I am a tenured Associate Professor in Computer Science at the University of Southern California, where I direct the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab, and a machine learning engineer at Waymo working on world models for autonomous driving simulation and scenario generation.

My research focuses on the robustness, evaluation, and alignment of embodied AI systems operating in complex real-world environments. It spans scenario generation, red-teaming, quality diversity optimization, and generative simulation, with the goal of systematically surfacing failures and edge cases before deployment — and generating the diverse, challenging experiences that enable robots and autonomous agents to develop robust, generalizable behaviors.

This work spans the spectrum from theory to real-world deployment, from fundamental advances in evolutionary computation and quality-diversity optimization — including CMA-ME, CMA-MAE, Discounted Model Search -- to deployed robotic systems that assist users in manufacturing, mining, and assistive care tasks. Our work has received oral presentations at NeurIPS and ICLR, best paper awards and nominations at GECCO and HRI, and our open-source quality-diversity library pyribs is used by research labs worldwide. I have also contributed to the Waymo World Model, a generative model for realistic simulation of rare and safety-critical driving scenarios.

I am the recipient of the NSF CAREER Award, the Agilent Early Career Professor Award, and the Okawa Foundation Research Award. Students that I have mentored have received ACM dissertation awards and are currently researchers at organizations including Waymo, OpenAI, Anthropic, and Lila Sciences.