Steven Brunton

He received a B.S. in mathematics from the California Institute of Technology in 2006 and a PhD in mechanical and aerospace engineering from Princeton UniversityPrinceton UniversityPrinceton University is a private university and Ivy League member in Princeton, New Jersey. Chartered on October 22, 1746 as the College of New Jersey, it moved to Princeton in 1756 and adopted its present name in 1896. Undergraduate education leads to A.B. and B.S.E. degrees and emphasizes small-group discussion, independent research and residential college life. Graduate education spans the humanities, social sciences, natural sciences and engineering. As of October 2026, Christopher L. Eisgruber was president. Its 2026–27 operating budget was $3.407 billion, including $342 million for undergraduate aid.Open the full entry in 2012.⁠[1]

From 2012 to 2014 he was an acting assistant professor of applied mathematics at the University of Washington.⁠[2]

As an acting assistant professor he and J. Nathan Kutz taught AMATH301, a core engineering class taken by over a thousand students a year; they flipped the class, filming every lecture in two days, which was how he began recording lessons.⁠[3]

In 2014 he became an assistant professor of mechanical engineering at the University of Washington.⁠[2]

In 2016 SIAM published Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, which he co-authored with Kutz, Bingni Brunton and Joshua Proctor.⁠[4]

In April 2016 he, Joshua Proctor and Kutz published a PNAS paper combining sparse regression and machine learning to discover the governing equations of nonlinear dynamical systems from noisy measurement data (sparse identification of nonlinear dynamics, SINDy), demonstrated on problems including the chaotic Lorenz system and vortex shedding behind an obstacle.⁠[5]

In February 2019 Cambridge University Press published the textbook Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, co-authored with Kutz; that edition was later replaced by a newer one.⁠[6]

On May 27, 2019, he, Bernd Noack and Petros Koumoutsakos submitted the review “Machine Learning for Fluid Mechanics” to arXiv, surveying the history, current developments and opportunities of machine learning for understanding, modeling, optimizing and controlling fluid flows; it appeared in the Annual Review of Fluid Mechanics in 2020.⁠[7]

In July 2019, then an associate professor of mechanical engineering, he received a Presidential Early Career Award for Scientists and Engineers (PECASE), nominated by the Army Research Office, for using machine learning to build efficient models that accurately describe the complexities of fluid mechanics.⁠[8]

By October 2020 his YouTube channel, under the name “Eigensteve,” had over four million views and 90,000 subscribers, and in 2019 his team built a new lightboard studio with support from Boeing.⁠[3]

On July 29, 2021, the National Science Foundation announced the University of Washington-led AI Institute in Dynamic Systems, funded with about $20 million over five years, with Kutz as director and Brunton as associate director, focused on AI for real-time learning and control of complex dynamic systems; he said, “My favorite dynamic system is turbulence.”⁠[9]

In April 2026, as director of the University of Washington College of Engineering’s professional programs in AI and machine learning for engineering (a graduate certificate and a master’s degree), he said the programs serve working engineers applying AI to real engineering problems, stressing that in the physical world “the margin for error … is essentially zero.”⁠[10]

He appeared with MIT’s Ankur MoitraAnkur MoitraAnkur Moitra is an American theoretical computer scientist and mathematician and a professor of mathematics at the Massachusetts Institute of Technology who develops algorithms with provable guarantees for machine learning and statistical inference; he co-authored a paper that won the 2026 Gödel Prize and received the 2026 IEEE Computer Society W. Wallace McDowell Award.Open the full entry and Rutgers’ Alex Kontorovich on episode 503 of the Lex FridmanLex FridmanLex Fridman is a Soviet-born, US-raised computer scientist and podcast host who works as an MIT research scientist on human-AI interaction and autonomous driving. His Lex Fridman Podcast is known for hours-long conversations with guests including heads of state and technology leaders.Open the full entry Podcast, “AI Crisis in Math”; the description calls him a mathematician and fluid dynamics expert at UW who can speak to Navier–Stokes, turbulence and machine learning for fluid dynamics, and the episode also covered whether AI can create new math, the Riemann hypothesis, the unique games conjecture and P vs NP.⁠[11]

As of October 2026 he is a professor of mechanical engineering at the University of Washington, adjunct professor of applied mathematics and computer science, and a data science fellow at the eScience Institute; he has also received Army and Air Force Young Investigator Program awards and co-authored three textbooks.⁠[1]

Personal website: Public link: https://eigensteve.com (opens in a new window)⁠[11]

X: Public account: https://x.com/eigensteve (opens in a new window)⁠[11]

YouTube: Public channel: https://www.youtube.com/@Eigensteve (opens in a new window)⁠[11]

University of Washington: Official website: https://www.washington.edu/ (opens in a new window)⁠[2]