Academic Leadership & Initiatives

Stanford Bio-X 2024 Interdisciplinary Seed Grant Recipient

I am the lead postdoctoral researcher and the main contributor on the awarded project:

Enhancing Specialty Care with Digital Medical Consultations: A Retrieval-Augmented Language Model Approach

This work was selected for funding through the Stanford Bio-X Interdisciplinary Initiatives Program Seed Grant, which supports high-impact collaborative science.

• Official PI: Dr. Jonathan H. Chen
• Co-PIs: Dr. Michael Bernstein, Dr. Robert Tibshirani, Dr. Mary Kane Goldstein
• Institution: Stanford University
• My Role: Lead Postdoctoral Researcher and Grant Lead

This interdisciplinary project leverages RAG and LLMs to:

• Automate clinical consultations by suggesting guideline-aligned templates
• Improve access to specialty care
• Scale equitable, data-driven decision support across health systems

Stanford HAI AI Index 2025 Contributor

I am contributing to the 2025 Stanford HAI AI Index Report.

I specifically contributed to Chapter 5: Science and Medicine, which explores the impact of AI on healthcare and biomedical research. My role focuses on:

• Highlighting major trends in non-imaging medical AI
• Surfacing data related to clinical NLP, decision support, and LLM evaluation
• Supporting global, accessible, policy-relevant insights for a broad audience

The AI Index Report is aimed at policymakers, researchers, and the public, and my role has included drafting content, identifying quantitative sources, and ensuring clarity in communication.

Session Organizer — PSB 2025 & 2026

I co-organize the session AI and Machine Learning in Clinical Medicine at the Pacific Symposium on Biocomputing (PSB), a leading venue for interdisciplinary work at the intersection of biology, medicine, and computation.

This session focuses on the growing deployment of AI systems in clinical care and the increasing need for interpretable, reliable, and fair systems. Topics include:

• Explainability techniques for clinical ML/AI
• Evaluation of trustworthiness and factuality
• Real-world applications and policy implications of clinical LLMs
• Bias and fairness in healthcare AI systems

Our goal is to foster a rigorous scientific community that addresses the practical and ethical challenges of deploying AI in healthcare.

Program Chair — ECML PKDD 2024

I served on the Program Committee for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024 Research Track.

As part of the PC, I reviewed submissions and contributed to maintaining the scientific rigor of one of Europe’s most prestigious machine learning conferences.