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\documentclass[runningheads,a4paper]{llncs}

\usepackage{times}
\usepackage{amssymb}
\setcounter{tocdepth}{3}
\usepackage{graphicx}

\usepackage{url}
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\begin{document}

\mainmatter  % start of an individual contribution

% first the title is needed
\title{Analyzing Text and Social Network Data with Probabilistic Models}

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\titlerunning{Analyzing Text and Social Network Data with Probabilistic Models}

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\author{Padhraic Smyth}
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\authorrunning{Padhraic Smyth}

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% unless you accept that it will be published
\institute{University of California, Irvine, USA\\
\email{smyth@ics.uci.edu}\\ 
\url{http://www.ics.uci.edu/~smyth/}
}

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\toctitle{Analyzing Text and Social Network Data with Probabilistic Models}
\tocauthor{Smyth, Padhraic}
\maketitle


\section*{Abstract}

Exploring and understanding large text and social network data sets is
of increasing interest across multiple fields, in computer science,
social science, history, medicine, and more. This talk will present an
overview of recent work using probabilistic latent variable models to
analyze such data. Latent variable models have a long tradition in
data analysis and typically hypothesize the existence of simple
unobserved phenomena to explain relatively complex observed data. In
the past decade there has been substantial work on extending the scope
of these approaches from relatively small simple data sets to much
more complex text and network data. We will discuss the basic concepts
behind these developments, reviewing key ideas, recent advances, and
open issues. In addition we will highlight common ideas that lie
beneath the surface of different approaches including links (for
example) to work in matrix factorization. The concluding part of the
talk will focus more specifically on recent work with temporal social
networks, specifically data in the form of time-stamped events between
nodes (such as emails exchanged among individuals over time).

\section*{Bio}

Padhraic Smyth is a Professor at the University of California, Irvine,
in the Department of Computer Science with a joint appointment in
Statistics, and is also Director of the Center for Machine Learning
and Intelligent Systems at UC Irvine. His research interests include
machine learning, data mining, pattern recognition, and applied
statistics and he has published over 150 papers on these topics. He
was a recipient of best paper awards at the 2002 and 1997 ACM SIGKDD
Conferences, received the ACM SIGKDD Innovation Award in 2009, and was
named a AAAI Fellow in 2010. He is co-author of Modeling the Internet
and the Web: Probabilistic Methods and Algorithms (with Pierre Baldi
and Paolo Frasconi in 2003), and co-author of Principles of Data
Mining, MIT Press (with David Hand and Heikki Mannila in 2001).
Padhraic has served in editorial and advisory positions for journals
such as the Journal of Machine Learning Research, the Journal of the
American Statistical Association, and the IEEE Transactions on
Knowledge and Data Engineering. While at UC Irvine he has received
research funding from agencies such as NSF, NIH, IARPA, NASA, and DOE,
and from companies such as Google, IBM, Yahoo!, Experian, and
Microsoft. In addition to his academic research he is also active in
industry consulting, working with companies such as eBay, Yahoo!,
Microsoft, Oracle, Nokia, and AT\&T, as well as serving as scientific
advisor to local startups in Orange County. He also served as an
academic advisor to Netflix for the Netflix prize competition from
2006 to 2009.  Padhraic received a first class honors degree in
Electronic Engineering from National University of Ireland (Galway) in
1984, and the MSEE and PhD degrees (in 1985 and 1988 respectively) in
Electrical Engineering from the California Institute of
Technology. From 1988 to 1996 he was a Technical Group Leader at the
Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC
Irvine since 1996.

\end{document}
