Summary
Suraj Pawar is an Artificial Intelligence Resident at Shell with eight years of experience applying machine learning, computer vision, and high-performance computing to energy and fluid dynamics problems. He holds a Ph.D. in Mechanical Engineering and has a strong track record of turning physics-guided research into production-ready deep learning solutions, from SGS closure models for turbulence to multi-fidelity neural emulators for wind farm wakes. His work at national labs and universities includes building scalable reinforcement learning tooling for leadership-class supercomputers and achieving measurable runtime and accuracy gains in CFD and wake prediction. Suraj blends classical engineering (RANS, multigrid solvers, surrogate-based optimization) with modern ML (frame-invariant CNNs, LSTMs, autoencoders) to produce robust, numerically stable models. Notably, he has delivered end-to-end systems that sped inference by an order of magnitude and reduced closure model run time by 30%, demonstrating an uncommon focus on deployability and computational efficiency. Based in Houston, he combines hands-on simulation expertise with software engineering practices to move novel algorithms from prototypes into operational tools.
8 years of coding experience
7 years of employment as a software developer
Virginia Tech
Bachelor of Technology (B.Tech.), Mechanical Engineering, CGPA : 9.1, Bachelor of Technology (B.Tech.), Mechanical Engineering, CGPA : 9.1 at VJTI
Doctor of Philosophy - Ph.D, Mechanical Engineering, 4.0, Doctor of Philosophy - Ph.D, Mechanical Engineering, 4.0 at Oklahoma State University
English, Hindi, Marathi, Gujarati