Summary
Harit Vishwakarma is a senior postdoctoral research fellow and machine learning researcher with 11 years of experience spanning industry and academia, currently based in Oxford. His work focuses on data-efficient and safe AI—specializing in semi-supervised learning, uncertainty quantification, out-of-distribution robustness, and reducing LLM hallucinations through test-time compute scaling and statistically grounded inference. He combines theoretical contributions (12 papers with six first-author works and multiple spotlights at NeurIPS/ICLR/AISTATS) with practical systems experience from roles at Snorkel AI, J.P. Morgan, Amazon, and IBM Research. Notably, he has developed methods like CP-OPT and CROQ to improve LLM decision-making and has a track record of translating conformal prediction and selective classification ideas into applied LLM and dataset-building tools. Trained at UW–Madison (PhD) and IISc, he equally enjoys empirical, theoretical, and systems work and often targets solutions that deliver anytime-valid robustness after deployment.
11 years of coding experience
11 years of employment as a software developer
Master's Degree Computer Science, Master's Degree Computer Science at Indian Institute of Science (IISc)
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Wisconsin-Madison
Bachelor of Engineering (B.E.) Computer Engineering, Bachelor of Engineering (B.E.) Computer Engineering at Shri G S Institute of Technology & Science
Higher Secondary, Higher Secondary at School of Excellence, Sagar
English, Hindi