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Phone: 646-660-6500

Fax: 646-660-6501

 

Email:

provost.office@baruch.cuny.edu

 

Mailing Address:

Office of the Provost & Senior Vice President for Academic Affairs

Baruch College/CUNY

One Bernard Baruch Way
Box D-701

New York, NY 10010-5585

 

Walk-In Address:

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135 East 22nd Street, 7th Floor

Office of the Provost and Senior Vice President for Academic Affairs

Message Archive



Thursday, May 11, 2017

 

This email is being sent to all members of the Baruch College faculty.

For an archive of announcements sent from the Associate Provost beginning June 2011, click here.

 

The Information Systems and Statistics Research Seminar Series

Presented by the Paul H. Chook Department of

Information Systems and Statistics

 

Scalable and Robust Model Estimation and Predictive Performance Assessment

Prof. Kamiar Rahnama Rad, Baruch College

 

 

From:  Prof. Rongning Huang, Paul H. Chook Department of Information Systems and Statistics

Tuesday, May 16, 2017, 12:30pm-1:45pm, NVC 11-217 (IS-STA Conference Room)

Scalable and Robust Model Estimation and Predictive Performance Assessment

Prof. Kamiar Rahnama Rad, Baruch College


Abstract: The complexity of models and the massive size of structured big data call for computationally efficient and statistically robust methodologies that avoid overfitting and undue bias. I will show how to innovate scalable statistical methodologies for model estimation and predictive performance assessment, taking advantage of the high dimensionality of contemporary data sets. I will demonstrate the robustness, scalability, and statistical efficiency of this approach by applying it to both synthetic and real data.

Prof. Kamiar Rahnama Rad is an Assistant Professor in the Department of Information Systems and Statistics. He completed his BSc in Electrical Engineering at Sharif University of Technology (Tehran), MS in Electrical Engineering at UCLA, and PhD in Statistics at Columbia University. His research interests in Computational Statistics, Information Theory, and Machine Learning are wide-ranging, and his research focuses on scalable and robust high dimensional inference, with applications to computational neuroscience.