Jonathan Tu

Postdoctoral research associate

University of California, Berkeley
Department of Electrical Engineering and Computer Science
Cory Hall 558
Berkeley, CA 94720-1774

jhtu [at] berkeley [dot] edu

Current position

I'm a postdoc at the University of California, Berkeley, where I am co-advised by Professors Murat Arcak (personal page, group page) and Michel Maharbiz (personal page, group page). My research deals with the design and control of small-scale, hybrid biotic/abiotic systems. Specifically, I am looking at design issues related to the fluid mechanics of flagellar locomotion at low Reynolds numbers.

Previous education

I grew up in Shoreline, WA, a suburb of Seattle. A lifelong Huskies fan, I did my undergraduate studies at the University of Washington, where I earned a double degree in Aeronautics and Astronautics and Mathematics. I worked on many research projects during that time, including the design and manufacturing of magnetic probes at Redmond Plasma Physics Laboratory, as well as numerical and theoretical analysis of mode-locked lasers, under Professor J. Nathan Kutz (personal page, group page).

From there, I went to Princeton University, where I obtained a Ph. D. under the mentorship of Professor Clancy Rowley (personal page). My research there involved reduced-order modeling and data-driven analysis of fluid mechanical systems, falling under the general topic of flow control. In particular, I focused on a method called dynamic mode decomposition (DMD), applying it to study various fluid flows and also developing its underlying theory.

Research interests

I am interested in the data-driven characterization of complex dynamical systems, with the end goal of forming reduced-order models for their behavior and modifying that behavior through control. This relies on two key concepts:

My research looks to combine tools from dynamical systems theory, control theory, signal processing, and computer science to model complex systems directly from the data they generate. Some of these tools include dynamic mode decomposition (DMD), compressed sensing, equation-free modeling, and machine learning.


In addition to my research, I am passionate about education, both in the university setting and more generally. At Princeton, I was an Assistant-in-Instruction (AI) for the following courses:

I also completed the Teaching Transcript Program through Princeton's McGraw Center for Teaching and Learning, which is a certification program for graduate students who wish to receive additional training in teaching methods. As part of this, I participated in a year-long seminar on evidence-based pedagogy and the challenges of teaching at a research university. I later ran an orientation to prepare graduate students for their first teaching assignments at Princeton.

In addition to teaching courses, I also had the opportunity to assist in mentoring undergraduates. This includes students who worked with me as summer research assistants and those who worked with me for their senior thesis projects:


I am one of the authors of modred, a Python module for modal decomposition and model reduction. modred is parallelized "under the hood," so users can run the code in parallel with minimal knowledge of MPI concepts. The code is also fully unit-tested and well-documented. You can find modred at the Python package index (source code, documentation).