September 2014 - talks - lists.cs.princeton.edu

Princeton Day of Statistics
by Nicole E. Wagenblast 29 Sep '14

29 Sep '14

Dear all, We are pleased to announce the 4th Princeton Day of Statistics. This bi-annual event will take place on Friday, October 17, 2014 in Sherrerd 101. Seven speakers will deliver talks on the most recent advances in statistics. * Marten Wegkamp (Cornell University) * Tony Cai (University of Pennsylvania) * Christopher Genovese (Carnegie Mellon University) * Jun Liu (Harvard University) * Regina Liu (Rutgers University) * Richard A. Davis (Columbia University) * Kung-Sik Chan (University of Iowa) For more information and registration, visit http://orfe.princeton.edu/statlab/events/pds Registration is required to attend, deadline is October 10th. Regards, Han Liu Assistant professor Operations Research & Financial Engineering Dept. -- Carol Smith Academic Assistant & Events Coordinator 609-258-9863 Fax: 609-258-4363 carols(a)princeton.edu

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4:30pm Tue Oct 7 talk on multipath routing in CS 402
by Jennifer Rexford 26 Sep '14

26 Sep '14

Speaker: Deep Medhi, University of Missouri-Kansas City Title: Is Multipath Routing Beneficial? Date/time: 4:30pm Tuesday October 7 Location: CS 402 Abstract: It's often believed that multipath routing is always beneficial. Taking a traffic engineering perspective, we consider commonly used goals such as congestion minimization, minimum average delay, and minimum cost routing. Using a well-known result from linear programming, we show that numbers of paths taken by all demands at optimality is limited by the total number of demands and links in a network. When all node pairs (demands) in a network have traffic, multipath routing essentially becomes single-path routing, especially as the network becomes large. Under certain traffic conditions, single-path routing is found to be optimal. We will also present results on a number of traffic scenarios and load conditions using topologies used by ISPs and in data center networks. These observations are counter-intuitive due to our commonly held belief about multipath routing. Bio: Deep Medhi is Curators' Professor in the Department of Computer Science and Electrical Engineering at the University of Missouri-Kansas City, USA. He received B.Sc. in Mathematics from Cotton College, Gauhati University, India, M.Sc. in Mathematics from the University of Delhi, India, and his Ph.D. in Computer Sciences from the University of Wisconsin-Madison, USA. Prior to joining UMKC in 1989, he was a member of the technical staff at AT&T Bell Laboratories. He was an invited visiting professor at the Technical University of Denmark, a visiting research fellow at Lund Institute of Technology, Sweden, a research visitor at University of Campinas, Brazil under the Brazilian Science Mobility Program and served as a Fulbright Senior Specialist. He is the Editor-in-Chief of Springer’s Journal of Network and Systems Management, and is on the editorial board of IEEE/ACM Transactions on Networking, IEEE Transactions on Network and Service Management, and IEEE Communications Surveys & Tutorials. He is co-author of the books, Routing, Flow, and Capacity Design in Communication and Computer Networks (2004) and Network Routing: Algorithms, Protocols, and Architectures (2007), both published by Morgan Kauffman/Elsevier.

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Colloquium Speaker Partha Mitra Wed Sept 17, 4:30pm
by Nicole E. Wagenblast 17 Sep '14

17 Sep '14

Mapping Neural Circuits in the Whole Mouse Brain Partha Mitra, Cold Spring Harbor Wednesday, September 17, 4:30pm Computer Science 105 Neuroanatomy, a century old subject, is currently undergoing a computational technology-driven make-over. Fall in data storage prices as well as automated equipment for digital histology and imaging, has made it possible for entire mammalian brains to be digitized using light microscopy. The talk will describe an ongoing effort to systematically uncover the neural circuit architecture of the whole mouse brain by scaling up classical neuroanatomical methods ( http://mouse.brainarchitecture.org ). The resulting petabyte-sized data sets are larger than any previously encountered in neuroscience and pose new and interesting data-analysis challenges. Partha Mitra received his B Sc in physics from Presidency College, Calcutta and his PhD in theoretical physics from Harvard University. He was an Assistant Professor of Physics at Caltech (1996) and a member of Theoretical Physics department at Bell Laboratories (1995-2003). He is currently Crick-Clay Professor of Biomathematics at Cold Spring Harbor Laboratory. His research combines experimental, theoretical and computational approaches to gain an understanding of how brains work.

