for contributions to graph and multimedia mining, fractals, self-similarity and power laws; indexing for multimedia and bioinformatics data, and data base performance evaluation.
Date: Jun 21, 2010
 Christos Faloutsos
ACM SIGKDD is pleased to announce that
Prof.
Christos Faloutsos
is the
winner of 2010 SIGKDD Innovation Award. He is
recognized for his fundamental contributions to graph and
multimedia mining, fractals, self-similarity and power laws; indexing
for multimedia and bioinformatics data, and data base performance
evaluation.
ACM SIGKDD Innovation Award is the highest award for technical
excellence in the field of Knowledge Discovery and Data Mining
(KDD). It is conferred on one individual or one group of
collaborators whose outstanding technical innovations in the KDD
field have had a lasting impact in advancing the theory and practice
of the field.
Professor Faloutsos seminal cross-disciplinary works on power-law
graphs, fractal-based analysis, time series, multimedia and spatial
indexing are a rare combination of both impressive breadth as well
as fundamental depth that set new research directions and inspired
subsequent research impacting the KDD field.
His fundamental contributions to spatial and multimedia mining and
indexing were well recognized. In 1997, VLDB recognized his
R+tree method
with its 'ten year paper' award. His work on
Hilbert curves
and
fractals
allowed for better access methods, as well as
for modeling and selectivity estimations of real clouds of points. His
work on
QBIC
(Query By Image Content) has been cited more than
1,000 times and inspired similar features that are in commercial
products such as DB2 Image Extender, and the proposed
GeMINI
approach (GEneralized Multimedia INdexIng) became the norm in
the field.
His expertise in the field of time series analysis and mining is widely
recognized. His
FODO 1993
paper introduced methods for efficient
similarity search in sequence databases, and it has been cited over
1200 times. His
SIGMOD 2004
paper on fast subsequence matching
in time-series databases was recognized with the best paper award
and has been cited over 1100 times. These seminal papers laid the
foundations that inspired a new research area on time series
databases.
His SIGCOMM 1999
paper discovered the fundamental power-laws
in the Internet topology. This work pioneered the field, and has
inspired many follow-up studies. As an indicator of the huge
impact, the paper is the 5th most cited paper in 1999, it has been
cited over 3000 times since, and will be presented with the "Test of
Time" award at SIGCOMM 2010.
His KDD 2004
paper was a pioneering contribution to large-scale
graph mining. It introduced fast methods to discover connectivity
subgraphs, leveraging the duality between random-walk and
electricity-based similarity to achieve efficient methods that can be
applied at web scale. Going beyond static network models, his
seminal paper on modeling network evolution received the best
paper award in
KDD 2005.
This paper reveals surprising
phenomena in time-evolving networks such as shrinking diameter
and provides a mathematical model, the densification law, to
accurately explain those phenomena. His work has also introduced
fast methods for estimation of key graph metrics, winning two
consecutive SDM best paper awards in
2007
and
2008
Two of
his students won dissertation award and runner-up on topics related
to graph mining in
KDD
2008
and
2007, respectively.
His sustained contributions to KDD, with more than 200 highly cited
publications, have been well recognized by a series of prestigious
awards, including the ICDM Research Contributions Award in 2006
and
15 Best Paper awards
from various highly competitive
academic forums including KDD, ICDM, SDM, PKDD, PAKDD,
SIGMOD, and VLDB.
His work has led not only to important publications, but also to
several projects with great broader societal impact. For example,
his NetProbe project, which developed tools combating Internet
auction fraud, was widely reported by many major news media.
He served on the Board of Directors of the first ACM SIGKDD
Executive Committee. He was Program Chair of KDD 2003 and
SIGMOD 1999. He was an associate Editor-in-Chief of IEEE
Transactions on Knowledge and Data Engineering (TKDE), and is
an associate editor of ACM Transactions on Knowledge Discovery
from Data (TKDD). He has made significant contribution to the KDD
community by continuously training many prominent students and
young researchers.
He received his PhD in 1987 from the University of Toronto,
Canada. Since 2000, he has been a full professor at the School of
Computer Science at Carnegie Mellon University . He has been
issued 5 patents with 4 more pending. He has delivered more than
100 Distinguished, Keynote, and Invited lectures.
The previous SIGKDD Innovation Award winners were Rakesh
Agrawal, Jerome Friedman, Heikki Mannila, Jiawei Han, Leo
Breiman, Ramakrishnan Srikant, Usama M. Fayyad, Raghu
Ramakrishnan, and Padhraic Smyth.
The award includes a plaque and a check for $2,500 and will be
presented at the Opening Plenary Session of the
16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
on July 25, 2010 in Washington , DC .
Prof. Faloutsos will present the
Innovation Award Lecture
immediately after the awards presentations.
ACM SIGKDD is pleased to present Prof. Christos Faloutsos its 2010
Innovation Award for his foundational technical contributions to the
KDD field.
2010 ACM SIGKDD Awards Committee
- Ramasamy Uthurusamy, Chair
- Robert Grossman ( University of Illinois at Chicago )
- Jiawei Han ( University of Illinois at Urbana-Champaign)
- Tom Mitchell ( Carnegie Mellon University )
- Gregory Piatetsky-Shapiro (KDnuggets)
- Raghu Ramakrishnan (Yahoo! Research)
- Sunita Sarawagi (Indian Institute of Technology , Bombay )
- Padhraic Smyth ( University of California at Irvine )
- Ramakrishnan Srikant (Google Research)
- Xindong Wu ( University of Vermont )
- Mohammed J. Zaki (Rensselaer Polytechnic Institute)
About ACM SIGKDD Innovation Award
ACM SIGKDD Innovation Award is the highest award for technical
excellence in the field of Knowledge Discovery and Data Mining
(KDD). It is conferred on one individual or one group of
collaborators whose outstanding technical innovations in the KDD
field have had a lasting impact in advancing the theory and practice
of the field. The contributions must have significantly influenced
the direction of research and development of the field or
transferred to practice in significant and innovative ways and/or
enabled the development of commercial systems. The award
includes a plaque and a check for $2,500 and will be presented at
the Opening Ceremony of the annual ACM SIGKDD International
Conference on KDD. The Innovation Award recipient presents the
Innovation Award Lecture immediately after the awards
presentations.
About ACM SIGKDD
ACM SIGKDD, ACM's Special Interest Group on Knowledge
Discovery and Data Mining (KDD), is the premier global
professional organization for researchers and professionals
dedicated to the advancement of the science and practice of
knowledge discovery and data mining. It established the Innovation
and Service Awards to recognize outstanding technical and service
contributions to the KDD field.
About ACM
The Association for Computing Machinery (ACM) is the world's
largest educational and scientific computing society, uniting
computing educators, researchers and professionals to inspire
dialogue, share resources and address the field's challenges. ACM
strengthens the computing profession's collective voice through
strong leadership, promotion of the highest standards, and
recognition of technical excellence. ACM supports the professional
growth of its members by providing opportunities for life-long
learning, career development, and professional networking.
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