Biographical Sketches of Committee Members

MICHAEL I. JORDAN, *Chair,* is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Department of Statistics at the University of California, Berkeley. He received a B.S. in psychology in 1978 from Louisiana State University, an M.S. in mathematics in 1980 from Arizona State University, and a Ph.D. in cognitive science in 1985 from the University of California, San Diego. His research interests are in the field of statistical machine learning, a field that bridges computation and statistics, with ties to information theory, signal processing, algorithms, control theory and optimization theory. One area of his research focus has been probabilistic graphical models, which blends probability theory and graph theory to represent uncertainty on interdependent collections of random variables. He developed new graphical model architectures that have had impact in various applied fields, including bioinformatics, computational vision, speech, natural language processing and information retrieval, and has contributed to the development of a new framework for inference in graphical models based on variational representations of probability distributions. Another area of focus has been nonparametric inference, including both Bayesian nonparametrics, where he developed new models based on the area of stochastic processes known as completely random measures, and frequentist nonparametrics, where he focused on kernel machines, spectral methods, dimension reduction and classification. He has also been interested in developing applications of machine learning to problems in distributed computer systems. In 2010, Dr. Jordan was elected to both the National Academy of Sciences and the National Academy of Engineering.

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B
Biographical Sketches of
Committee Members
MICHAEL I. JORDAN, Chair, is the Pehong Chen Distinguished Professor
in the Department of Electrical Engineering and Department of Statistics at
the University of California, Berkeley. He received a B.S. in psychology in
1978 from Louisiana State University, an M.S. in mathematics in 1980 from
Arizona State University, and a Ph.D. in cognitive science in 1985 from the
University of California, San Diego. His research interests are in the field
of statistical machine learning, a field that bridges computation and statis-
tics, with ties to information theory, signal processing, algorithms, control
theory and optimization theory. One area of his research focus has been
probabilistic graphical models, which blends probability theory and graph
theory to represent uncertainty on interdependent collections of random
variables. He developed new graphical model architectures that have had
impact in various applied fields, including bioinformatics, computational
vision, speech, natural language processing and information retrieval, and
has contributed to the development of a new framework for inference in
graphical models based on variational representations of probability distri-
butions. Another area of focus has been nonparametric inference, including
both Bayesian nonparametrics, where he developed new models based on
the area of stochastic processes known as completely random measures,
and frequentist nonparametrics, where he focused on kernel machines,
spectral methods, dimension reduction and classification. He has also been
interested in developing applications of machine learning to problems in
distributed computer systems. In 2010, Dr. Jordan was elected to both the
National Academy of Sciences and the National Academy of Engineering.
171

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172 APPENDIX B
KATHLEEN M. CARLEY is a professor in the School of Computer Science
at Carnegie Mellon University (CMU). She is the director of the Center
for Computational Analysis of Social and Organizational Systems, a uni-
versity-wide interdisciplinary center that brings together network analysis,
computer science and organization science and has an associated National
Science Foundation (NSF)-funded training program for Ph.D. students.
Her research combines cognitive science, social networks, and computer
science to address complex social and organizational problems. Her specific
research areas are dynamic network analysis, computational social and
organization theory, adaptation and evolution, text mining, and the impact
of telecommunication technologies and policy on communication, informa-
tion diffusion, disease contagion, and response within and among groups,
particularly in disaster or crisis situations. She and her team have developed
infrastructure tools for analyzing large-scale dynamic networks and various
multi-agent simulation systems. The infrastructure tools include the ORA, a
statistical toolkit for analyzing and visualizing multi-dimensional networks.
Another tool is AutoMap, a text-mining system for extracting semantic
networks from texts and then cross-classifying them using an organiza-
tional ontology into the underlying social, knowledge, resource, and task
networks. She is the founding co-editor of Computational Organization
Theory and has co-edited several books in the computational organizations
and dynamic network area.
RONALD R. COIFMAN is a professor of mathematics and computer
science at Yale University. His research interests include nonlinear Fourier
analysis, wavelet theory, singular integrals, numerical analysis and scat-
tering theory, real and complex analysis, and new mathematical tools for
efficient computation and transcriptions of physical data, with applications
to numerical analysis, feature extraction recognition, and de-noising. He is
currently developing analysis tools for spectrometric diagnostics and hyper-
spectral imaging. Dr. Coifman is a member of the American Academy of
Arts and Sciences and the National Academy of Sciences. He is a recipient
of the 1996 DARPA Sustained Excellence Award, the 1996 Connecticut
Science Medal, the 1999 Pioneer Award of the International Society for
Industrial and Applied Science, and the 1999 National Medal of Science.
DANIEL J. CRICHTON is a principal computer scientist and program
manager for the Earth Science Data System and Technology Directorate
and the Solar System Exploration Directorate at NASA’s Jet Propulsion
Laboratory (JPL), where he provides leadership in the development of
large-scale, scientific data systems for planetary, Earth, and other data-
intensive technology projects. He has served in numerous roles including
as principal investigator supporting the research and implementation of

