Raghav Kansal is a Senior Deep Learning Scientist with a decade of experience at the intersection of AI and fundamental science, currently building BexFlow, a foundation model for the human brain at Bexorg. He holds a PhD in particle physics from UC San Diego and was a Schmidt AI Fellow at Caltech and Fermilab, where he developed equivariant graph and transformer models deployed at the CERN Large Hadron Collider. His work spans generative modeling, multimodal representation learning, and Lorentz-group equivariant ML, with publications in NeurIPS, ICML, PRL and JHEP and production-grade deployments in high-energy physics. Raghav has a track record of turning physics and biology priors into state-of-the-art simulation and classification systems, and his open-source JetNet dataset and tools have been widely adopted by the ML+Hep community. He is now translating those techniques to brain molecular trajectories and multiomic integration for biotech applications. An experimentalist-turned-ML scientist, he combines hands-on lab experience (neuroscience and quantum optics) with rigorous model-building for scientific discovery.
10 years of coding experience
Island School
University of California, San Diego
International Baccalaureate, International Baccalaureate at American School of Bombay
Contributions:25 commits, 7 PRs, 106 pushes in 26 days
pythonmachine-learningjets
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Raghav Kansal - Schmidt AI Postdoctoral Fellow at Caltech