November 2013 - talks - lists.cs.princeton.edu

4:30pm Tue Nov 19 talk on routing algebras
by Jennifer Rexford 16 Nov '13

16 Nov '13

Speaker: Joao Sobrinho, Instituto de Telecomunicações Title: Algebra: A Tool for a Unifying View of Routing Protocol Behavior Date/time: 4:30-5:30pm, Tuesday November 19 Location: room 302 in the CS building Abstract: The Internet routing system is composed of millions of routers running one or more of several routing protocols---BGP, OPSF, IS-IS, RIP, EIGRP, DSR, AODV, OLSR, RPL---with the goal of guiding data-packets from sources to destinations. Beyond the specificity of every routing protocol, a number of questions are pertinent across wide classes of them, namely: • Does the routing protocol always terminate in a stable state devoid of forwarding loops and forwarding deflections? • Are the paths followed by data-packets optimal in some sense? • How much of the inherent resiliency of the network does the routing protocol exploit? • Does the routing protocol scale with the number of destinations? Routing selects paths for the transport of data-packets indirectly, through attributes they possess. Routing protocols operate by continually interleaving two processes: that of computing attributes of paths from characteristics of its constituent links; and that of choosing best attributes. The algebraic approach studies how the entwining of these two processes determines the global behavior of a routing protocol, providing answers to the questions above. In this talk, I will illustrate the unifying power of the algebraic approach by delving into the non-optimality of paths determined by EIGRP (Enhanced Interior Gateway Routing Protocol) and termination and resiliency issues raised by BGP (Border Gateway Protocol). In addition, I will survey recent applications of the algebraic approach, notably to the interconnection of routing instances and to route aggregation in the Internet. Bio: João Luís Sobrinho received his Licenciatura and Ph.D. degrees in Electrical and Computer Engineering from the Technical University of Lisbon, Portugal, in 1990 and 1995, respectively. During 1995 and 1996, he was a Member of the Technical Staff at Bell Labs, Nieuwegein, The Netherlands; he further consulted for Bell Labs, Murray Hill, NJ, from 1997 to 1999. He is currently Associate Professor at the Faculty of Engineering of the Technical University of Lisbon and a Researcher at the Telecommunications Institute. Prof. Sobrinho is a member of the IEEE and of the ACM. http://www.lx.it.pt/~jls/

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Colloquium speaker Michael Isard Monday Nov 4, 4:30pm
by Nicole E. Wagenblast 11 Nov '13

11 Nov '13

Practical Elegance: Designing programming models for large-scale systems Michael Isard , Microsoft Research Monday, November 11, 4:30pm Computer Science 105 The growing need for systems that operate on large datasets is well known, but most programming models for such systems are either too low level to appeal to non-expert programmers, or are specialized to tightly restricted problem domains. I will describe the evolution of a programming model, and its associated systems, that I have worked on at Microsoft Research. The model encourages programmers to describe an algorithm as a series of data-parallel steps, embedded within a familiar language and programming environment (C#/.NET). In order to write code this way programmers must think indirectly about data-dependencies, but can leave out the messy details of concurrency. We believe this elegantly balances our desire to insulate programmers from implementation details with the need for the system to automatically infer safe parallel and distributed execution strategies. We have been able to design systems that take programs written in this model and execute them efficiently on large datasets and clusters of hundreds of computers. As the model has evolved we have made it more expressive, so that it now encompasses both incremental and iterative computation, while keeping the ability to execute these richer programs efficiently, at scale. Our ultimate goal is to integrate data-parallelism seamlessly into all aspects of a general-purpose programming language. Michael Isard started out as a computer vision researcher, but for the last few years has mostly been building distributed execution engines and thinking about how to program them. He received his D.Phil in computer vision from the Oxford University Engineering Science Department in 1998, and worked at the Compaq Systems Research Center in Palo Alto for three years before joining Microsoft Research in Silicon Valley in 2002.

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Colloquium Speaker Barbbara Englehardt Wed Nov 6, 4:30pm
by Nicole E. Wagenblast 06 Nov '13

06 Nov '13

Bayesian models of structured sparsity for discovery of regulatory genetic variants Barbara Engelhardt , Duke University Wednesday, November 6th 4:30pm Computer Science 105 In genomic sciences, the amount of data has grown faster than statistical methodologies necessary to analyze those data. Furthermore, the complex underlying structure of these data means that simple, unstructured statistical models do not perform well. We consider the problem of identifying allelic heterogeneity, or multiple, functionally independent, co-localized genetic regulators of gene transcription. Sparse regression techniques have been critical to the discovery of allelic heterogeneity because of their computational tractability in large data settings. These traditional models are hindered by the substantial correlation between genetic variants induced by linkage disequilibrium. I describe a new model for Bayesian structured sparse regression. This model uses positive definite covariance matrices to incorporate the arbitrarily complex structure of the predictors directly into a Gaussian field to yield structure-aware sparse regression coefficients. This broadly applicable model of Bayesian structured sparsity enables more efficient parameter estimating techniques than models assuming independence would allow. On simulated data, we find that our approach substantially outperforms the state-of-the-art models and methods. We applied this model to a large study of expression quantitative trait loci, and found that our approach yields highly interpretable, robust solutions for allelic heterogeneity, particularly when the interactions between genetic variants are well approximated by an additive model.

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Distinguished Colloquium Speaker Susan Dumais Tues Nov 5 4:30pm Friend 006
by Nicole E. Wagenblast 05 Nov '13

05 Nov '13

Temporal Dynamics and Information Retrieval Susan Dumais , Microsoft Research Tuesday, November 5 4:30pm Friend Center 006 Many digital resources, like the Web, are dynamic and ever-changing collections of information. However, most tools developed for interacting with Web content, such as browsers and search engines, focus on a single static snapshot of the information. In this talk, I will present analyses characterizing how Web content changes over time, how people re-visit Web pages over time, and how re-visitation patterns are influenced by changes in user intent and content. These results have implications for many aspects of information management including crawling policy, ranking and information extraction algorithms, result presentation, and system evaluation. I will describe a prototype that supports people in understanding how the information they interact with changes over time, and new information retrieval models that incorporate the temporal dynamics to improve ranking. Finally, I will conclude with speculations about "slow search" and an overview of challenges that need to be addressed to fully incorporate temporal dynamics into information systems. Susan Dumais is a Distinguished Scientist and manager of the Context, Learning and User Experience for Search (CLUES) Group at Microsoft Research. Prior to joining Microsoft Research, she was at Bell Labs and Bellcore for many years, where she worked on Latent Semantic Analysis, interfaces for combining search and navigation, and organizational impacts of new technology. Her current research focuses on user modeling and personalization, context and search, temporal dynamics of information, and novel evaluation methods. She has worked closely with several Microsoft groups (Bing, Windows Desktop Search, SharePoint, and Office Online Help) on search-related innovations. Susan has published widely in the fields of information science, human-computer interaction and cognitive science, and holds several patents on novel retrieval algorithms and interfaces. Susan is an adjunct professor in the Information School at the University of Washington. She is Past-Chair of ACM's Special Interest Group in Information Retrieval (SIGIR), and serves on several editorial boards, technical program committees, and government panels. She was elected to the CHI Academy in 2005, an ACM Fellow in 2006, received the SIGIR Gerard Salton Award for Lifetime Achievement in 2009, and was elected to the National Academy of Engineering (NAE) in 2011. More information is available at her homepage, http://http://research.microsoft.com/en-us/um/people/sdumais/

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