About Me

Why I do control theory

I believe that control, automation, and artificial intelligence are here to stay. Automatic control algorithms will appear more physically and more pervasively in every aspect of our lives. However, with this increase in automation, I am deeply worried about systems that are conceived and implemented ad hoc, without formal guarantees about their behavior, and worse, about hidden mistakes from the 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.

Luckily, much can be done to address many aspects of these problems when good tools and philosophies are implemented from the start. I believe that modeling, control theory, and formal verification, properly and broadly applied, can provide an unprecedented level of assurance that computer controlled 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. To that end, I have devoted much of my professional life to advancing state-of-the-art theory, tools, and education related to safe and reliable automation.

Bio

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.