October 2007 - talks - lists.cs.princeton.edu

1:30 Thu Nov 1 talk on anomaly detection in large networks using approximation techniques
by Jennifer Rexford 10 Oct '07

10 Oct '07

SPEAKER: Nina Taft, Intel Research TITLE: Anomaly Detection in Large Networks using Approximation Techniques DATE/TIME: 1:30-2:30pm Thu Nov 1 PLACE: room 402 in the CS Building ABSTRACT: A tremendous enthusiasm for amassing enormous amounts of network measurement data has spurred the development of numerous applications that incorporate data mining techniques. In this talk we question the hidden assumption in these applications that one needs to collect "all the data all the time". We consider this question in the context of an anomaly detection application. We study the popular "Subspace method detector" that is based on PCA analysis. This method normally collects data from many parts of the network, centralizes the data, and then analyzes it to uncover anomalies. In our research, we ask whether we can throw away some of the data. Can we still do anomaly detection accurately without all the data? To avoid backhauling large amounts of data through networks, we present a framework that couples filtering at local monitors with centralized detectors that can operate on approximate views of the global data (i.e. network state). We show that the errors made by the central detector - due to the use of approximate data - can be upper bounded using matrix perturbation theory. The challenge is to design the filtering parameters; these are determined by the bound on detection errors and the criteria being tracked for detection. Our approximate anomaly detector can detect anomalies with 80 to 90% less data than the original method, and incurs less than a 1% reduction in detection accuracy. Finally, we comment on issues and future directions for data reduction in the context of anomaly detection. BIO Nina Taft is currently a senior research scientist at Intel Research Berkeley. At Intel she works focuses on enterprise network traffic characterization, anomaly detection at both the enterprise and host level, and approximation techniques for data mining algorithms. Prior to joining Intel, Nina worked at Sprint for 5 years in the IP Group working on backbone Internet measurement. She conducted research in various areas such as traffic matrix estimation, traffic characterization, routing protocols and IP-over-WDM network design problems. Prior to Sprint, Nina worked at SRI International for four years. There her work focused on congestion control and QoS routing in ATM networks. She received her PhD degree from the University of Berkeley in 1994. Nina is currently serving as an associate editor for the IEEE Transactions on Networking (ToN) journal, was SIGCOMM 2007 PC co-Chair, and is a member of the ACM Internet Measurement Conference (IMC) steering committee.

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Talk today
by Ginny Hogan 10 Oct '07

10 Oct '07

Eric Xing, CMU, will present his talk, "Probabilistic Graphical Models and Algorithms For Integrative Bioinformatics" at 4:15pm today, Computer Science Room 105 Refreshments will be available at 3:30 in the tea room -

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Eric Xing talk 4:15pm TODAY
by Olga Troyanskaya 10 Oct '07

10 Oct '07

Please come to an exciting colloquium talk by Eric Xing from CMU. Eric is speaking today (Wednesday, October 10) at 4:15 P.M. in the Small Auditorium (Room 105). Title: "Probabilistic Graphical Models and Algorithms for Integrative Bioinformatics" Abstract: Probabilistic graphical model is a formalism that exploits the conjoined talents of graph theory and probability theory to build complex models out of simpler pieces. It offers a powerful language to elegantly define expressive distributions under complex scenarios in high-dimensional space, and provides a systematic computational framework for probabilistic inference. These virtues have particular relevance in bioinformatics, where many core inferential problems e.g., linkage analysis, phylogenetic analysis, network reconstruction are already naturally expressed in probabilistic terms, and must deal with experimental data with complex structure and temporal and/or spatial dynamics. I will discuss our recent work on graphical model inferential methodology in three areas in bioinformatics: (1) Population structure and recombination hotspot inference, using a novel approach based on Dirichlet process priors. I present a hidden Markov version of the Dirichlet process which allows us to infer recombination events among haplotypes in an "open" ancestral space. (2) Comparative genomics prediction of imperfectly conserved transcription factor binding sites, where multi-resolution phylogenetic inference combines with Markovian inference to provide sensitive detection of motifs and their evolutionary turnovers in eleven Drosophila species. (3) Reverse-engineering of temporally rewiring networks from gene expression time courses, where a novel hidden temporal exponential random graph model is employed to model temporal evolution of network topologies during a biological process, and to facilitate the inference of transient (rather than a single universal) regulatory circuitry underlying each time-point of the microarray time series. Bio: Eric Xing is an assistant professor in the Machine Learning Department, the Language Technology Institute, and the Computer Science Department within the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems; and for building computational intelligence systems involving automated learning, reasoning, and decision-making in open, evolving possible worlds. Professor Xing received his B.S. in Physics from Tsinghua University, his first Ph.D. in Molecular Biology and Biochemistry from Rutgers University, and then his second Ph.D. in Computer Science from UC Berkeley. He has been a member of the faculty at Carnegie Mellon University since 2004, and his current work involves, 1) graphical models, Bayesian methodologies, inference algorithms, and optimization techniques for analyzing and mining high-dimensional, longitudinal, and relational data; 2) computational and comparative genomic analysis of biological sequences, systems biology investigation of gene regulation, and statistical analysis of genetic variation, demography and disease linkage; and 3) application of statistical learning in text/image mining, vision, and machine translation. -- ______________________________________________________ Olga Troyanskaya, Ph.D. Assistant Professor Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics Princeton University, NJ 08544 (609)258-1749(ph) (609)258-1771(f) ______________________________________________________

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Talk Wednesday - October 10
by Ginny Hogan 08 Oct '07

08 Oct '07

Eric Xing will present his talk "Probabilistic Graphical Models and Algorithms for Integrative Bioinformatics" Wednesday, October 10, at 4:15pm Computer Science Room 105

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Refreshments -
by Ginny Hogan 03 Oct '07

03 Oct '07

Refreshments are now available in the tea room - followed by Peter Wayner's talk Policing Online Games -- Defining A Strong, Mutually Verifiable Reality in Virtual Games at 4:15 Computer Science Room 105.

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Colloquium speaker today -
by Ginny Hogan 03 Oct '07

03 Oct '07

Peter Wayner will present his talk "Policing Online Games - Defining A Strong, "Mutually Verifiable Reality in Virtual Games" today at 4:15pm Computer Science Room 105 - Refreshments will be in the tea room at 3:30 -

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Speaker Wednesday - Oct 3rd -
by Ginny Hogan 01 Oct '07

01 Oct '07

Peter C. Wayner, will present his talk "Policing Online Games - Defining a Strong, Mutually Verifiable Reality in Virtual Games" On Wednesday, Oct 3rd at 4:15pm - COS 105 Refreshments will be in the tea room at 3:30pm -

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