Fall 2013 Events

We have an exciting lineup of speakers for our Fall 2013 Seminar Series. Be sure to check them out below and reserve your spot early!

All of our events are free and open to the public. If you would like to RSVP, you can do so at cunydatamining.eventbrite.com. Alternatively, you can email us with your name, organizational affiliation (if any), and the event name(s) at datamining@gc.cuny.edu.

We look forward to seeing you at the event!

FORTHCOMING EVENTS

SEPTEMBER 27, 2013, 12 Noon @ The Graduate Center, Room 6112

How to see 2 million Instagram photos? Visualizing patterns in art, films, mass media, and user-generated content.

Speaker: Professor Lev Manovich, The Graduate Center, CUNY 3181071223_aa4132bae0

Bio: Lev Manovich is the author of Software Takes Command (Bloomsbury Academic, 2013), Soft Cinema: Navigating the Database (The MIT Press, 2005), and The Language of New Media (The MIT Press, 2001) which is described as “the most suggestive and broad ranging media history since Marshall McLuhan.” Manovich is a Professor at The Graduate Center, CUNY and a Director of the Software Studies Initiative at CUNY and California Institute for Telecommunication and Information (Calit2).

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OCTOBER 11, 2013, 9 AM to 5 PM @ The Graduate Center, Room 4102

Workshop on Sequence Analysis and Traminer.

Speaker: Mr. Alexis Gabadinho, Scientific Collaborator at the University of Genevagabadinho_pic

Bio: Alexis Gabadinho holds a postgraduate diploma in demography. He is a scientific collaborator at the Institute for Demographic and Life Course Studies at the University of Geneva where he is finishing a PhD on methods for sequence analysis. He is also a junior researcher at the Life Course and Inequality Research Center at the University of Lausanne.

He is a developer of the TraMineR R package for sequence analysis and has taught sequence analysis in doctoral schools at the University of Bristol, Geneva, Lausanne and Lille and in conferences and postgraduate courses. His research interests are the application of data-mining methods in social sciences and the development of methods for categorical state sequences analysis. He worked in particular on measures of sequence complexity and methods for summarizing sets of sequences. His current research is focused on the development of Markovian model oriented methods for sequence analysis that are made available in the PST R package.

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OCTOBER 25, 2013, 12 Noon @ The Graduate Center, Room 6112

Featurizing Text: Converting Text into Predictors for Regression Analysis.

Speaker: Professor Robert Stine, Wharton School of the University of Pennsylvania  stine-pic

Bio: Robert Stine is Professor of Statistics in the Wharton School of the University of Pennsylvania.  His research spans a variety of areas with practical applications, ranging from forecasting and spatial temporal models to fundamentals of multiple testing and methods for text analysis. Recent projects consider methods for selecting factors for predictive models from large databases, with particular relevance to the selection of factors that produce cost-effective decisions.  These methods are crucial in the development of predictive models in data mining.  His research has appeared in numerous academic journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, and the Annals of Statistics.  His teaching has been recognized by awards in both the Wharton MBA and Undergraduate programs, and he is the co-author of a recent textbook Business Statistics: Decision Making and Analysis.

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NOVEMBER 22, 2013, 12 Noon @ The Graduate Center, Room 6112

Optimal Dissemination on Graphs: Theory and Algorithms.

Speaker: Professor Hanghang Tong, City College, CUNY  Hanghang Tong - pic

Bio: Hanghang Tong is currently an Assistant Professor of  Computer Science at City College, City University of New York. Before that, he was a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degrees in Machine Learning from Carnegie Mellon University in 2008 and 2009, respectively. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including best paper award in CIKM 2012, best paper award in SDM 2008 and best research paper award in ICDM 2006. He has published over 70 referred articles and more than 20 patents. He has served as a program committee member in top data mining, databases and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc).