Research

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  Cost-Efficient Serving of LLM Agents via Test-Time Plan Caching

  Qizheng Zhang, Michael Wornow, Kunle Olukotun

  NeurIPS (2025)

• 

  MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

  Suhana Bedi*, Hejie Cui*, Miguel Fuentes*, Alyssa Unell*, Michael Wornow, et al. (+ many authors)

  Nature Medicine (2025)

  Website  |  Blog  |  Tweet  |  Github

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  Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs

  Michael Wornow*, Suhana Bedi*, Miguel Fuentes, Ethan Steinberg, Jason Fries, Christopher Ré, Sanmi Koyejo, Nigam H. Shah

  ICLR (2025)

  Talk  |  Github  |  Huggingface

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  Top of the CLASS: Benchmarking LLM Agents on Real-World Enterprise Tasks

  Michael Wornow, Vaishnav Garodia, Vasilis Vassalos, Utkarsh Contractor

  ICLR: Workshop on Trustworthy LLMs (2025)

  Github  |  Huggingface

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  WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks

  Michael Wornow, Avanika Narayan, Ben Viggiano, Ishan S. Khare, Tathagat Verma, Tibor Thompson, Miguel Angel Fuentes Hernandez, Sudharsan Sundar, Chloe Trujillo, Krrish Chawla, Rongfei Lu, Justin Shen, Divya Nagaraj, Joshua Martinez, Vardhan Agrawal, Althea Hudson, Nigam H. Shah, Christopher Ré

  NeurIPS: Benchmarks (2024)

  Talk  |  Website  |  Tweet  |  Github  |  Dataset

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  Automating the Enterprise with Foundation Models

  Michael Wornow*, Avanika Narayan*, Krista Opsahl-Ong, Quinn McIntyre, Nigam H. Shah, Christopher Ré

  VLDB (2024)

  Talk  |  Blog  |  Tweet  |  Github

• 

  Zero-Shot Clinical Trial Patient Matching with LLMs

  Michael Wornow*, Alejandro Lozano*, Dev Dash, Jenelle Jindal, Kenneth W. Mahaffey, Nigam H. Shah

  NEJM AI (2024)

  Talk  |  Website  |  Github

• 

  EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

  Michael Wornow*, Rahul Thapa*, Ethan Steinberg, Jason Fries, Nigam H. Shah

  NeurIPS: Benchmarks (2023) — Spotlight

  Talk  |  Website  |  Github  |  Dataset  |  Huggingface

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  HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

  Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin W. Thomas, Michael Wornow, Callum Birch Sykes, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Christopher Ré

  NeurIPS (2023) — Spotlight

  Talk  |  Tweet  |  Blog  |  Github  |  Huggingface

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  The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs

  Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A. Pfeffer, Jason Fries, Nigam H. Shah

  NPJ Digital Medicine (2023)

  Talk  |  Tweet  |  Huggingface

• 

  APLUS: A Python Library for Usefulness Simulations of Machine Learning Models in Healthcare

  Michael Wornow, Elsie Ross, Alison Callahan*, Nigam H. Shah*

  Journal of Biomedical Informatics (2023)

  Github

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  Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning

  Jonathan C. Chen, Jonathan P. Chen, Max W. Shen, Michael Wornow, Minwoo Bae, Wei-Hsi Yeh, Alvin Hsu, David R. Liu

  Nature Communications (2022)

  Tweet

• 

  Cut out the annotator, keep the cutout: better segmentation with weak supervision

  Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Ré

  ICLR (2021)

  Talk

• Medical Event Data Standard (MEDS): Facilitating Machine Learning for Health

  Bert Arnrich, Edward Choi, Jason Alan Fries, Matthew B.A. McDermott, Jungwoo Oh, Tom Pollard, Nigam H. Shah, Ethan Steinberg, Michael Wornow, Robin van de Water

  ICLR: TS4H Workshop (2024)

  Github

• MEDS Decentralized, Extensible Validation (MEDS-DEV) Benchmark: Establishing Reproducibility and Comparability in ML for Health

  Kolo, et al. (+ many authors)

  ML4H: Demo Track (2024)

  Github

• A Systematic Review of Testing and Evaluation of Health Care Applications of Large Language Models (LLMs)

