About Me
I'm a machine learning research scientist based in NYC. I am part of Morgan Stanley's Machine Learning Research group. I'm currently focused on large language models, working on both research and applied aspects. For applied work, I specialize in model fine-tuning (adaptive pretraining, SFT, and RL) and building agentic workflows for frontier models. From a research perspective, I'm interested in advancing RL techniques for LLMs, exploring novel loss signals, and investigating fundamental ML questions like model initialization effects on optimization and the lottery ticket hypothesis.
I earned my PhD in Computer Science at Cornell in 2024, working with Professor Carla Gomes in Computational Sustainability. Before that, I studied Bioengineering and Physics at the University of Maryland, graduating in 2016.
More broadly, I’m interested in how AI intersects with art and philosophy, not just as a tool, but as a way to understand consciousness, spark awe, and open new directions for research.