Projects and Groups
- Computer Science
- Algorithms and Theory
- Computational Biology
- Distributed and Fault-Tolerant Computing
- Knowledge Discovery and Data Mining
- Signal Processing
Thomas J. Watson Research Center, Yorktown Heights, NY USA
grahul us dot ibm dot com   +19149452276
I work at the Computational Biology Center of IBM T. J. Watson research center, Yorktown Heights, NY, USA. My present research is exploring the use of High-performance computing and machine learning techniques for the analysis of Neuroimaging data. I am developing algorithms to solve inverse problems in the domain of medical image reconstruction and analyze functional connectivity of the brain in the domain of fMRI data analysis.
In my previous life, I was manager of the high-performance computing group at IBM India Research Lab where I worked on performance and reliability of the IBM's Blue Gene family of supercomputers. I was privileged to work with wonderful colleagues at the IBM India Research Lab. All the wonderful work we did would not have been possible without their enthusiasm, active involvement and support.
Prior to this, I worked in the areas of communication networks, game theory, auction algorithms and Economics. I also held an adjunct faculty position in the Computer Science Department of Indian Institute of Technology, Delhi, where I taught courses on game theory and communications networks.
Representative publication( Click here for download links )
Prediction and interpretation of distributed neural activity with sparse models. Melissa K Carroll, Guillermo A Cecchi, Irina Rish, Rahul Garg, A Ravishankar Rao, Neuroimage, Volume 44(1), January 2009, pages 112-122.
Inferring brain dynamics using Granger causality on fMRI data. Guillermo A. Cecchi, Rahul Garg, A. Ravishankar Rao, The Fifth IEEE International Symposium on Biomedical Imaging (ISBI 2008): 604-607.
Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property. Rahul Garg and Rohit Khandekar, In Proceedings, 26th International Conference on Machine Learning (ICML), 2009.
HPCC RandomAccess Benchmark for Next Generation Supercomputers. Vikas Aggarwal, Yogish Sabharwal, Rahul Garg and Philip Heidelberger, IEEE International Parallel and Distributed Processing Symposium (IPDPS 2009). (Winner of the best paper award).
Software Routing and Aggregation of Messages to Optimize the Performance of the HPCC Randomaccess Benchmark. Rahul Garg and Yogish Sabharwal, In proceedings of the ACM/IEEE Conference on Supercomputing (SC 06) 2006. (Best paper award finalist).
Auction Algorithms for Market Equilibrium. Rahul Garg and Sanjiv Kapoor, Proceedings of the Annual ACM Symposium on Theory of Computing (STOC 04) 2004. Journal version appeared in Mathematics of Operations Research Vol. 31, No. 4, November 2006, pp. 714-729
A Game-Theoretic Approach Towards Congestion Control in Communication Networks. Rahul Garg, Abhinav Kamra and Varun Khurana, ACM Computer Communication Review, 32(3) July 2002.
Fair Bandwidth Sharing Among Virtual Networks: A Capacity Resizing Approach. Rahul Garg and Huzur Saran, In Proceedings of INFOCOM, March 2000, Tel-Aviv, Israel.