  Suhana Bedi, Yutong Liu, Lucy Orr-Ewing, Dev Dash, Sanmi Koyejo, Alison Callahan, Jason A. Fries, Michael Wornow, Akshay Swaminathan, Lisa Soleymani Lehmann, Hyo Jung Hong, Mehr Kashyap, Akash Chaurasia, Nirav Shah, Karandeep Singh, Troy Tazbaz, Arnold Milstein, Michael A. Pfeffer, Nigam H. Shah

  JAMA (2024)

• Standing on FURM ground - A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems

  Alison Callahan, Duncan McElfresh, Juan M. Banda, Gabrielle Bunney, Danton Char, Jonathan Chen, Conor Corbin, Debadutta Dash, Norman Downing, Sneha Jain, Nikesh Kotecha, Jonathan Masterson, Michelle M. Mello, Keith Morse, Srikar Nallan, Abby Pandya, Anurang Revri, Aditya Sharma, Christopher Sharp, Rahul Thapa, Michael Wornow, Alaa Youssef, Michael A. Pfeffer, Nigam H. Shah

  NEJM Catalyst (2024)

• Construction of disease-specific cytokine profiles by associating disease genes with immune responses

  Tianyun Liu, Shiyin Wang, Michael Wornow, Russ B. Altman

  PLOS Computational Biology (2022)

• Inter-region transfers for pandemic surges

  Kenneth A Michelson, Chris A Rees, Jayshree Sarathy, Paige VonAchen, Michael Wornow, Michael C Monuteaux, Mark I Neuman

  Clinical Infectious Diseases (2020)

• In vivo base editing restores sensory transduction and transiently improves auditory function in a mouse model of recessive deafness

  Wei-Hsi Yeh, Olga Shubina-Oleinik, Jonathan M. Levy, Bifeng Pan, Gregory A. Newby, Michael Wornow, Rachel Burt, Jonathan C. Chen, Jeffrey R. Holt, David R. Liu

  Science Translational Medicine (2020)

Education

	Stanford University | 2020 - 2025 PhD, Computer Science NSF Graduate Research Fellowship HAI Graduate Fellowship
	Harvard College | 2016 - 2020 AB, Double Major in Computer Science & Statistics Phi Beta Kappa Summa Cum Laude Undergraduate Statistics Prize John Harvard Scholar Detur Book Prize Derek Bok Certificate for Excellence in Teaching (3x)

Experience

	Microsoft Research Machine Learning Research Intern Summer 2023
	Insitro Machine Learning Intern Summer 2021
	Broad Institute of MIT & Harvard Research Assistant, David Liu Lab Fall 2018 - Spring 2020
	Bain & Company Associate Consultant Intern Summer 2019

	Goldman Sachs Global Investment Research Summer Analyst Summer 2018
	Facebook Software Engineering Intern Summer 2017
	Joint Genome Institute Bioinformatics Intern Summer 2017
	Joint BioEnergy Institute Bioinformatics Intern Summer 2015

Teaching

	Electronic Health Records and Clinical AI (BIOS 417) Instructor Fall 2024
	Machine Learning (CS 229) Course Assistant Summer 2024
	Artificial Intelligence: Principles and Techniques (CS 221) Course Assistant Fall 2023

	Graduate Cybersecurity (CS 263) Teaching Fellow Fall 2019
	Machine Learning (CS 181) Teaching Fellow Spring 2019, Spring 2020
	Introduction to Computer Science (CS 50) Teaching Fellow Fall 2017

Talks

	Thesis Defense March 2025 Links: Video
	Long Context Models for EHR Data March 2025 Links: Video
	Clinical Trial Patient Matching with LLMs (Short) January 2025 Links: Video
	Multimodal Foundation Models for Business Process Management Tasks November 2024 Links: Video
	Foundation Models for Structured EHR Data November 2024 Links: Video
	Clinical Trial Patient Matching with LLMs November 2024 Links: Video
	Automating the Enterprise With Foundation Models May 2024 Links: Video
	Large Language Models (LLMs) for Healthcare May 2024 Links: Video
	Shaky Foundations of Clinical LLMs March 2024 Links: Video
	Foundation Models for EHRs November 2023 Links: Video
	EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models November 2023 Links: Video