Som Dhulipala
Senior Scientist AI ML For Scientific Computing at Idaho National Laboratory
Idaho Falls, Idaho, United States
Summary
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Rockstar🎓
Top SchoolSom Dhulipala is a Senior Scientist specializing in AI/ML for scientific computing with 9 years of experience and 7+ years focused on scalable probabilistic and generative methods. Based at Idaho National Laboratory, he leads research that marries diffusion and probabilistic models with HPC to accelerate multiphysics simulation and forecasting, achieving over 50% improvement in forecast accuracy while cutting computational cost. He has served as PI on major AI-for-Science projects, led lab-wide digital twin data assimilation efforts, and supervised postdocs and interns to translate research into multi-million-dollar proposals and 40+ publications. His work bridges probabilistic programming (Pyro), large-scale uncertainty quantification, and active-learning methods that have reduced rare-event simulation cost by orders of magnitude. An active open-source contributor, he has contributed backend improvements to the widely used MOOSE multiphysics framework, including tensor mechanics and stochastic tools. Trained as an engineer-statistician (PhD, Virginia Tech), he combines rigorous theoretical grounding with hands-on implementation at HPC scales.
9 years of coding experience
4 years of employment as a software developer
Jawaharlal Nehru Technological University HyderabadVirginia TechIndian Institute of Technology Bombay
Github Skills (9)
simulations10
simulation10
finite-element-analysis10
c-language10
tensor10
el10
f10
tensorflow10
cprogramming-language10
Programming languages (5)
Github contributions (5)
idaholab/moose
Aug 2019 - Jan 2023
Multiphysics Object Oriented Simulation Environment
Role in this project:
Backend Developer
Backend DeveloperContributions:96 reviews, 50 commits, 29 PRs in 3 years 5 months
Contributions summary:Som made several changes to the `moose` repository, which is a multiphysics simulation environment. The commits focused on improvements to tensor mechanics modules, specifically addressing and implementing changes to the `ComputeIsotropicElasticityTensor` and related test files. Furthermore, the user implemented code that involved the stochastic tools package by adding a kernel density estimation function.
pythonfinite-element-analysisfemparallelmultiphysics
somu15/Small_Pf_code
Jun 2020 - Mar 2022
Contributions:3 PRs, 86 pushes, 4 branches in 1 year 8 months
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