2006-2007 AbstractsComputer Science Distinguished Lecturer Series:Dude, Where is my genome? Past, Present and Future of Genomics TechnologiesBud Mishra, Courant Institute, NYU School of Medicine & Mt. Sinai School of Medicine
4:10 p.m., Monday, September 4, 2006 AbstractIn this talk, I will describe the basic technologies, experiment design and algorithmic analysis needed to build an effective single-molecule sequencing platform. I will start with the basic optical-mapping platform, which has been used for restriction maps for clones, whole-genomes and difficult-to-sequence regions of human genome (e.g., Y-chromosome). I will also describe how we plan to successively improve it to do haplotypes, chromosomal aberration maps, personal sequencing, methylation pattern maps, expression profile, and alternative-splicing measurements. I will also describe how these ideas originally developed in the optical setting can be adapted to nano-scale technologies. I will also show how important ideas from algorithm analysis, complexity theory, and probabilistic methods are used in designing superior technologies and experiments that yield to efficient computational analysis. I will discuss few important applications to oncogenomics, association studies and genetics. The talk will be self-contained and will assume no prior knowledge of biology, biotechnology or nanotechnology. BiographyProf. Bud Mishra is a professor of computer science and mathematics at NYU's Courant Institute of Mathematical Sciences, professor of human genetics at Mount Sinai School of Medicine, and a professor of cell biology at NYU School of Medicine. He founded the NYU/Courant Bioinformatics Group, a multi-disciplinary group working on research at the interface of computer science, applied mathematics and biology. He has developed several sophisticated technologies, algorithms, and statistical analysis tools to attack biological problems that range from deciphering the structure of a genome to understanding chromosomal aberrations and their relation to cancer genetics. Prof. Mishra has a degree in Physics from Utkal University, in Electronics and Communication Engineering from IIT, Kharagpur, and MS and PhD degrees in Computer Science from Carnegie-Mellon University. He has industrial experience in Computer Science (Tartan Laboratories, and ATTAP), Finance (Tudor Investment and PRF, LLC), Robotics and Biotechnology (OpGen, and Bioarrays). His research has ranged from compilers, algorithms and complexity, logic, and algebra to robotics, finance, internet, and biology. He also holds adjunct professorship at Tata Institute of Fundamental Research in Mumbai, India. From 2001-04, he was a professor at the Watson School of Biological Sciences, Cold Spring Harbor Lab. He is a co-inventor of Optical Mapping, Array Mapping, and Copy-Number Variation Mapping in biotechnologies. His other technological inventions include model checker for circuit verification, grasping and fixturing algorithms, reactive robotics, real-time schedulers, and nanotechnology for DNA profiling. Faculty Contact: Nancy Amato (amato [at] cs.tamu.edu) Computer Science Distinguished Lecturer Series:Massively Parallel Systems: Ready or Not, Here They ComeManish Gupta, IBM
4:10 p.m., Wednesday, September 27, 2006 AbstractRising power dissipation in microprocessor chips is driving computer architects towards a variety of solutions, all of which require exploiting greater degrees of parallelism. Hence, even though Moore's Law is alive, relying primarily on frequency scaling is no longer a viable path for meeting the growing computational needs of applications. There are several hurdles to exploiting greater levels of parallelism, such as, programming complexity, communication bottlenecks, interference from operating system services, and system management costs. We describe our experiences with the IBM Blue Gene project on pushing the limits of scalability in all aspects of system design. We will present our successes as well as outstanding challenges in programming and managing massively parallel systems. We will argue that we need another revolution in software to help achieve scientific breakthroughs and truly deliver on the promise offered by the next generation of high performance computing systems. BiographyManish Gupta is a Research Staff Member at the IBM T. J. Watson Research Center. Most recently, he was Senior Manager of the Emerging System Software department, and led research on system software for the IBM Blue Gene supercomputer, parallel performance tools for all of POWER based systems, applications for the Cell Broadband Engine, and visualization systems for Deep Computing. He is currently on assignment in India as the CTO of the India Systems and Technology Lab, which is one of the IBM global labs developing software for all of IBM servers and storage products. Manish received a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1992, and has worked with IBM since then. He has received two Outstanding Technical Achievement Awards at IBM, filed about a dozen patents, and has co-authored over 60 papers in the areas of high performance compilers, parallel computing, and Java Virtual Machine optimizations. Faculty Contact: Lawrence Rauchwerger (rwerger [at] cs.tamu.edu) Computer Science Distinguished Lecturer Series:Efficiency and Security Issues for Distributed Data StructuresMichael T. Goodrich, University of California, Irvine
