About Me

Why I am a control theorist


We are firmly in the age of automation and artificial intelligence. Automatic control algorithms will appear more physically and more pervasively in every aspect of our lives. What the systems that we create today do, how they act, the choices they make, and how we choose to design them will set the stage for humanity's relationship with machines for years to come. But I am deeply worried about unsafe automatic systems, created without formal guarantees about their behavior, or worse, about hidden mistakes from their engineering process that cause destruction and unforeseen consequences — having written a lot of software myself, I know how easy it is to unintentionally create bugs in code.

We can do better. I believe we can make safe, reliable software. I believe that we should not be afraid of the systems we create, because we should know everything about the systems we create. I believe we can even automatically generate correct-by-construction control algorithms using the very computers that will then execute these algorithms. This is possible if we implement good tools and philosophies from the start. Control theory, modeling, and formal verification, properly and broadly applied, will provide unprecedented assurance that autonomous systems — including physical ones like robots, self-driving cars, and airplanes, as well as virtual ones like operations research, scheduling, financial, and logistics systems — function as intended. These theoretical tools are not just nice to have — they are essential for continued technological development.

Because I believe in the good that is possible when we slow down, be careful, and do it right from the start, I spend my days advancing the state of the art in theory, tools, and education related to safe and reliable automation.


Currently, I am a postdoctoral fellow at the Institute for Computational Engineering and Sciences at the University of Texas at Austin, where I explore and invent new formal methods for autonomy and verification of machine learning systems. I completed my PhD in Control and Dynamical Systems at the California Institute of Technology (Caltech), funded by a National Defense Science and Engineering Graduate Fellowship (NDSEG) and by Boeing. My PhD research, supervised by Richard Murray in the Networked Control Systems Lab, was on robustness, adaptation, and learning in optimal control — in particular, I applied convex optimization methods and formal verification techniques to aerospace systems. I received my BS and MS degrees, both in Electrical Engineering, from Stanford University in 2011.