Annie S. Chen

Hi! I am a Research Scientist at Google DeepMind. I recently completed my PhD in Computer Science at Stanford University, advised by Prof. Chelsea Finn and affiliated with the Stanford Artificial Intelligence Laboratory (SAIL). During my PhD, I was supported by an NSF Graduate Research Fellowship and an OpenAI Superalignment Fellowship. I also spent six months as a full-time student researcher at Google DeepMind in London. Previously, in 2021, I received a joint B.S. in math and M.S. in computer science, both also at Stanford.

I am originally from Boulder, Colorado, and outside of research, I enjoy spending time outdoors (hiking and backpacking), playing tennis, and learning to play the guitar. For three years I organized the Stanford CS Undergraduate Mentoring Program to help undergraduate students get involved with computer science research.

My research focuses on developing principled methods for advancing the capabilities and reliability of intelligent agents in the physical world. I am excited by a broad range of machine learning topics, including reinforcement learning, data curation, test-time reasoning, robustness, out-of-distribution generalization, and embodied AI.

In particular, I have been interested in how we can design self-improvement pipelines that leverage an agent's direct experience as information-rich probes of its weaknesses and opportunities for exploration. Some of my recent work has shown how we can harness experiential signals to push model performance through curating high-quality data [12], efficient exploration in reinforcement learning [14], and adaptive test-time reasoning at the timestep level and strategy level [10, 11]. This self-improving feedback-centric perspective has also informed my work on training and fine-tuning models to handle spurious correlations and generalize out-of-distribution [2, 5, 7, 8].

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Please see my CV or Google Scholar for a full list of work.