J. Nathan Kutz

J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington, affiliated with the College of Arts & Sciences. He also holds positions as Director of the AI Institute in Dynamic Systems, Boeing Professor of AI and Data-Driven Engineering, and Professor of Electrical and Computer Engineering. His research focuses on data-driven dynamical systems, machine learning, computational neuroscience, and optical physics. Kutz leads the Kutz Research Group, which explores topics such as reduced-order modeling, dynamic mode decomposition, and sparsity-driven dynamics.

His academic contributions span interdisciplinary fields, including neuroscience (e.g., neural network functionality and neurodegenerative diseases), fluid dynamics (flow classification and control), and computer vision (video processing and gesture recognition). He has advised numerous PhD students and postdoctoral researchers, fostering collaborative projects across applied mathematics, physics, and engineering.

Kutz's work emphasizes equation-free modeling and sparse sampling techniques, enabling efficient analysis of complex systems. His group develops open-source tools and frameworks for data assimilation and control, with applications in robotics, metamaterials, and biological systems.