About Me
Princeton CS 2027 | Reinforcement Learning, LLMs, Semantic
Representations, Diversity
Research @
Reinforcement Learning Lab
Let’s talk about research or ideas for making adaptive intelligent
systems that align with humanity.
I also create art and write humor focused on the tech world, AI, and
the tension between humans and machines.
Selected Research
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NeurIPS 2026 LP4FM, WiML & Queer in AI poster | Escaping the Obvious: Reinforcement Learning for Conceptually Diverse Generation
Large Language Models often converge to a narrow set of
high-probability ideas. This research investigates methods
to mitigate this tendency, evaluating how RL changes the
semantic geometry of model outputs for conceptually diverse generation.
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Empowerment—an objective that measures how much influence an
agent has over its environment—provides a principled way to
learn concise representations that capture only the aspects
of the environment that matter for control while ignoring
irrelevant distractors.
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This project evaluates the robustness of Maximum Entropy
Reinforcement Learning (MaxEnt RL) via Energy-Based
Normalizing Flows (EBFlow). While RL algorithms are
typically tested in clean, idealized simulations, this work
introduces realistic disturbances—including sensor
corruption, action noise, and observation noise—using tools
from the Robust Gym benchmark to assess generalization.
Published Humor
Art
Interactive Art
Intimate Machines
Four Types of AI
Portraits