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APPENDIX B 173
new novel approaches for dealing with the capture, management, distribu-
tion, and analysis of massive scientific data. He conceived of and built an
open-source software framework to enable large-scale data management
and sharing of scientific data across organizations that has been accepted
into the Apache Software Foundation. He has served on a number of com-
mittees for NASA, the National Institutes of Health, and other agencies.
He has authored more than 100 book chapters and papers on the topic of
data-intensive systems. He has a B.S. in information and computer science
from the University of California, Irvine, and an M.S. in computer science
from the University of Southern California.
MICHAEL J. FRANKLIN is the Thomas M. Siebel Professor of Computer
Science at the University of California, Berkeley, specializing in large-scale
data management applications and infrastructure. Dr. Franklin is a mem-
ber of the Database and Operating Systems and Networking Technology
groups. He is director of the Algorithms, Machines, and People Laboratory
(AMPLab), where he collaborates with students, postdoctoral researchers,
and faculty who specialize in cloud computing, statistical machine learn-
ing, networking, and other important topics necessary for building scalable
data-intensive systems. He is a co-founder of Truviso, a high-performance
analytics software company in Foster City, California.
ANNA C. GILBERT is a professor in the Department of Mathematics at
the University of Michigan. She has an S.B. degree from the University of
Chicago and a Ph.D. from Princeton University, both in mathematics. In
1997 Dr. Gilbert was a postdoctoral fellow at Yale University. From 1998
to 2004 she was a member of technical staff at AT&T Labs-Research in
Florham Park, New Jersey. Her research interests include analysis, prob-
ability, networking, and algorithms, and she is especially interested in
randomized algorithms with applications to harmonic analysis, signal and
image processing, networking, and massive data sets.
ALEX GRAY is director of the Fundamental Algorithmic and Statisti-
cal Tools Laboratory (FASTlab) at the Georgia Institute of Technology.
Dr. Gray received bachelor’s degrees in applied mathematics and computer
science from the University of California, Berkeley, and a Ph.D. in com-
puter science from Carnegie Mellon University. He worked in the Machine
Learning Systems Group of NASA’s JPL for 6 years. FASTlab works on
the problem of how to perform machine learning/data mining/statistics on
massive data sets and related problems in scientific computing and applied
mathematics. Employing a multidisciplinary array of technical ideas (from
machine learning, nonparametric statistics, convex optimization, linear
algebra, discrete algorithms and data structures, computational geometry,

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174 APPENDIX B
computational physics, Monte Carlo methods, distributed computing, and
automated theorem proving), his laboratory has developed the current
fastest algorithms for several fundamental statistical methods, and is in the
process of developing new machine learning methods for difficult aspects of
real-world data, such as in astrophysics and biology. This work has enabled
high-profile scientific results that have been featured in Science and Nature.
Dr. Gray has received an NSF CAREER award, two best-paper awards, and
two best-paper award nominations.
TREVOR HASTIE is a professor in the Department of Statistics at Stanford
University and the Division of Biostatistics of the Health, Research, and
Policy Department in the Stanford School of Medicine. His main research
contributions have been in the field of applied nonparametric regression
and classification, and he has co-written two books in this area, General-
ized Additive Models and Elements of Statistical Learning. He has also
made contributions to statistical computing, co-editing a large software
library on modeling tools in the S language (Statistical Models in S, 1992),
which form the basis for much of the statistical modeling in R and S-plus.
His current research focuses on applied problems in biology and genomics,
medicine, and industry, in particular data mining, prediction, and classifica-
tion problems.
PIOTR INDYK is a professor in the Department of Electrical Engineering
and Computer Science at Massachusetts Institute of Technology (MIT). He
joined MIT in September 2000 after earning a Ph.D. from Stanford Univer-
sity. Earlier, he received a magister degree from Uniwersytet Warszawski,
Poland, in 1995. Dr. Indyk’s research interests include computational geom-
etry (especially in high-dimensional spaces), algorithms using sublinear time
and/or space, and streaming algorithms. He is also interested in algorithmic
coding theory and pattern-matching problems.
THEODORE JOHNSON is a research scientist in the Database Research
Department at AT&T Labs-Research. He received a B.S. in mathematics
from the Johns Hopkins University in 1986 and a Ph.D. in computer science
from the Courant Institute of New York University in 1990. From 1990
through 1996, he was an assistant professor, and then an associate profes-
sor, in the Computer and Information Science and Engineering Department
at the University of Florida. In 2004 he received an AT&T Science and
Technology Award for his work in the Bellman database browser, and in
2010 he was made an AT&T fellow. He has co-authored two books, Dis-
tributed Operating Systems and Algorithms and Exploratory Data Mining
and Data Cleaning.

