Michael Yao is an MD-PhD candidate at the University of Pennsylvania leveraging AI to improve human health.
Currently: Medical student, Research scientist
I am an MD-PhD candidate at the University of Pennsylvania and Research Scientist at Abridge. My research focuses on trustworthy machine learning and how we can reliably use clinical ML systems under distribution shift. I am interested in how to leverage prior knowledge and statistical methods to help algorithms better generalize to new distributions, and how we can use these methods to improve clinical decision support in rare disease diagnostics and hospital workflows. I also seek to better understand the types of distribution shift observed in clinical practice, and am also working to improve physician AI literacy in medical education.
I was advised by Osbert Bastani and James Gee during my PhD, and am grateful to be supported by an NIH F30 NRSA Fellowship from the National Institute on Minority Health and Health Disparities (NIMHD).
- 2026 | Research Scientist at Abridge
- 2025 | Received my PhD in Bioengineering and MS in Computer Science from the University of Pennsylvania
- 2025 | ML Scientist Intern at Genentech Generative AI
- 2025 | Human Frontier Collective Intern at Scale AI
- 2023 | AI Clinical Fellow at Glass Health
- 2022 | Research Scientist Intern at Microsoft Research Health Futures
- 2021 | Software Engineering Intern at Hyperfine Research
- 2021 | Graduated salutatorian from Caltech, BS Applied Physics
- 2026 | Can language models help us personalize treatment strategies for patients? Learn more about how we can use LLMs for precision medicine in our new paper accepted to ICLR 2026!
- 2025 | Can generative language models like ChatGPT help clinicians order diagnostic imaging studies in the ED? Check out our new paper in Communications Medicine to learn more! Penn press release Aunt Minnie article
- 2025 | How can we ensure that offline optimization methods propose both high-quality and diverse sets of designs? Learn more about our method DynAMO in our new paper accepted to ICML 2025!
- 2025 | Excited to share our work in Nature Communications on multimodal concept bottleneck models for interpretable eye cancer diagnostics, led by the incredible Yifan Wu!
- 2025 | Can datathons be effective venues for teaching AI to med students? Check out our work on trainee-led datathons now out in JMIR Medical Education!
- 2024 | Can we reliably optimize against surrogate objectives in offline optimization problems? Learn more about our method for Generative Adversarial Model-Based Optimization (GAMBO) accepted to NeurIPS 2024. Check out our work in Vancouver!
- 2024 | Grateful to have contributed to our NeurIPS Spotlight work on Knowledge Bottlenecks for improved interpretability and robustness of ML for healthcare, led by the fabulous Yue Yang! Penn press release
- GitHub
- X (Twitter)
- [Email [no spam]](hello [at] michaelsyao [dot] com)
- Google Scholar
- 2026 | Course Author: Ethical Algorithms for the Modern Clinician
- 2026 | Head TA: Health, Healthcare and Technology
- 2025 | TA: Distributed Systems
- 2024 | TA: Principles of Deep Learning
- 2024 | TA: Imaging Informatics
- 2024 | TA: Diagnostic Ultrasound for Medical Students
- 2024 | TA: Clinical Reasoning for Medical Students
I designed and run a short course on the fundamentals of ML for medical students. I currently serve as a president of the Penn HealthX student group, and have previously served as Vice Chair of the Technology Committee for the American Physician Scientists Association (APSA) and as Director of Data Science and AI for MDplus. At Penn, I am involved in a number of mentorship and outreach initiatives and have served on both the Admissions Committee and AI Curriculum Steering Committee for the School of Medicine.
I am actively involved in Penn's interview and recruitment process for medical school admissions. I set aside half an hour a week to meet with current students for pro bono feedback on applications and general college advice, especially for underrepresented students from minority backgrounds. I also enjoy working with and mentoring students on interesting research projects. If you're interested in connecting, please reach out to me via email.
In my free time, I enjoy solving fun math problems and festive programming puzzles with code, and contributing to open-source software. I also enjoy solving puzzles like the New York Times Games, and also building their corresponding medical knockoffs (check out Clerkship Connections and Pimping Rounds). Finally, I am a (mediocre) chess hobbyist and occasionally write on my blog.