WELCOME TO ICDM 2015

 

The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels.

KEYNOTE SPEAKERS

 
  Robert F. Engle
Prof. Robert F. Engle
(Nobel Prize for Economics, 2003; NAS)
Michael Armellino Professor of Finance
New York University
 
Prof. Michael I. Jordan
(NAS, NAE, AAAS, F'AAAS)
Pehong Chen Distinguished Professor
University of California, Berkeley
 
Dr. Lada Adamic
Data Scientist
Facebook, Inc.

Best Paper Awards

We are pleased to announce that the ICDM 2015 Best Paper Award goes to
"Diamond Sampling for Approximate Maximum All-pairs Dot-product (MAD) Search" (DM860) by 
Grey Ballard, Seshadhri Comandur, Tamara Kolda, and Ali Pinar;

the ICDM 2015 Best Student Paper Award goes to
"From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics" (DM345) by 
Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, and Shiqiang Yang;

and the 2015 ICDM 10-year Highest-Impact Paper Award goes to
"Fast Random Walk with Restart and Its Applications" by Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan.

ICDM 2015 is Calling for Your Participation

You are cordially invited to attend the 15th IEEE International Conference on Data Mining (ICDM 2015), which will be held in Atlantic City, NJ, U.S.A., on November 14-17, 2015. The IEEE International Conference on Data Mining series (ICDM) is the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications.

Read more: http://icdm2015.stonybrook.edu/attending/call-for-participation

Registration is Now Open

The information of registration fees and deadlines is available here:

http://icdm2015.stonybrook.edu/attending/registration

Author registrations must be completed by the deadlines as follows. See  Author Registration Policies
  • Conference paper author registration: September 1, 2015.
  • Workshop paper author registration: September 8, 2015.

For the travel information of Atlantic City, such as airports, public transportation, and visa information Visa Support Letters, please visit:

http://icdm2015.stonybrook.edu/attending/about-ac

2015 IEEE ICDM Keynote Speakers

Nobel Prize Winner, Machine Learning Guru, and Facebook Data Scientist to Keynote 2015 IEEE ICDM

Press Release from IEEE: Robert F. Engle, co-recipient of the Nobel Prize for Economics; Michael I. Jordan, a guru in machine learning; and Lada Adamic, a computational social scientist at Facebook, will be keynote speakers for the 2015 IEEE International Conference on Data Mining series (ICDM), the world’s premier research conference in data mining.  

Read More: http://www.computer.org/web/pressroom/icdm-keynotes

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KEYNOTE SPEAKERS

Robert F. Engle, American economist, corecipient of the Nobel Prize for Economics in 2003 for his development of methods for analyzing time series data with time-varying volatility.

Michael I. Jordan, the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.

Lada Adamica leader in the Product Science group within Facebook’s Data Science Team.

learn more

An idea I liked is to basically treat the daily part and the time and date effect as three components. What is really trying to do is to predict the volatility in all these buckets. That’s the way you can build high-frequency volatility models.

Robert F. Engle