Himaghna Bhattacharjee
Senior AI Research Scientist, Meta Superintelligence Labs
San Francisco, California, United States
Summary
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Senior🎓
Top SchoolHimaghna Bhattacharjee is a Senior AI Research Scientist at Meta Superintelligence Labs with eight years of experience bridging generative model alignment and rigorous scientific modeling. He transitioned from a PhD in Chemical and Biomolecular Engineering—where he developed interpretable corrections to DFT errors and explainable graph models for molecular data—to applied ML research at Meta, contributing early Bayesian tree methods to Bean Machine. His work spans interpretable, physics-informed machine learning, active learning for experimental design, and practical advances in generative model safety. Based in San Francisco, he pairs deep theoretical skills with production-oriented research, having delivered cross-disciplinary impact from industrial vulcanization simulation to state-of-the-art ML intern projects. Notably, his PhD research achieved near-chemical accuracy while remaining computationally efficient, reflecting a focus on scalable, explainable solutions.
8 years of coding experience
11 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceDoctor of Philosophy - PhD Chemical and Biomolecular Engineering, Doctor of Philosophy - PhD Chemical and Biomolecular Engineering at University of Delaware
French, English, Bengali, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
clustering10
python9
probabilistic-models9
sampling9
scipy9
machine-learning9
inference7
ai6
data-science6
ml4
graph-neural-network3
journal2
creative-commons2
academia1
open-access1
Programming languages (2)
Github contributions (5)
himaghna/phosphine-ligands
May 2020 - Apr 2021
Contributions:53 pushes, 1 branch in 11 months
himaghna/beanmachine
Jun 2022 - Aug 2022
A library that allows for inference on probabilistic models
Contributions:41 pushes, 38 branches in 2 months
inferencemachine-learningprobabilistic-programmingprobabilistic-modelsprobabilistic
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