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APPENDIX B 175
DIANE LAMBERT is a research scientist at Google, Inc. She has previously
served as head of statistics and data mining research at Bell Laboratories
from 1997 to 2005 and was a member of its technical staff from 1994 to
1997. She was a tenured member of the faculty at CMU from 1980 to 1986
and was also a visiting associate professor at University of Chicago from
1984 to 1986. She has held numerous editorial and program committee
positions.
DAVID MADIGAN is a professor of statistics at Columbia University. He
received a B.A. in mathematical sciences (1984) and Ph.D. (1990) in sta-
tistics from Trinity College in Dublin, Ireland. He was previously the dean
of physical and mathematical sciences at Rutgers, The State University of
New Jersey. He has received numerous honors that include the Institute
of Mathematical Statistics Medallion Lecturer, fellow of the Institute of
Mathematical Statistics, and being named as the “36th Most Cited Math-
ematician in the World, 1995-2005.”
MICHAEL MAHONEY is an engineering research associate in the De-
partment of Mathematics at Stanford University. His research interests are
algorithmic and statistical aspects of modern large-scale data analysis; de-
sign and analysis of algorithms for matrix, graph, and regression problems;
statistical data analysis in large-scale scientific and Internet applications;
applications to the analysis of large social and information networks; ap-
plications to DNA microarray and single nucleotide polymorphism data;
and randomized algorithms for large linear algebra problems. Much of
his current research focuses on geometric network analysis, developing
a
pproximate computation and regularization methods for large informatics
graphs; applications in large social and information networks; and statisti-
cal data analysis of extremely large data sets. Recently, this work led to
improved algorithms for two classical linear algebra problems.
F. MILLER MALEY is a researcher on the staff at the Communications
Research Center (CRC), Princeton, a division of the Institute for Defense
Analysis that supports National Security Agency research interests. He
is also co-chair of the CRC’s SCAMP program on supercomputing. He
was a visiting research fellow at Princeton University from 1987 to 1990.
Dr. Maley received a B.S. in mathematics and physics from Amherst Col-
lege in 1983, a Ph.D. in computer science from MIT in 1987, and a Ph.D.
in mathematics from Princeton University in 1996. He is the author or
co-author of 17 classified papers. His awards include the NSF Mathemati-
cal Sciences Postdoctoral Research Fellowship (1987-1990) and Office of
Naval Research Graduate Fellowship (1983-1987).

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176 APPENDIX B
CHRISTOPHER OLSTON is a staff research scientist at Google, Inc. Previ-
ously, he was a principal research scientist at Yahoo! Research. His research
interest is data management, focusing especially on Web data management
challenges. Dr. Olston received his Ph.D. in computer science in 2003 from
Stanford University, supported by fellowships from the university and NSF.
He received his bachelor’s degree in electrical engineering and computer
sciences from the University of California, Berkeley, with highest honors.
He has previously held teaching and research positions at Yahoo! Research,
Carnegie Mellon University, Stanford University, Xerox Palo Alto Research
Center, the University of California, Berkeley, and Informix Software, Inc.
YORAM SINGER is a senior research scientist at Google, Inc. Before join-
ing Google, he was an associate professor at the School of Computer Sci-
ence and Engineering of Hebrew University of Jerusalem, and before that he
was a member of the technical staff at AT&T-Research. Dr. Singer received
his B.Sc. and M.Sc. degrees in computer science from the Technion and his
Ph.D. in computer science from Hebrew University.
ALEXANDER SANDOR SZALAY is a professor in the Department of
Physics and Astronomy of Johns Hopkins University. His research interests
are theoretical astrophysics and galaxy formation. His research interests are
multicolor properties of galaxies, galaxy evolution, the large-scale power
spectrum of fluctuations, gravitational lensing, pattern recognition and clas-
sification problems, the SDSS project, and large scalable databases. He is a
leader in the use of massive data as input for scientific research.
TONG ZHANG is a professor of statistics at Rutgers University. His
research interests are machine learning, statistical and numerical computa-
tion, and design and theoretical analysis of statistical algorithms. He has
worked extensively in large-scale data analysis and statistical modeling,
especially in text mining, natural language processing, search, and various
other Web applications. Dr. Zhang received a Ph.D. in computer science
from Stanford University in 1998. After graduation, he worked at IBM T.J.
Watson Research Center and then Yahoo! Research in New York.