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Electrical Engineering Seminar Speaker Dr. Aryeh Kontorovich 9/18-4:30pm
by Nicole E. Wagenblast 15 Sep '14

15 Sep '14

DEPARTMENT OF ELECTRICAL ENGINEERING SEMINAR SERIES Speaker: Dr. Aryeh Kontorovich Ben-Gurion University of the Negev Title: Good Margins Make Good Neighbors Date: Thursday, September 18, 2014 Time: 4:30 p.m. Room: E-Quad, B205 Host: Prof. Sergio Verdu Abstract: Although well-known by practitioners to be an effective classification tool, nearest-neighbor methods have been somewhat neglected by learning theory of late. The goal of this talk is to revive interest in this time-tested technique by recasting it in a modern perspective. We will present a paradigm of margin-regularized 1-nearest neighbor classification which: (i) is Bayes-consistent (ii) yields simple, usable finite-sample error bounds (iii) provides for very efficient algorithms with a principled speed-accuracy tradeoff (iv) allows for near-optimal sample compression. Further extensions include multiclass, regression, and metric dimensionality reduction. I will argue that the regularized 1-nearest neighbor is superior to k-nearest neighbors in several crucial statistical and computational aspects. Based on a series of works with: Lee-Ad Gottlieb, Robert Krauthgamer, Roi Weiss Bio: Aryeh Kontorovich received his undergraduate degree in mathematics with a certificate in applied mathematics from Princeton University in 2001. His M.Sc. and Ph.D. are from Carnegie Mellon University, where he graduated in 2007. After a postdoctoral fellowship at the Weizmann Institute of Science, he joined the Computer Science department at Ben-Gurion University of the Negev in 2009 as an assistant professor; this is his current position. His research interests are mainly in machine learning, with a focus on probability, statistics, automata theory and metric spaces.

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Aaron Wong Pre-FPO 9/16/14 @ 9:30am in CS 402
by Nicki Gotsis 15 Sep '14

15 Sep '14

Aaron Wong will present his Pre FPO Tuesday, September 16 at 9:30am in CS 402. The members of his committee are Olga Troyanskaya, advisor; Mona Singh, reader; Barbara Engelhardt, reader; Moses Charikar, non-reader; Kai Li, non-reader. The title and abstract are below. All are welcome to join. Title: Tissue- and disease-specific analysis of genome-scale data to inform biological experiments Biologists using modern experimental methods are generating a massive number of genome-scale datasets. In particular, the rate of large-scale data creation in most organisms is quickly outpacing biologists’ ability to perform detailed follow-up experiments. We address this growing disparity by developing methods to improve functional annotation coverage, methods to predict tissue specific function even though most genomic data are not resolved to specific tissues, and tools enabling biologists to make experimental discoveries. We demonstrate a method to systematically transfer experimental knowledge by identifying a gene’s “functional analogs” based on both sequence similarity and pathway partners inferred from high-throughput experimental data. Our methodology improves the coverage and accuracy of function predictions across six diverse organisms and machine learning methods. We created IMP (imp.princeton.edu), a web server that enables biologists to explore multi-species functional predictions and relationships for seven organisms, and worked with zebrafish biologists to experimentally validate gene predictions for heart left/right asymmetry. Tissue and cell-type identity lie at the core of human physiology and disease. However, we still lack tools to systematically explore the genes and interactions that shape specialized cellular functions. We develop a method that leverages large data compendia to build genome-scale tissue-specific functional maps in human by integrating thousands of genome-scale datasets. Our method can extract both functional and tissue/cell-type signals even when genomic data are not resolved for the tissue. We demonstrate the broad applicability of these networks by applying them to predict IL1B responsive genes and re-prioritize genome-wide association data. Finally, we created GIANT (giant.princeton.edu), which provides access to functional networks for 144 tissues/cell-types, and enables multi-network comparisons. Almost all functional genomic methods require biologists to pose biological questions as sets of genes of interest. However, biologists are generating and trying to make sense of massive high-throughput datasets, and their biological questions can be more precisely addressed within the biological context of their experiment. We develop a dataset-driven method for integration and search of gene expression compendia and show substantial predictive improvement in identifying functionally related co-expressed genes and experimentally similar datasets.

