INVITED
SPEAKERS
-------------------------------------
Keynotes Speakers:
Jerome H.
Friedman (Stanford Univ.)
Andrew McCallum (Univ. of Massachusetts)
Robert Schapire (Princeton Univ.)
Invited Speakers:
Hang Li (Microsoft Research Asia)
Tie-Yan Liu (Microsoft Research Asia)
John Langford (Yahoo Research)
Tiefeng Jiang (Univ. of Minnesota)
Partha Niyogi (Univ. of Chicago)
Alex Smola (Univ. of Australia)
Martin Wainwright (UC Berkeley)
Bin Yu (UC Berkeley & Peking Univ.)
Alice Zheng (Carnegie Mellon Univ.)
Chang-Shui Zhang (Tsinghua Univ.)
Tong Zhang (Yahoo Research)
Zhi-Hua Zhou (Najing Univ.)
Biography
Robert Schapire
Robert
Schapire received his ScB in math and computer science from
Brown University in 1986, and his SM (1988) and PhD (1991) from
MIT under the supervision of Ronald Rivest. After a short
post-doc at Harvard, he joined the technical staff at AT&T Labs
(formerly AT&T Bell Laboratories) in 1991 where he remained for
eleven years. At the end of 2002, he became a Professor of
Computer Science at Princeton University.
His awards include the 1991 ACM Doctoral Dissertation Award, the
2003 Goedel Prize and the 2004 Kanelakkis Theory and Practice
Award (both of the last two with Yoav Freund). His main research
interest is in theoretical and applied machine learning.
Tong Zhang
Tong Zhang
is a principal research scientist at Yahoo and an associate
professor at Rutgers University. His research interests include
machine learning, numerical algorithms, their theoretical
analysis and applications. He received a B.A. in mathematics and
computer science from CornellUniversity in 1994 and a Ph.D. in
computer science from Stanford University in 1998. After being a
research staff member of IBM T.J. Watson Research Center in
Yorktown Heights, New York, he joined Yahoo in 2005.
Bin Yu
Bin Yu is
Chancellor's Professor of Statistics and Professor of Electrical
Engineering & Computer Science, at University of California at
Berkeley. She is also a co-founding director of the Microsoft
Lab on Statistics and Information Technology at Peking
University.
Her current research interests are machine learning, information
theory, and data modeling for problems from remote sensing,
sensor networks, neuroscience, nano technology, and finance.
She is a Fellow of IEEE, IMS and ASA. She was a Guggenheim
Fellow in 2006, and a Miller Research Professor in Spring 2004
at UC Berkeley. She was a co-recipient of the 2006 Best Paper
Award of IEEE Signal Processing Society.
She is currently a ChangJiang Chair Professor at Peking
University, and on the editorial boards of Journal of Machine
Learning Research, Journal of American Statistical Society,
Statistica Sinica, and Tecnometrics, among others
Jerome H. Friedman
Jerome H.
Friedman is Professor of Statistics at Stanford University.
Through 2006 he was also leader of the Computation Research
Group at the Stanford Linear Accelerator Center. He received his
Ph.D degree in physics from the University of California at
Berkeley. His primary research interests involve statistical
machine learning. He has coauthored two books on the subject and
has invented or coinvented several widely used machine learning
methods including decision trees (CART), multivariate adaptive
regression splines (MARS), gradient boosting (MART), and the
COSA clustering algorithm. Bibliography can be found at
http://www-stat.stanford.edu/~jhf.
Alice Zheng
Alice
Zheng received her Ph.D. from UC Berkeley in 2005 and is
currently a postdoctoral fellow at Carnegie Mellon University.
Her interests lie in applied machine learning, in particular to
computer systems, software, and networks.Current projects
include statistical software debugging, performance diagnosis of
distributed file systems, and modeling social networks.
Tie-Yan Liu
Tie-Yan
Liu is a researcher at Microsoft Research Asia. His current
research interests include learning to rank for information
retrieval, infrastructure and algorithms for large-scale graph
mining, and anti-spam for Web search. Dr. Liu is very active in
the research communities. So far, he has more than 50 quality
papers published in referred international conferences and
journals and has over 30 filed US/international patents or
pending applications. He was the winner of the Most Cited Paper
Award for the Journal of Visual Communication and Image
Representation. He has served on the program committees for more
than 20 international conferences, such as SIGIR, ICSE, ICDM,
and ICIP. He is also a reviewer for a dozen international
journals. Prior to joining Microsoft, Dr. Liu obtained his B.S.,
M.S., and Ph.D. from the Department of Electronic Engineering,
Tsinghua University, where his research efforts were devoted to
video coding and multi-media content analysis. During his
studies at Tsinghua University, he also worked as research
assistant for the City University of Hong Kong and the Hong Kong
Polytechnic University. He has been a member of IEEE since 1999.
