Healthcare AI · Medical imaging · Physics
Research Scientist and Physicist
Massachusetts General Hospital | Harvard Medical School
I develop AI methods that connect biomedical data with clinical practice. My research spans medical imaging, electronic health records, omics, physiological time series, and behavioral data, with the aim of improving patient care.
A current focus is patient health timeline modeling: learning from clinical events to simulate possible health trajectories. This work includes ETHOS and adaptive risk estimation, alongside open data standards for reproducible healthcare AI.
Explore my research“Science is the belief in the ignorance of experts.”
Selected work
The Lancet Digital Health, 8(8), 101011, 2026 · Viewpoint
A perspective on learning directly from tokenized medical data. Using ETHOS as an example, we discuss patient health timeline models as a foundation for simulating clinical trajectories and sharing models across institutions without sharing patient data.
NEJM AI, 3(6), 2026 · Review Article
The Medical Event Data Standard (MEDS) provides a shared, open-source foundation for health AI research. This review describes the standard, its growing ecosystem, and its role in reproducible workflows and foundation model development.
DOI: 10.1056/AIra2501253
NeurIPS 2025
A hierarchical mixture-of-experts architecture built on Mamba for 3D medical image segmentation. The model combines local and global context across CT, MRI, and ultrasound data.
GigaScience, Volume 14, 2025
ETHOS-ARES uses patient health timelines to estimate changing clinical risks without task-specific fine-tuning. The framework supports adaptive risk assessment from electronic health records using the MEDS data format.
