Caroline Wang

I am currently on the job market for industry research scientist roles—feel free to reach out!

I am a final year Computer Science Ph.D. student at UT Austin, supervised by Peter Stone. I am fortunate to have previously interned at Google DeepMind, where I was hosted by Pablo S. Castro, and at Sony AI, where I was hosted by Varun Kompella. I’m currently motivated by a vision of AI agents that can work alongside humans—contributing to and benefiting from the collective knowledge and effort that drives human progress, unlocking new forms of collaboration that neither humans nor AI could achieve alone, and ensuring that such collaboration is steered by shared values.

My PhD work pursues the above vision through the lens of multi-agent reinforcement learning: understanding cooperative dynamics in multi-agent settings, and designing algorithms to enable agents to coordinate with previously unseen teammates towards shared goals (ad hoc teamwork). More broadly, I’m interested in the principles of learning necessary for cooperation, generalization, and emergent abilities in rich multi-agent systems, including continual learning and agents learning from other agents.

I received a B.S. in Mathematics and Computer Science from Duke University, where I researched interpretable machine learning methods for criminal recidivism prediction with Cynthia Rudin, including an analysis of the COMPAS algorithm that offered an alternative explanation for its previously observed racial bias. My work was recognized with a Goldwater Scholarship, a premier undergraduate research award in the United States.