Machine Learning
Machine learning is broad research area focused on pattern recognition, with strong ties to artificial intelligence, data mining, statistical pattern recognition, and predictive analytics. In the Machine Learning group at CCLS we work on designing new machine learning algorithms motivated by problems in real-world data. We also apply these algorithms to a variety of problems in the fields of energy management, computational biology, and the physical sciences.
Drs. Salleb-Aouissi, Passonneau, Waltz, McCord, McGurk and Elhadad awarded a Research Initiatives in Science and Engineering (RISE) funding from the Columbia University Executive Vice President for Research
CCLS researchers, Ansaf Salleb-Aouissi, Rebecca Passonneau, David Waltz, their collaborators from Columbia University Medical School, Mary McCord, Harriet McGurk and CU Biomedical Informatics colleague Noémie Elhadad were awarded funding by Columbia University's Office of the Executive Vice President for Research, for their research on “Understanding Infantile Colic via Machine Learning”. 
Talk by: Arindam Banerjee
Yahoo! Academic Relations and The Columbia University Center for Computational Learning Systems Present:
Who: Arindam Banerjee
When: Friday December 3rd, 2010
Time: 3pm
Where: Interschool Lab, 7th Floor CEPSR
Title: Probabilistic Models for Analyzing Large Sparse Matrices
Abstract:
CCLS wins GE Ecomagination Innovation Prize
The CCLS submission “Creation of a Columbia Machine Learning System to Optimize the Recharging of Electric Delivery Vehicles in Large Urban Cities” has been chosen as the “Winning University Program” in General Electric’s Ecomagination Innovation Challenge of 2010.GE’s CEO Jeff Immelt presented
the award funding the 3 year, $1.1 million project, at a live event in New York City on Novem
Talk by Alex Smola, Yahoo! Research
Yahoo! Academic Relations and The Columbia University Center for Computational Learning Systems Present:
Talk Title: Fast and Sloppy – Scaling Up Linear Models
Abstract:
Dr. Monteleoni and co-authors win Best Application Paper Award at CIDU 2010
CCLS researcher, Claire Monteleoni, and her co-authors, Gavin Schmidt, and Shailesh Saroha, were awarded Best Application Paper at the NASA Conference on Intelligent Data Understanding (CIDU) 2010, for their paper, "Tracking Climate Models," which uses machine learning to combine the predictions of 20 global climate models.
Dr. Monteleoni awarded Earth Institute funding for research on Climate Informatics
CCLS researcher, Claire Monteleoni, and her collaborator Gavin Schmidt, were awarded funding by Columbia University's Earth Institute, for their research on Climate Informatics. The funding will support an undergraduate research assistant on the project.
Talk by John Langford, Yahoo! Research
Yahoo! Academic Relations & the Columbia University Center for Computational Learning Systems Present:
Talk Title: Steps Towards Efficient Parallel Learning
Abstract:
MathWorks, Inc signs a pilot agreement with CCLS to test scalability of Distributed Computing Server on the Amazon Cloud Infrastructure
A team from CCLS (Haimonti Dutta, Hatim Diab and Manoj Pooleery) is collaborating with MathWorks, Inc. to test the scalability of MATLAB Parallel Computing Toolbox and Distributed Computing Server (MDCS) on the Amazon EC2 / S3 Cloud.
CCLS research to inform the Intergovernmental Panel on Climate Change (IPCC)
CCLS researcher Dr. Claire Monteleoni and student 
Shailesh Saroha are collaborating with NASA climate modeler Dr. Gavin Schmidt, on a project that Dr. Schmidt will present this month at the IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections. The IPCC, established by the United Nations in 1988, received the Nobel Peace Prize in 2007, shared with Al Gore.
Drs. Dutta, Waltz, Schevon and Emerson win an NSF award to work on a Distributed Framework for Learning on EEG Data obtained from Epilepsy Patients
Project Name: EEGMine: A Distributed Framework for Learning 
on EEG Data obtained from Epilepsy Patients
PI/Co-Pis: Haimonti Dutta, David Waltz, Catherine A Schevon
and Ronald Emerson
NSF Project ID: IIS-0916186
Award: $440,000 for 2 years
Drs. Waltz and Vapnik win an NSF grant to study Learning Using Hidden Information
Dr. David Waltz and Dr. Valdimir Vapnik has been awarded a grant by the National Science Foundation to perform researc
h in the area of "An Advanced Learning Paradigm: Learning Using Hidden Information". This project will be focusing on developing algorithms in the SVM family that allow extra information to be used effectively during training, with the understanding that this extra information will not be available during actual operation.
Automating Science
ACM Awards Banquet
