Summary
Maziar Raissi is an applied mathematician and machine learning researcher with nine years of experience bridging deep learning theory and real-world systems. He has held academic posts as an assistant professor and postdoc at leading universities and applied his expertise at NVIDIA as a senior software engineer, proving he can move between rigorous research and production engineering. His work focuses on leveraging data as the “source code” for deep models and injecting prior knowledge to make learning possible even with limited datasets. Trained with multiple advanced degrees in applied mathematics, statistics and economics, he brings a strong quantitative foundation to problems in modeling, simulation, and scientific computation. Colleagues value his ability to translate theoretical insights into practical algorithms and scalable implementations. Less obvious: he pairs academic rigor with industry pragmatism, having contributed to both world-class research and Treasury-level quantitative analysis early in his career.
9 years of coding experience
8 years of employment as a software developer
The University of Maryland, College Park
Bachelor's Degree, Applied Mathematics, Bachelor's Degree, Applied Mathematics at University of Isfahan
Master's Degree, Applied Mathematics, Master's Degree, Applied Mathematics at Isfahan University of Technology
Doctor of Philosophy (Ph.D.), Mathematics, Doctor of Philosophy (Ph.D.), Mathematics at George Mason University
Persian, English, French, German