Maziar Raissi

Maziar Raissi is an applied mathematician whose work brings physical laws into machine learning for scientific problems. He coauthored a 2017 paper that introduced physics-informed neural networks. By 2020, he had earned a PhD at the University of Maryland, College Park, completed postdoctoral research at Brown University, and worked as a senior software engineer at NVIDIA before joining the University of Colorado Boulder as an assistant professor. He joined UC Riverside in 2023, was promoted to associate professor with tenure by March 2026, and is now an associate professor of mathematics; his lab develops physics-informed networks, and in October 2026 he described simulation-based AI for geothermal energy through MAESTRO.

Now at: University of California, Riverside

Timeline

2017

Physics-informed neural networks

With Paris Perdikaris and George Em Karniadakis, he introduces physics-informed neural networks in a 2017 paper. The networks train on data while following nonlinear partial differential equations.
By 2020

From Brown to NVIDIA

He earns a PhD at the University of Maryland, College Park, completes postdoctoral research at Brown University, and works as a senior software engineer at NVIDIA in Silicon Valley. In January 2020, he joins the University of Colorado Boulder as an assistant professor of applied mathematics.
2020

Modeling aircraft-cabin airflow

In 2020, the NSF funds his project to model how air flow in aircraft cabins influences contagion with computational mathematics and physics-informed deep learning.
2023

Joining UC Riverside

In 2023, he joins UC Riverside as an assistant professor of applied mathematics.
By March 2026

Associate professor with tenure

By March 2026, UC Riverside promotes him to associate professor with tenure.
As of October 2026

Associate professor at UC Riverside

As of October 2026, he is an associate professor of mathematics at UC Riverside. His lab develops physics-informed neural networks that embed physical laws in their architecture. In October 2026, he describes the MAESTRO geothermal AI project as learning through simulation; its feedback loop runs targeted simulations when predictions are uncertain.

Career

Since 2023University of California, RiversideAssistant Professor, Associate Professor of Mathematics
Until 2020Brown UniversityPostdoctoral Researcher
Until 2020NVIDIASenior Software Engineer
From 2020University of Colorado BoulderAssistant Professor