I am an applied scientist broadly interested in unlocking reinforcement learning, improving sample efficiency, and robustly learning from imperfect labels. I have spent the past five years solving machine learning problems at the boundary of research and engineering, mostly at small startups. I am trained as a theoretical biophysicist, where my PhD research advanced our understanding of the emergence of antibiotic resistance in microbial communities by combining mathematical modeling, high-performance computing, and machine learning algorithms.
Active Ideas
These are the projects that I am actively working on today as I have time:
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Small Language Models + RL Investigating task-specific small language models optimized using reinforcement learning on a single consumer graphics card.
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Agentic RL Exploring self-evolving agentic frameworks using role-specific small language models with verifiable rewards.
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3D Pose Estimation Continuing to explore applying the latest computer vision models to analyzing powerlifting form.
Timeline
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Harpin AI
Combined agentic AI, predictive models, and identity resolution to understand customers.
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Agentic AI
Trained neural networks to play video games.
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FICO
Applied explainability methods to financial models.
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University of Michigan
Completed biophysics PhD studying emergent behavior in mathematical models of antibiotic resistance.
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Caltech
Completed undergrad studies in applied physics.