I am a senior undergraduate in Computer Science at Princeton University, where I am advised by Tom Griffiths . I am pursuing minors in Cognitive Science and Latin American Studies. I also serve as a Writing Center Head Fellow and Editor-In-Chief of the Tortoise Journal.
I am broadly interested in reinforcement learning as a methodology for building more efficient, generalizable and structured AI systems. Under the mentorship of Brad Malin at Vanderbilt University Medical Center, I developed a sample-efficient RL-based generative model for privacy-preserving synthetic health data that outperformed existing state-of-the-art GAN and diffusion models, particularly when confronted with limited training samples. For my junior thesis at Princeton, I explored how an agent could ‘learn to walk like humans do’ via a developmentally inspired RL curriculum and expanding neural networks. My senior thesis extends this work by introducing a hierarchical RL framework that sequences motor skills through value functions, enabling online composition.
My research is more broadly motivated by human-centered AI: both how AI can help humans, and how insights from human learning and cognition can inspire more efficient algorithms. Previously, I’ve worked on various applications of machine learning in healthcare settings. Under the mentorship of Weiqing Gu at Dasion , I built a voice analysis model to detect various medical conditions—including autism, diabetes and depression—from audio data. I also spent a summer at the University of Macedonia–Thessaloniki, where I built a stacked autoencoder model to classify MRI brain scans as autistic or normally developing.
I am applying to PhD programs this application cycle.
Publications
Preprint
Synthetic data generation is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models often require large datasets and complex training procedures, limiting their applicability in small-sample settings common in biomedical research. This study aims to develop a more principled and efficient approach to SDG and evaluate its efficacy for biomedical applications. In this work, we reframe SDG as a reinforcement learning (RL) problem and introduce RLSyn, a novel framework that models the data generator as a stochastic policy over patient records and optimizes it using Proximal Policy Optimization with discriminator-derived rewards. We evaluate RLSyn on two biomedical datasets--AI-READI and MIMIC-IV--and benchmark it against state-of-the-art generative adversarial networks (GANs) and diffusion-based methods across extensive privacy, utility, and fidelity evaluations. On MIMIC-IV, RLSyn achieves predictive utility comparable to diffusion models (S2R AUC 0.902 vs 0.906 respectively) while slightly outperforming them in fidelity (NMI 0.001 vs. 0.003; DWD 2.073 vs. 2.797) and achieving comparable, low privacy risk (~0.50 membership inference risk AUC). On the smaller AI-READI dataset, RLSyn again matches diffusion-based utility (S2R AUC 0.873 vs. 0.871), while achieving higher fidelity (NMI 0.001 vs. 0.002; DWD 13.352 vs. 16.441) and significantly lower vulnerability to membership inference attacks (AUC 0.544 vs. 0.601). Both RLSyn and diffusion-based models substantially outperform GANs across utility and fidelity on both datasets. Our results suggest that reinforcement learning provides a principled and effective approach for synthetic biomedical data generation, particularly in data-scarce regimes.
Research
Junior Thesis at Princeton University, Spring 2025
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This project explored how principles of human motor development could improve reinforcement learning for humanoid locomotion. Two strategies were investigated in parallel: (i) a curriculum of locomotion subtasks aligned with developmental milestones (ex: crawling before standing) and (ii) progressively expanding neural networks that “grow” in depth or width as task complexity increases. Baseline results highlighted both the promise and the challenges of transferring knowledge across subtasks. These experiments laid the groundwork for the senior thesis work detailed above.
Final Project for COS 435 at Princeton University, Spring 2025
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This project investigated the role of representations in MR.Q, a generalist reinforcement learning algorithm. We asked two key questions: (i) Does an explicit planner still matter once you have a strong MR.Q-style representation? and (ii) How small can that representation become before performance deteriorates? We found that a one-step planning update often failed to help and even hurt performance - particularly in sparse-reward, pixel-based Atari tasks - while scaling down representation size proved more forgiving in discrete or lower-dimensional domains than in complex continuous-control settings.
Junior Independent Work at Princeton University, Fall 2024
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This project applied machine learning to forecast drug trafficking activity across Colombia’s departments using United Nations seizure data. Socioeconomic indicators and engineered time-series features were used to train Random Forest, SVM and XGBoost models, with XGBoost achieving the best performance. The analysis revealed key drivers such as crime rates, government operations and urban–rural population patterns, offering an empirical analysis of existing socioeconomic theories.
Research at University of Macedonia-Thessaloniki, Summer 2024
Code
This project applied stacked autoencoders to structural MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) dataset. The models were trained to compress and reconstruct brain images, and the learned representations were used to classify scans as autistic or normally developing. To probe group differences, the models were cross-tested - trained on one group and evaluated on the other - so that discrepancies in reconstruction quality could highlight structural variations between autistic and non-autistic brains.
Industry Research at Dasion, 2023-2024
This project developed machine learning pipelines to diagnose various health conditions from voice recordings. Work focused on creating robust preprocessing strategies to handle noisy, real-world data, along with advanced feature extraction and classification techniques to support accurate diagnosis.
Last updated October 2025