Research

My research spans clinical AI systems, computational cancer biology, open-source bioinformatics, and medical education. Below is a summary of the major threads of my work.

Clinical AI Systems

My current work at Stanford Health Care focuses on the design, engineering, deployment, evaluation, monitoring, and governance of AI systems embedded in clinical workflows. I am particularly interested in the infrastructure and engineering practices required to translate promising prototypes into durable clinical systems.

Clinical AI platforms and infrastructure

A central project is ChatEHR, Stanford Health Care’s proprietary LLM-powered clinical AI platform, which supports both interactive and automated use of large language models with longitudinal patient data from the electronic health record. My work includes EHR integration, model and tool interfaces, clinician-facing application design, and the production infrastructure needed to operate these systems reliably.

Agentic workflows and harness engineering

I design and evaluate workflows that coordinate models, clinical data, and tools for specific chart review and abstraction tasks. This includes decisions about tool use, progressive disclosure of context, orchestration, and when deterministic versus agentic harness patterns are appropriate. Applications include identifying patients who may benefit from a palliative medicine consult, detecting post-operative surgical-site infections, triaging surgical patients for co-management, and preparing case summaries for tumor board conferences.

Evaluation, monitoring, and governance

My work also includes model and system evaluation and continuous monitoring of deployed AI systems—developing frameworks for what to measure, how often to measure it, who responds, and what “actionable” looks like in practice. This spans pre-deployment evaluation through real-world clinical assessment and post-deployment oversight, with attention to system integrity, performance, and impact. Related work includes:

Computational Cancer Biology

During my PhD in the laboratories of Kara Davis (Department of Pediatrics) and Garry Nolan (Department of Pathology) at Stanford, I focused on applying machine learning to high-dimensional single-cell data in the context of pediatric leukemia.

Using mass cytometry (CyTOF) and other single-cell technologies, I built predictive models of patient outcomes that operate at the time of diagnosis. In collaboration with Erin Craig, Robert Tibshirani, and colleagues, I helped develop mixture modeling for multiple-instance learning (MMIL), a statistical framework that identifies disease-relevant cells using patient-level labels when cell-level labels are unavailable. Applied to acute leukemia, MMIL distinguished leukemia cells from nonleukemic cells—including rare measurable residual disease—and identified biologically relevant features with performance approaching that of a hematopathologist.

A key output of this work was the development of tidytof, an R package providing a user-friendly, tidy interface for scalable and reproducible analysis of high-dimensional cytometry data.

Open-Source Bioinformatics

I’m deeply committed to building open-source tools that make computational biology more accessible and reproducible:

  • tidytof — An R/Bioconductor package for high-dimensional cytometry data analysis using tidy data principles. Published in Bioinformatics Advances (2023).

  • tidyomics — A software ecosystem bridging Bioconductor to the tidy R paradigm, enabling streamlined analysis of multi-omic data. I’m a co-first author on the Nature Methods paper (2024) describing this ecosystem, which we demonstrated by analyzing 7.5 million cells from the Human Cell Atlas.

  • CytofIn — A data integration strategy for combining public mass cytometry datasets using generalized anchors, published in Nature Communications (2022).

Medical Education

I was a founding member of the Medical Student Pride Alliance (MSPA), a 501(c)(3) non-profit advocating for diversity, equity, and inclusion for LGBTQ+ medical students across the United States. In my role as MSPA’s resident data scientist, I analyzed and visualized data guiding strategic decision-making and academic publications.