The nature of information processing has evolved dramatically over the years as we try to explore increasingly complex and diverse systems ranging from the Internet and wireless networks, to the human brain and genome. I am interested in developing techniques at the intersection of statistical machine learning and
signal processing that can adaptively learn and exploit the low-dimensional information structure inherent in high-dimensional systems for efficient inference. The primary thrust of my research is on bridging the gap between theoretically optimal and practically useful methods with applications that include wireless and sensor networks, internet data analysis, neuroscience and bioinformatics. |
- CAREER: Distilling information structure from big and dirty data: Efficient learning of clusters and graphs in modern datasets (Sponsored by NSF)
- BIGDATA: Distribution-based machine learning for high dimensional datasets (Sponsored by NSF)
- Resource-constrained data collection and fusion for
identifying weak distributed patterns in networks (Sponsored by AFOSR)
- Spectral Methods for Active Clustering and Bi-Clustering (Sponsored by NSF)
- Using Non-Local Connectivity Information to Identify Nascent Disease Outbreaks (Sponsored by NIH MIDAS National Center of Excellence at University of Pittsburgh)
- SML (Statistical Machine Learning) Reading Group
- Information Theory and Applications (ITA) Workshop, UCSD, Feb 10-15, 2013.
- BIRS workshop on Asymptotics of Large-Scale Interacting Networks, Feb 25-Mar 1, 2013.
- Fete Parisienne in Computation, Inference and Optimization: A Young Researchers' Forum, Institut des Hautes Etudes Scientifiques (IHES), Paris, Mar 20, 2013.
- Artifical Intelligence and Statistics (AISTATS), Apr 29-May 1, 2013.
- Systems Information Learning Optimization (SILO) Workshop, UW-Madison, Jun 17-19, 2013.
Spring 2013 10-702/36-702 Statistical Machine Learning - with Larry Wasserman
Fall 2012 10-701/15-781 Machine Learning - with Eric Xing
Spring 2012 10-704 Information Processing and Learning
Fall 2011 10-601 Machine Learning - with Tom Mitchell
Spring 2011 10-702/36-702 Statistical Machine Learning - with Larry Wasserman
Fall 2010 10-701/15-781 Machine Learning
Spring 2010 10-701/15-781 Machine Learning - with Tom Mitchell, Eric Xing
10-915 MLD Journal Club - with Geoff Gordon
Fall 2009 MLD Journal Club - with Geoff Gordon
I received my B.E. in Electronics and Communication Engineering from the
University of Delhi in 2001,
and M.S. and Ph.D. degrees in Electrical and Computer Engineering
from the University of Wisconsin-Madison in 2003 and 2008, respectively.
I was a Postdoctoral Research Associate at the Program in Applied and
Computational Mathematics at Princeton University from 2008-2009.
Detailed CV (pdf) - Updated: 04/12/13
Aaditya Ramdas (co-advised with Larry Wasserman)
I also work with Sivaraman Balakrishnan, Min Xu and Mladen Kolar.
Tongbo Huang (now M.S. Student CS, Carnegie Mellon)
Prospective students: I can only advise students who have been admitted to the School of Computer Science at CMU. If you would like to work with me and have been admitted to CMU, please send me an email with your CV and briefly describe your research interests. I do not offer summer internships.