Som Dhulipala
Senior Scientist Ai Ml for Scientific Computing @Idaho National Laboratory
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WORK HISTORY
Senior Scientist Ai Ml for Scientific Computing @Idaho National Laboratory
Idaho Falls, ID, US
Lead research on probabilistic machine learning, diffusion models, and AI for multiphysics systems, integrating generative AI with HPC- Principal Investigator for two major AI for Science projects (2025–2027) on generative models and foundation models for simulation and forecasting- Developed compositional generative models for coupled physics, improving forecasting accuracy by >50% while reducing computational cost- Led Digital Twin data assimilation scope across INL’s lab-wide initiative- Supervised 3 postdocs and 7+ interns, fostering AI-for-science collaboration- Authored 40+ publications and collaborated on multi-million-dollar AI proposals.
EDUCATION
Virginia Tech
Doctor of Philosophy (Ph.D.), Engineering and Statistics
Jawaharlal Nehru Technological University
Bachelor of Technology (B.Tech.), Civil Engineering
Indian Institute of Technology, Bombay
Master's degree, Structural Engineering
ABOUT SOM DHULIPALA
Senior AI Scientist with 7+ years of experience developing scalable probabilistic and generative AI models for scientific and engineering systems. I specialize in diffusion models, uncertainty quantification, and AI-driven simulation, building end-to-end solutions that integrate physics-based modeling with modern ML.At Idaho National Laboratory, I lead cross-functional AI research programs applying generative modeling, Bayesian inference, and graph neural networks to forecasting, reliability, and digital twins for complex systems. My work has resulted in >40 peer-reviewed publications, multiple leadership roles, and recognition through an EB-1A immigrant visa for extraordinary ability.I’m passionate about advancing AI for Science. I build scalable AI systems that accelerate physical-simulation, forecasting, and decision-making in engineering and energy applications.Research Interests:Generative AI, Diffusion Models, Probabilistic Inference, Bayesian methods, Uncertainty Quantification (UQ), Graph ML, Scientific Simulation, Digital Twins, AI for Energy and Engineering Systems
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