I am a Postdoctoral Researcher at Stanford working with Fei-Fei Li and Silvio Savarese at Stanford AI Lab. I also collaborate closely with Marco Pavone and his group.

My current research focuses on machine learning algorithms for perception and control in robotics. I am specifically interested in enabling self-supervised robot learning and human-robot interaction in the process.

I received MS in Computer Science and Ph.D. in Operations Research from the UC, Berkeley in 2016. I was advised by Ken Goldberg in the Automation Lab as a part of the Berkeley AI Research Lab (BAIR). I also worked closely with Pieter Abbeel, Alper Atamturk and UCSF Radiation Oncology. My thesis work was on algorithms at the intersection of Optimization and Design for Automation. I studied the interaction of data-driven Learning for Autonomy and Design for Automation for human skill-augmentation and decision support.

Research Interests: Optimization (Mixed Integer, Non-convex), Machine Learning and Design for Automation in Healthcare & Robotics.

Contact me: garg@cs·stanford·edu
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Recent News

Recent Research

Rethinking Generalization in Robot Learning
At Stanford, I am leading the effort in Rethinking Generalization in Robot Learning. It is a multi-proged effort in developing hierarcical Deep RL abstractions along with learning from video demonstrations. Stay Tuned
Learning & Automation in Surgical Subtasks
We are working towards surgical sub-task automation and exploring a data driven approach for reduction in training effort with unsupervised task structure learning. Read More
Brachytherapy for Cancer: Planning and Delivery
Brachytherapy is an internal form of radiotherapy for cancer, involving radioactive source placement in proximity of the tumor. We have worked on novel patient specific delivery methods for interstitial (e.g. prostate) and intracavitary (e.g. oral/thoracic, GYN) tumors. Read More

Coverage in Media/Press

  • IEEE Spectrum
  • IEEE Spectrum
  • IEEE Spectrum
  • New York Times
  • Med Gadget
  • 3D Printing World
  • Phys.Org

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