About
I am a final-year Ph.D. candidate in Electrical & Computer Engineering at Duke University, advised by Prof. Maciej A. Mazurowski. I develop foundation models and agentic AI systems that reason across images and language, use specialized tools, and adapt to complex real-world tasks.
My work spans LLM-agent optimization at Meta, in-context 3D medical image segmentation at Siemens Healthineers, and large-scale multimodal learning at Duke. I previously earned my M.S. in Computer Science at Duke, where I worked with Prof. Guillermo Sapiro, and B.S. degrees in Mathematics–Computer Science and Cognitive Science at UC San Diego, where I worked with Prof. Zhuowen Tu.
I am seeking research scientist, applied scientist, and research engineering positions starting in Summer 2027.
Selected Work
Meta-Harness — LLM agent optimization
Improved Meta-Harness’s algorithm-evolution pipeline by evolving agent trajectories and dynamically adapting training data. Increased performance from 49% to 62% on math-solving tasks and from 68% to 71.3% on Terminal-Bench 2.
SNAIL — In-context 3D medical image segmentation
Developed a training-free support–query framework for segmenting unseen anatomy from five labeled examples, with preprocessing and evaluation infrastructure spanning 14K MRI/CT volumes. Achieved 54.9% Dice without retraining versus 56.9% for a supervised nnU-Net on AMOS CT. Co-inventor on the resulting patent-pending technology assigned to Siemens Medical Solutions USA, Inc.
MRI-CORE — MRI foundation model
Pretrained a foundation model on 7M MRI slices, improving few-shot downstream segmentation by 5 Dice points over SAM, and extended this direction toward multimodal 3D MRI and radiology-report alignment.
MMedAgent — Multimodal medical agent
Developed a multimodal agent that coordinates specialized medical tools across imaging and language tasks; published in Findings of EMNLP 2024.
Experience
Software Engineering Intern, Meta · May 2026 – Present
Worked on algorithm evolution for LLM agents, making trajectories evolvable and adapting training data to strengthen optimization signals.
AI Research Intern, Siemens Healthineers · June 2025 – August 2025
Developed SNAIL, a training-free in-context learning framework for 3D CT segmentation of unseen anatomy, resulting in patent-pending technology assigned to Siemens Medical Solutions USA, Inc.
Recent News
- [Jan. 2026] Our work on Fréchet Radiomic Distance was published in Medical Image Analysis.
- [Jan. 2026] Our work applying SAM 2 to 2D and 3D medical images was published in IEEE Transactions on Biomedical Engineering.
- [Dec. 2025] Our work on breast MRI registration was published in IEEE Journal of Biomedical and Health Informatics.
- [Oct. 2025] Our work on AI-based breast density quantification was published in npj Breast Cancer.
- [Oct. 2025] Our work on volumetric annotation with SAM 2 was published in IEEE Transactions on Medical Imaging.
- [July 2025] We released MRI-CORE, a foundation model for magnetic resonance imaging.