Rishi Ranade
Applied Research Manager, Computational Engineering
Pittsburgh, Pennsylvania, United States
Summary
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Rockstar🎓
Top SchoolRishi Ranade is a Senior Technical Product Manager at NVIDIA who blends deep expertise in engineering simulations, numerical solver development, and machine learning to deliver Physics AI models and CAE blueprints. He specializes in ML-driven solvers for PDEs, FEM/FVM-informed neural architectures, generative methods for PDEs, and thermal and turbulence modeling—skills honed through roles at Ansys and a PhD in Mechanical Engineering from NC State. Rishi’s work emphasizes practical integration of research into simulation workflows, including geometry and boundary-condition representation, topology optimization, and data compression for large-scale physics problems. Known for translating complex numerical methods into product-ready features, he bridges research and productization in high-performance engineering domains. Based in Pittsburgh, he brings a rare combination of academic rigor and hands-on industry experience driving next-generation CAE tooling.
2 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Mechanical Engineering, Doctor of Philosophy (PhD) Mechanical Engineering at North Carolina State UniversityBachelor of Engineering (BE) Mechanical Engineering, Bachelor of Engineering (BE) Mechanical Engineering at Savitribai Phule Pune University
Github Skills (9)
pytorch10
machine-learning10
deep-learning10
physics10
fine-tuning10
nvidia9
aerodynamics8
fluid-dynamics7
algorithms6
Programming languages (2)
Github contributions (5)
RishikeshRanade/modulus
Nov 2024 - Mar 2026
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
Contributions:36 pushes, 8 branches in 1 year 4 months
RishikeshRanade/didactic-guide
Nov 2024 - Nov 2024
Contributions:6 pushes, 3 branches in 1 day
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