server systems. Mike‚s primary research objective is to understand the interactions of software and processor microarchitecture to enable the design of high-performance, power-efficient, and compact servers.
Speaker/Affiliation: Alexis Battle/Stanford University
Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Students and Outside Interested Parties
Date/Time: Friday, May 4, 2012, 11:00 am
Location: ACES 2.302
Host: Ray Mooney
Talk Title: Combining data and networks to unravel the genetics of complex traits
Talk Abstract: Complex traits, including many human diseases, are affected by multiple genetic elements, often working together in intricate networks and pathways. Recent technologies have allowed us to collect detailed genetic profiles on a large scale, opening up the possibility of identifying the genes and pathways disrupted in many diseases. However, untangling genetic factors from such data has presented significant statistical challenges. In particular, available studies are often underpowered, with up to millions of candidate genetic elements, but only hundreds or thousands of individuals.
In this talk, I will discuss machine learning methods for inferring the effects of genetic variation on complex traits, based on structured probabilistic models. We model the effects of multiple genetic elements jointly on a given trait, and leverage known relationships among genes, incorporating gene networks as structured prior distributions. Thus, our models will preferentially identify multiple genetic elements which are known to be functionally connected, reflecting a more biologically plausible model of many complex traits, and providing improved statistical power. I will discuss the use of these methods in identifying genetic variants relevant to autoimmune diseases, and in identifying important interactions between genes in cancer survival.
Speaker Bio: Alexis Battle is a PhD candidate in Computer Science at Stanford University. Her research in computational biology focuses on machine learning and probabilistic models for the genetics of complex traits. Alexis received her BS in Symbolic Systems from Stanford, and spent four years as a member of the technical staff at Google. She is the recipient of an NSF Graduate Research Fellowship and an NIH NIGMS training award.
Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Students and Outside Interested Parties
Date/Time: Thursday, May 17, 2012, 11:00 am
Location: PAR 101
Host: Ray Mooney
Talk Title: Entropy Reduction and Asian Languages
Abstract:
This talk presents a particular conceptualization of human language understanding as information processing. From this viewpoint, understanding a sentence word-by-word is a kind of incomplete perception problem in which comprehenders over time become more certain about the linguistic structure of the utterance they are trying to understand. The Entropy Reduction hypothesis holds that the scale of these certainty-increases reflects psychological effort. This claim revives the application of information theory to psycholinguistics, which languished since the 1950s. But in contrast to that earlier work, modern applications of information theory to language-understanding now use generative grammars to specify the relevant structures and their probabilities. This representation makes it possible to apply standard techniques from computational linguistics to work out weighted "expectations" about as-yet-unheard words. The talk exemplifies the general theory using examples from Korean and Chinese. The prenomial character of relative clauses in these languages is an important test case for any general cognitive theory of sentence processing.
Bio:
John Hale's research focuses on cognitive models of human language. He is particularly interested in combining ideas from AI, (Computational) Linguistics and Psychology to address questions about human sentence comprehension. He has been an Associate Professor at Cornell University since 2008. His PhD is from Johns Hopkins University (2003).
Speaker/Affiliation: Ming C. Lin/Dept of Computer Science, University of North Carolina at Chapel Hill
Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Students and Outside Interested Parties
Date/Time: Wednesday, May 9, 2012, 11:00 am
Location: PAI 3.14
Host: Don Fussell
Talk Title: Fast Simulation of Complex Phenomena Using Hybrid Models
Abstract:
From turbulent fluid flow to chaotic traffic patterns, many phenomena observed in nature and in society show complex emergent behavior on different scales. The modeling and simulation of such phenomena continues to intrigue scientists and researchers across different fields, from computational sciences, traffic engineering, urban planning, to social sciences. Understanding and reproducing the visual appearance and dynamic behavior of such complex phenomena through simulation is valuable for enhancing the realism of virtual scenes and for improving the efficiency of design evaluation. This is especially important for interactive applications, where it is impossible to manually animate all the possible interactions and responses beforehand. In this talk, we introduce several hybrid models that synthesize together macroscopic models of the large- scale flows and local representations of small-scale behaviors to capture both the aggregate dynamics and fine-grained details of such phenomena with significantly accelerated performance on commodity hardware. Some of the example dynamical systems that I will describe using these hybrid techniques include turbulent fluids, granular flows, crowd simulation, traffic visualization, and sound synthesis. I conclude by discussing some possible future directions.
Bio:
Ming C. Lin is currently John R. & Louise S. Parker Distinguished Professor of Computer Science at the University of North Carolina (UNC), Chapel Hill. She obtained her B.S., M.S., and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. She received several honors and awards, including the NSF Young Faculty Career Award in 1995, Honda Research Initiation Award in 1997, UNC/IBM Junior Faculty Development Award in 1999, UNC Hettleman Award for Scholarly Achievements in 2003, Beverly W. Long Distinguished Professorship 2007-2010, Carolina Womenï¿∏s Center Faculty Scholar in 2008, UNC WOWS Scholar 2009-2011, IEEE VGTC Virtual Reality Technical Achievement Award in 2010, and eight best paper awards at international conferences. She is a Fellow of ACM and IEEE.
Her research interests include physically-based modeling, virtual environments, sound rendering, haptics, robotics, and geometric computing. She has (co-)authored more than 230 refereed publications in these areas and co-edited/authored four books. She has served on over 120 program committees of leading conferences and co-chaired dozens of international conferences and workshops. She is currently the Editor-in-Chief (EIC) of IEEE Transactions on Visualization and Computer Graphics, a member of 6 editorial boards, and a guest editor for over a dozen of scientific journals and technical magazines. She also has served on several steering committees and advisory boards of international conferences, as well as government and industrial technical advisory committees.
