Rahul Garg



Department of Computer Science and Engineering,

Indian Institute of Technology, Delhi

Email: <firstname><lastname> [ at ] cse [ dot ] iitd [ dot ] ac [ dot ] in

Ph.D. Indian Institute of Technology, Delhi

M.S. University of California, Berkeley

B.Tech. Indian Institute of Technology, Delhi





I am a Professor at Department of Computer Science and Engineering at the Indian Institute of Technology, Delhi.


Prior to joining IIT-Delhi, I worked for Opera Solutions India as VP Analytics and Head R&D. Headquartered in New York City, Opera Solutions is an analytics software/solutions company providing state-of-the-art analytics solutions to several global Fortune 500 clients. Prior to Opera Solutions in August 2011, I worked at the Computational Biology Center of IBM T. J. Watson research center, Yorktown Heights, NY, USA. My research explored the use of High-performance computing and machine learning techniques for the analysis of Neuroimaging data.  I developed 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. Before moving to IBM TJ Watson Research Center, I was manager of the high-performance computing group at IBM India Research Lab. I was privileged to work with some wonderful colleagues at the IBM India Research Lab. All the wonderful work we did wouldn’t have been possible without their enthusiasm, active involvement and support.


My earlier areas of work include 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.






Research Interests


·        Brain Imaging and Neuroscience of Yoga

·        Machine Learning and Big Data Analytics

·        IT for Society





         Spring 2016

·        COL 786: Advanced Functional Brain Imaging

Fall 2015

·         CSL 730: Modern Parallel Programming 

Spring 2015

 ·        CSL 865 – Special Topics in Computer Applications: Introduction to Neuroimaging Methods and Analysis

·        CSV880 – Special Topics in Parallel Computing




Current Research Projects


 I am currently actively working in the following projects. Most of my projects require problem solving and building systems/software/application/algorithms for solving the problem. The innovation comes from applying techniques learned in one domain to problems in another domain. These projects require varying degrees of expertise and skills that I have picked up over the years. Those interested in working on the projects, should best avoid expectation of working in any particular area. Instead it will be better if they find the problem interesting and worth solving. I try to guide the students towards their innate strengths areas and try to adapt the problems to suit their temperaments and interests. 


o   fMRI data analysis methods

o   Computational Sanskrit linguistics

o   Technologies for massively online open courses (MOOC)

o   Scientific research on Yoga (watch out this space for our upcoming work on Yoga Nidra)


            Students not studying at IIT-Delhi, please read this before you write an email to me.





Representative publication


o      Full-brain auto-regressive modeling (FARM) using fMRI.  [doi, pdf]
Rahul Garg, Guillermo A. Cecchi and A. Ravishankar Rao,
NeuroImage, Volume 58(2), 15 September 2011, pages 416–441.

o      Prediction and interpretation of distributed neural activity with sparse models.  [ doi, pdf, bibtex ]
Melissa K Carroll, Guillermo A Cecchi, Irina Rish, Rahul Garg, A Ravishankar Rao,
Neuroimage, Volume 44(1), January 2009, pages 112-122.

o      Inferring brain dynamics using Granger causality on fMRI data. [ pdf ]
Guillermo A. Cecchi, Rahul Garg, A. Ravishankar Rao,
The Fifth IEEE International Symposium on Biomedical Imaging (ISBI 2008): 604-607.

o      Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property. [ pdf ]
Rahul Garg and Rohit Khandekar,
In Proceedings, 26th International Conference on Machine Learning (ICML), 2009.
Following are the links to Matlab implementation of GraDeS and the ICML talk.

o      HPCC RandomAccess Benchmark for Next Generation Supercomputers. [ pdf ]
Vikas Aggarwal, Yogish Sabharwal, Rahul Garg and Philip Heidelberger,
IEEE International Parallel & Distributed Processing Symposium (IPDPS 2009).  (Winner of the best paper award).

o      Software Routing and Aggregation of Messages to Optimize the Performance of the HPCC Randomaccess Benchmark. [ pdf ]
Rahul Garg and Yogish Sabharwal,
In proceedings of the ACM/IEEE Conference on Supercomputing (SC’06) 2006. (Best paper award finalist).

o      Auction Algorithms for Market Equilibrium. [ pdf ]
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 [ link, preprint ]


o      A Game-Theoretic Approach Towards Congestion Control in Communication Networks. [ link, preprint ]
Rahul Garg, Abhinav Kamra and Varun Khurana,
ACM Computer Communication Review, 32(3) July 2002.

o      Fair Bandwidth Sharing Among Virtual Networks: A Capacity Resizing Approach. [ link, pdf ]
Rahul Garg and Huzur Saran,
In Proceedings of INFOCOM, March 2000, Tel-Aviv, Israel.






Publications organized by topics



     High performance computing

     Algorithms, Machine Learning, and Game theory

     Communication Networks





Awards and Honours


o       Best paper award at IPDPS 2009

o       IBM research division award in recognition for the contributions made to the Blue Gene project

o       IBM Research Fellowship, by IBM Research, New Delhi, India, for pursuing Ph.D. at Indian Institute of Technology, Delhi

o       Technical program committees: IPDPS 2006, HIPC 2003, INFOCOM 2001, ICCCN 2000, ICCCN 1999.


Download my resume here.





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