Rahul Garg

 

Professor,

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 and Head National Resource Centre for Value Education in 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.

 

  

 

News

 

·         NRCVEE is organizing a workshop on Mind Meditation and Human Values: A Scientific Perspective. Check it out.

 

 

 

 

Research Interests

 

·        Brain Imaging and Neuroscience of Yoga

·        Machine Learning and Big Data Analytics

·        IT for Society

 


 

Teaching

 

·         COL 786: Advanced Functional Brain Imaging

Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020

 

·         COL341: Fundamentals of Machine Learning

Monsoon 2016, Monsoon 2018, Monsoon 2019

·         VEV741 - Special Module on Inner Development: Introduction to Yogic Neuroscience

Spring 2018 (jointly with Shri Adinarayanan V.)

·         COL100 Introduction to Computer Science

Monsoon 2017 (jointly with Vinay J Ribeiro)

·         CSL 730: Modern Parallel Programming 

Monsoon 2015

·          CSV880 – Special Topics in Parallel Computing

Spring 2015 (jointly with Yogish Sabharwal)

 

 

 

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 these 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   Yogic Sciences

o   Computational Sanskrit linguistics

o   Posture estimation and tracking

 

 

  

            Students interested in internships, please read this before you write an email to me.

 

News: Students from NIFTI with 8+ GPA can now get admission into the (two year) full-time Masters by Research (MSR) program in the CSE as well as in SIT without GATE.  For more information read the information brochure or write an email to me with subject “Interested in full-time MSR/PhD”.

 

Students interested in research should consider applying to the MSR or PhD program at IIT-Delhi. You may consider MSR/PhD at School of Information Technology for interdisciplinary applied areas, Computer Science and Engineering for fundamental CS research or a PhD at National Resource Center for Value Education in Engineering for research in Yogic Sciences.

 

University of Queensland and IIT-Delhi now offer a very strong joint PhD program in brain sciences and neuroimaging. There are two options for the PhD program. Under the Indian option students are expected to spend 1 year at UQ, Australia, and rest of the time at IIT Delhi. Under the Australian option, the students are expected to spend one year at IIT-Delhi and rest of the time at UQ. Please also explore the IIT-Delhi and University of Queensland joint PhD program in the area of machine learning applied to fMRI analysis, epilepsy (project number UQIDAR 00166).

 

Students who obtain very good UG grades may now be eligible to apply for PhD under prime minister research fellowship (PMRF). This program offers a stipend of Rs. 70,000-80,000 per month (depending on your stage in the PhD program) and research grant of Rs.2 lakhs per year.  A caution – the program may be very competitive and some of the decisions may seem arbitrary and unfair to applicants. So, for students seriously considering PhD, it is a good idea to have a backup option other than PMRF.

 

 

 

 

 

 

Recent/Representative publication

 

o     Mind and Body in Balance: Assessing Yoga to Demystify its Effects on Cognitive Performance,
Varsha Singh, Sonika Thakral, Kunal Singh, Sanjeev Jain, Rahul Garg. In ACCS 2019, Goa, India, December 2019.

o     Acceleration of Sparse Vector Autoregressive Modeling using GPUs [pdf],
Shreenivas Bharadwaj Venkataramanan, Rahul Garg, Yogish Sabharwal. In HiPC 2019, Hyderabad, India, December 2019.

o     Engagement of multiple networks during meditation: An fMRI study
Vaibhav Tripathi, Rahul Garg, Vidur Mahajan, Anju Dhawan. In ISMRM 2019, Montreal, Canada, May 2019.

o     Near-Separable Non-negative Matrix Factorization Using L1-Optimization [pdf]
Aashish Nagpal, Chayan Sharma, Rahul Garg and Pawan Kumar, In The ACM India Joint International Conference on Data Science & Management of Data CoDS-COMAD 2019.

o     Evidence of Dense Functional Connectivity in the Human Brain
Ankita Saha, Ishaan Batta and Rahul Garg, In ISMRM 2018, Paris, France, June 2018

o     Disrupted functional connectivity of occipital lobe with frontal and parietal regions in subjects with Cocaine addiction
Jaspreet Kaur, Divya Gautam, Rahul Garg, In ISMRM 2018, Paris, France, June 2018.

o     SandhiKosh: A Benchmark Corpus for Evaluating Sanskrit Sandhi Tools [pdf, bibtex]
Bhardwaj, Shubham, Neelamadhav Gantayat, Nikhil Chaturvedi, Rahul Garg, and Sumeet Agarwal, In LREC. 2018.

o      A Tool for Transliteration of Bilingual Texts Involving Sanskrit [pdf]
Nikhil Chaturvedi and Rahul Garg
In Proceedings of the 17th World Sanskrit Conference: Computational Sanskrit and Digital Humanities, University of British Columbia, Vancouver, July 2018.

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

 

     Neuroimaging

     High performance computing

     Algorithms, Machine Learning, and Game theory

     Communication Networks

     Others

 

 

 


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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