Computer Science Events
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[PAST EVENT] A network-centric approach to data science: from distributed learning to social recommender systems
March 2, 2016
8am - 9am
Colloquium talk by Zhenming Liu, Princeton.
Title: A network-centric approach to data science: from distributed learning to social recommender systems
Speaker: Zhenming Liu
Abstract: Networks play important roles in various stages of a data science life cycle, including the design of scalable platforms, the collection of data, and the analysis of statistical models. I will talk about my efforts to develop a suite of network-based techniques in these stages. After briefly describing my work on designing scalable platforms for online machine learning algorithms and that for sampling data from the Web, I will discuss the details of a recent project that uses network analysis to study social recommender systems. A social recommender system leverages its users' social connections to improve recommendation service. The recommender system we have designed simultaneously maximizes (a) an individual's benefit from using a social network and (b) the network's efficiency in disseminating information. The design solution brings together techniques from spectral analysis, random walk theory, and large-scale optimization.
Bio: Zhenming Liu received his Ph.D. from Harvard University (working with Michael Mitzenmacher) and then spent two years as a postdoc at Princeton University (primarily working with Mung Chiang and Jennifer Rexford). Presently, he is a machine learning researcher for a quantitative hedge fund. Dr. Liu's research focus is the intersection of data science and network analysis; he designs both algorithms that analyze network structures inherent in the data (e.g., social and biological networks) and scalable platforms in support of big data analytics. He has received several awards for his research, including a Best Paper Runner Up at INFOCOM 2015 and a Best Student Paper Award at ECML/PKDD 2010.
Title: A network-centric approach to data science: from distributed learning to social recommender systems
Speaker: Zhenming Liu
Abstract: Networks play important roles in various stages of a data science life cycle, including the design of scalable platforms, the collection of data, and the analysis of statistical models. I will talk about my efforts to develop a suite of network-based techniques in these stages. After briefly describing my work on designing scalable platforms for online machine learning algorithms and that for sampling data from the Web, I will discuss the details of a recent project that uses network analysis to study social recommender systems. A social recommender system leverages its users' social connections to improve recommendation service. The recommender system we have designed simultaneously maximizes (a) an individual's benefit from using a social network and (b) the network's efficiency in disseminating information. The design solution brings together techniques from spectral analysis, random walk theory, and large-scale optimization.
Bio: Zhenming Liu received his Ph.D. from Harvard University (working with Michael Mitzenmacher) and then spent two years as a postdoc at Princeton University (primarily working with Mung Chiang and Jennifer Rexford). Presently, he is a machine learning researcher for a quantitative hedge fund. Dr. Liu's research focus is the intersection of data science and network analysis; he designs both algorithms that analyze network structures inherent in the data (e.g., social and biological networks) and scalable platforms in support of big data analytics. He has received several awards for his research, including a Best Paper Runner Up at INFOCOM 2015 and a Best Student Paper Award at ECML/PKDD 2010.