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Omri Weinstein will present his pre-FPO Sept 16, 2014 at 11am in CS Room 401 (Title: Interactive Information Complexity and Applications)
by Nicki Gotsis 11 Sep '14

11 Sep '14

Omri Weinstein will present his pre-FPO on September 16, 2014 at 11am in CS Room 401. The members of his committee are: Mark Braverman, advisor; Anup Rao(University of Washington), reader; Zeev Dvir, reader; Sanjeev Arora, non-reader; Michael Saks(Rutgers), non-reader. Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Interactive Information Complexity and Applications Abstract: ======= Few models in the history of theoretical computer science have affected the field in so many ways as profoundly as communication complexity. With applications in nearly every area of computer science, this model constitutes one of the few known techniques for proving unconditional lower bounds - the holy grail of complexity theory. Developing tools in communication complexity is therefore a promising approach for making progress in other computational models such as streaming, property testing, distributed computing, circuit complexity and data structures. One striking example of such tool is information theory, introduced by Shannon in the late 1940's in the context of the one way data transmission problem. Shannon's work revealed the intimate connection between information and communication, namely, that the amortized transmission cost of a random message is equal to the amount of information it contains. Classical information theory, however, does not readily convert to interactive setups, where two (or more) parties must engage in a multi-round conversation in order to accomplish a desirable task. The goal of our ongoing research is to extend this theory, develop the right tools, and understand how information behaves in interactive setups, such as the communication complexity model. Our main measure if interest is the Information Complexity of a function, which informally captures the least amount of information the parties need to disclose each other about their inputs in order to solve the underlying task. We demonstrate the broad power of this theory via applications to communication complexity, data streaming, circuit lower bounds, privacy and economics. In particular, I will discuss how information complexity helped us make progress on some of the most fundamental questions in communication complexity, including the limits of parallel computation, interactive compression, and the KRW conjecture.

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Fwd: EE SEMINAR : Thursday September 18th, 4:30pm, E-Quad B205- Aryeh Kontorovich-Good Margins Make Good Neighbors
by Jennifer Rexford 10 Sep '14

10 Sep '14

An EE seminar that may be of interest to folks in CS, too... Begin forwarded message: > From: "Lisa R. Lewis" <ll2(a)Princeton.EDU> > Subject: EE SEMINAR : Thursday September 18th, 4:30pm, E-Quad B205- Aryeh Kontorovich-Good Margins Make Good Neighbors > Date: September 10, 2014 at 10:29:06 AM EDT > To: ee-seminar(a)Princeton.EDU > Reply-To: "Lisa R. Lewis" <ll2(a)Princeton.EDU> > > > > > DEPARTMENT OF > ELECTRICAL ENGINEERING > SEMINAR SERIES > > > > > Speaker: Dr. Aryeh Kontorovich > Ben-Gurion University of the Negev > Title: Good Margins Make Good Neighbors > Date: Thursday, September 18, 2014 > Time: 4:30 p.m. > Room: E-Quad, B205 > Host: Prof. Sergio Verdu > > > Abstract: Although well-known by practitioners to be an effective classification tool, nearest-neighbor methods have been somewhat neglected by learning theory of late. The goal of this talk is to revive interest in this time-tested technique by recasting it in a modern perspective. We will present a paradigm of margin-regularized 1-nearest neighbor classification which: (i) is Bayes-consistent (ii) yields simple, usable finite-sample error bounds (iii) provides for very efficient algorithms with a principled speed-accuracy tradeoff (iv) allows for near-optimal sample compression. Further extensions include multiclass, regression, and metric dimensionality reduction. I will argue that the regularized 1-nearest neighbor is superior to k-nearest neighbors in several crucial statistical and computational aspects. > > Based on a series of works with: Lee-Ad Gottlieb, Robert Krauthgamer, Roi Weiss > > > > Bio: Aryeh Kontorovich received his undergraduate degree in mathematics with a certificate in applied mathematics from Princeton University in 2001. His M.Sc. and Ph.D. are from Carnegie Mellon University, where he graduated in 2007. After a postdoctoral fellowship at the Weizmann Institute of Science, he joined the Computer Science department at Ben-Gurion University of the Negev in 2009 as an assistant professor; this is his current position. His research interests are mainly in machine learning, with a focus on probability, statistics, automata theory and metric spaces. > >

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