Vikas Garg
20092023

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Personal profile

Artistic and research interests

My research interests span both theoretical and applied aspects of Quantum Computing & Artificial Intelligence (AI)/Machine Learning (ML)/Deep Learning (DL). Current focus is on applications of quantum methods & AI/ML/DL in Computational Biology and Healthcare (with an emphasis on protein and drug design), Material Synthesis,  Cybersecurity, Energy, E-commerce, Wireless systems, Blockchain technologies,  Energy, Internet of Things (IoT), Computer Vision, and Natural Language Processing.     

Some ongoing projects include theory and applications of deep learning and quantum AI, human-assisted drug discovery, generative models, graphical models, and learning under uncertainty or resource constraints along with their intersections with optimization and game theory. Select recent publications can be found at: https://www.mit.edu/~vgarg/

We collaborate widely with academic and industrial experts. In the last few years, I've worked closely with several amazing researchers:

List of collaborators

Tommi Jaakkola: PhD supervisor (MIT)
Cynthia Rudin: SM supervisor (now at Duke University)
Raquel Urtasun:  MS supervisor (now at University of Toronto/Waabi)
Regina Barzilay and Stefanie Jegelka (MIT)
Adam Kalai and David Alvarez-Melis (Microsoft Research New England)
Steven Wu (CMU)
Katrina Ligett (Hebrew University and Caltech)
John Ingraham (now at Generate Biomedicines)
Inderjit Dhillon and Hsiang-Fu Yu (Amazon Research)
Lin Xiao (now at Facebook AI Research)
Ofer Dekel (Microsoft Research Redmond) 
Sukrit Shankar and Roberto Cipolla (University of Cambridge)
Risi Kondor (University of Chicago)
John Lafferty (now at Yale)

I'm also Co-Founder and Chief Scientist at YaiYai Oy, a company that provides cutting-edge quantum and AI/ML/DL solutions to startups, governments, and corporate powerhouses across the globe in multiple sectors including Biopharma, Retail, Manufacturing & Automation, IoT, Energy, FinTech, and Gaming.

Leadership in research work

Two decades of experience in research, teaching, engineering, product development, and supply chain management including stints at leading academic institutes, business schools, and companies such as Amazon Search (A9), Microsoft Research, and IBM Research. Also, Energy Fellow at MIT as well as an active research member of the Machine Learning for Pharmaceutical Discovery and Synthesis Symposium  (https://mlpds.mit.edu/) 

Some recent innovations have led to advances in diverse domains such as protein design,  multi-attribute visual search, fast inference on IoT devices such as smartphones, query recommendation and auto-completion for e-commerce platforms, drug discovery, strategic decision making in multi-agent systems, global supply chain dynamics, and design of smart grids and next generation wireless systems.

 

Multiple patents and publications at premier venues in AI, ML, NLP, Computer Vision, Automation, Energy, etc. (e.g., NeurIPS, ICML, CVPR, AISTATS, UAI, AAAI, IJCAI, TKDE, CIKM, e-Energy, and SmartGridComm).

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 7 - Affordable and Clean Energy
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 11 - Sustainable Cities and Communities

Education/Academic qualification

Graduate Research Assistant, MIT CSAIL, Massachusetts Institute of Technology (MIT)

External positions

Chief Scientist and Co-Founder, YaiYai Oy

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Collaborations and top research areas from the last five years

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  • AbODE: Ab initio antibody design using conjoined ODEs

    Verma, Y., Heinonen, M. & Garg, V., Jul 2023, Proceedings of the 40th International Conference on Machine Learning. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). JMLR, p. 35037-35050 14 p. (Proceedings of Machine Learning Research; vol. 202).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    File
    7 Downloads (Pure)
  • Are GANs overkill for NLP?

    Alvarez-Melis, D., Garg, V. & Kalai, A. T., 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Morgan Kaufmann Publishers, 13 p. (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
  • Modular Flows: Differential Molecular Generation

    Verma, Y., Kaski, S., Heinonen, M. & Garg, V., 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Morgan Kaufmann Publishers, 13 p. (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
  • Provably expressive temporal graph networks

    Souza, A. H., Mesquita, D., Kaski, S. & Garg, V., 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Morgan Kaufmann Publishers, 13 p. (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
  • Symmetry-induced Disentanglement on Graphs

    Mercatali, G., Freitas, A. & Garg, V., 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Morgan Kaufmann Publishers, 15 p. (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access