4:10 p.m., Monday, December 4, 2006 AbstractThis talk highlights our recent work on the efficiency and security of distributed peer-to-peer data structures, including distributed hash tables (DHTs), rainbow skip graphs, and skip webs. These structures share a common theme in that they assume that data is distributed throughout a peer-to-peer network, for which we wish to build an indexing scheme so that we can locate items of interest quickly, as well as efficiently insert and delete items from the search structure. These structures differ in how they perform this indexing, with DHTs supporting only exact-match queries, rainbow skip graphs supporting one-dimensional range queries, and skip webs supporting multi-dimensional searches. Their efficiency depends on how well they distribute search keys and how well they avoid congestion among concurrent searches. Their security derives from how well they tolerate node losses and/or malicious responses. BiographyProf. Goodrich received his B.A. in Mathematics and Computer Science from Calvin College in 1983 and his PhD in Computer Sciences from Purdue University in 1987. He served as a professor of computer science at Johns Hopkins University from 1987-2001, at which time he joined the faculty at UC-Irvine. He has also served on the faculties of Univ. of Illinois and Brown University during sabbatical visits. Dr. Goodrich's research is directed at the design of high performance algorithms and data structures for solving large-scale problems motivated from information assurance and security, the Internet, information visualization, and geometric computing. He has pioneered and led research on efficient parallel and distributed solutions to a number of fundamental problems, including sorting, convex hull construction, segment intersection reporting, fixed-dimensional linear programming, polygon triangulation, Voronoi diagram construction, and data authentication. With nearly 200 publications, including several widely-adopted books, his recent work includes contributions to efficient and secure distributed data structures, authenticated geometric searching, IP traceback, and network/grid security. He is a Compere Loveless Fellow and a member of the Fulbright Senior Specialist Roster, the Sigma Xi Scientific Research Honor Society, and the editorial boards of several top journals on algorithms. He is a recipient of the NSF Research Initiation Award, the DARPA Spirit of Technology Transfer Award, the Brown Univ. Award for Technological Innovation, the ACM Recognition of Service Award, and the Pond Award for Excellence in Undergraduate Teaching. Faculty Contact: Nancy Amato (amato [at] cs.tamu.edu) Computer Science Distinguished Lecturer Series:Emotional Intelligence Technology and the Death of ClippyRosalind W. Picard, Director, Affective Computing Research Group; Co-Director, Things That Think Consortium, M.I.T. Media Laboratory
4:10 p.m., Monday, March 19, 2007 AbstractSkills of emotional intelligence include the ability to recognize and respond appropriately to another person's emotion, and the ability to know when (not) to display emotion. This talk will demonstrate new advances at M.I.T. giving several of these intelligence skills to computers. For example, I will attempt to demonstrate (live) our newest system designed to recognize complex cognitive-affective states in real time from a person's head and facial movements. This technology can discern when you are concentrating or interested, agreeing or disagreeing, confused or thinking. Thus, a computer can be better equipped to discern when is a good time to interrupt, to show you new things, or to change its behavior. A wearable version of this system is currently being explored for helping people with autism (who have trouble reading these cues). I also will show several other examples enabling emotional intelligence to be used for improving human experience with technology. BiographyRosalind W. Picard is founder and director of the Affective Computing Research Group at the Massachusetts Institute of Technology (MIT) Media Laboratory and co-director of the Things That Think Consortium, the largest industrial sponsorship organization at the lab. She holds a Bachelors in Electrical Engineering with highest honors from the Georgia Institute of Technology, and Masters and Doctorate degrees, both in Electrical Engineering and Computer Science, from the Massachusetts Institute of Technology (MIT). She has been a member of the faculty at the MIT Media Laboratory since 1991, with tenure since 1998. Prior to completing her doctorate at MIT, she was a Member of the Technical Staff at AT&T Bell Laboratories where she designed VLSI chips for digital signal processing and developed new methods of image compression and analysis. She was honored as a Fellow of the IEEE in 2005. The author of over a hundred peer-reviewed scientific articles in multidimensional signal modeling, computer vision, pattern recognition, machine learning, and human-computer interaction, Picard is known internationally for pioneering research in affective computing and, prior to that, for pioneering research in content-based image and video retrieval. She is recipient (with Tom Minka) of a best paper prize for work on machine learning with multiple models (1998) and is recipient (with Barry Kort and Rob Reilly) of a "best theory paper" prize for their work on affect in human learning (2001). Her award-winning book, Affective Computing, (MIT Press, 1997) lays the groundwork for giving machines the skills of emotional intelligence. She and her students have designed and developed a variety of new sensors, algorithms, and systems for sensing, recognizing, and responding respectfully to human affective information, with applications in human and machine learning, health, and human-computer interaction. Faculty Contact: Andruid Kerne (andruid [at] cs.tamu.edu) |
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