Hang Li
Hang Li is
research manager at Microsoft Research Asia. He is also adjunct
professor at Peking University, Xian Jiaotong University, and
Nankai University. His research interests include statistical
machine learning, natural language processing, information
retrieval, and data mining. He graduated from Kyoto University
and earned his PhD from the University of Tokyo. Hang has many
publications in international journals and conferences. He is
associate editor of ' ACM Transactions on Asian Language
Information Processing (TALIP)' , and in editorial board of
'Journal for Computer and Science Technology', 'Computational
Linguistics and Chinese Language Processing', etc. His recent
academic activities include program committee co-chair of
PAKDD'07 and program committee co-chair of AIRS'08. Hang has
been working on development of several products. These include
Microsoft SQL Server Text Mining, Microsoft Office 2007
SharePoint Search, and Microsoft Live Search.
Andrew McCallum
Andrew
McCallum is an Associate Professor and Director of the
Information Extraction and Synthesis Laboratory in the Computer
Science Department at University of Massachusetts Amherst. He
was previously Vice President of Research and Development at
WhizBang Labs, a company that used machine learning for
information extraction from the Web. In the late 1990's he was a
Research Scientist and Coordinator at Justsystem Pittsburgh
Research Center, where he spearheaded the creation of CORA, an
early research paper search engine that used machine learning
for spidering, extraction, classification and citation analysis.
McCallum received his PhD from the University of Rochester in
1995, followed by a post-doctoral fellowship at Carnegie Mellon
University. He is currently an action editor for the Journal of
Machine Learning Research, and on the board of the International
Machine Learning Society. For the past ten years, McCallum has
been active in research on statistical machine learning applied
to text, especially information extraction, coreference,
document classification, clustering, finite state models,
semi-supervised learning, and social network analysis. New work
on search and bibliometric analysis of open-access research
literature can be found at http://rexa.info. McCallum's web
page:
http://www.cs.umass.edu/~mccallum.
Martin Wainwright
Martin
Wainwright is currently an assistant professor at University of
California at Berkeley, with a joint appointment between the
Department of Statistics and the Department of Electrical
Engineering and Computer Sciences. He received the Ph.D. degree
from Massachusetts Institute of Technology (MIT), for which he
was awarded the George M. Sprowls Award. His research interests
include statistical signal processing, statistical machine
learning, and information theory.
Recently, he has received an NSF CAREER award (2006), an Alfred
P. Sloan Foundation Fellowship (2005), and an Okawa Foundation
Research Fellowship (2005).
Zhi-Hua Zhou
Zhi-Hua
Zhou is currently Cheung Kong Professor and Head of the LAMDA
group at Nanjing University, China. He has wide research
interests, mainly including artificial intelligence, machine
learning, data mining, information retrieval and pattern
recognition. In particular, he has been active in research of
ensemble learning, multi-instance learning, semi-supervised
learning, and application to content-based image retrieval
during the past years. He is on the editorial boards of six
journals and served as guest editor for four journals. He was
the program chair of PAKDD¡¯07, vice chair of ICDM¡¯06 and
PRICAI¡¯06, and has been appointed as the program co-chair of
PRICAI¡¯08. He has won various awards or honors. He is the chair
of the CAAI Machine Learning Society and the vice chair of the
CCF Artificial Intelligence & Pattern Recognition Society.
Webpage:
http://cs.nju.edu.cn/zhouzh/
John Langford
John
Langford is a researcher specializing in Machine Learning and
Learning Theory at Yahoo Research. He was previously at TTI-Chicago
(as a Research Assistant Professor), IBM Research (on the Herman
Goldstine Fellowship), UPenn (as a Postdoc with Michael Kearns),
Carnegie Mellon University (for a PhD in Computer Science), and
Caltech (for a double major in Physics and Computer Science).
John's interests include the theory of statistical learning,
online learning, learning reduction, and active learning, along
with broader work in dimensionality reduction (Isomap), CAPTCHAs,
Steganography, Nearest Neighbor Search (cover tree), and Machine
Learning algorithms. Subjects of new interest include the
integration of exploration and learning and incentive-compatible
mechanisms for exploration. John Langford runs http://hunch.net
which is a blog on machine learning and learning theory
subjects.
