Hello! Thank you for taking a look at my profile. I'm a machine learning researcher based in the United States.
I've been passionate about programming and mathematics for as long as I can remember. When I began, the concept of AI was drastically different than now. However, the idea of AI piqued my interest immediately and has since developed into a hobby and profession. After starting my undergraduate program, I began independent and sanctioned research projects into reinforcement learning, natural language processing, and computer vision. Later, throughout my PhD, I continued to research machine learning with a particular focus on low-level understanding.
For samples of my work, feel free to browse my public-facing repositories:
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DreamerX is an implementation of model-based DreamerV3 with minor optimizations and novel training adjustments. It is designed to be flexible and user-friendly, allowing researchers and practitioners to easily interchange components and environments. |
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Massively multi-agent (10k+) reinforcement learning methodology for multimodal processing and trajectory prediction. The library includes custom distributed training logic using NCCL and Ray. |
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Novel joint variational autoencoder methodology allowing for multimodal integration and cross-modal imputation. Also includes interpretability components and extensive benchmarks. |
During the course of my education, I've held several roles that have given me opportunities to develop impactful methods and projects, including:
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Creating an agentic LLM application providing emotional support to mothers in the workforce, including implementation of traditional and reinforcement learning fine-tuning and evaluation pipelines with an emphasis on user safety. |
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Leading research teams in novel methodological research resulting in publications in reinforcement learning, variational methods, graph learning, and optimal transport in Q1 AI journals. Concurrently developing open-source libraries to encourage innovation. |
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Performing research into data anonymization and conducting briefings on nascent ML technologies, including generative modeling. |
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Designing and maintaining the talent-employer matching algorithm (2018-2022) for 500K+ users, as well as client contract prioritization and outcome prediction. Concurrently maintained cross-team and backend databases. |
I'm currently looking for work. If you're interested in any of my libraries or would like to work together, please don't hesitate to send me an email or check out my publications.
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Inferring virtual cell environments using multi-agent reinforcement learning
Network-based drug repurposing for psychiatric disorders using single-cell genomics
Personalized Single-cell Transcriptomics Reveals Molecular Diversity in Alzheimer’s Disease
Joint variational autoencoders for multimodal imputation and embedding BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids
Last updated: February 16, 2026