Ethan Wu
MD/PhD Candidate · Pitt/CMU MSTP
I'm an MD/PhD student at the University of Pittsburgh and Carnegie Mellon, where I split my time between medical school and building machine learning models for healthcare. Before this, I spent two years at McKinsey & Company advising health organizations, and studied mathematics at Duke.
My research sits at the intersection of machine learning, mathematics, and medicine. I've worked across virology, cancer genomics, mathematical biology, ophthalmology, and infectious disease. The common thread is using quantitative methods to understand complex diseases.
Experience
MD/PhD Candidate
2024 – PresentPitt/CMU Medical Scientist Training Program · Pittsburgh, PA
Pursuing an MD at the University of Pittsburgh School of Medicine and a PhD in Computer Science at Carnegie Mellon University. My research focuses on developing interpretable machine learning models for large-scale health data, spanning cancer genomics, ophthalmology, mathematical biology, and infectious disease. I also created and lead-instruct an Applied ML in Medicine course for medical students and co-founded the Pitt AI in Medicine Student Society.
Senior Business Analyst
2022 – 2024McKinsey & Company · Washington, DC
Advised healthcare and technology organizations on digital, data, and AI transformations. Built and managed a $100M+ data organization for a U.S. government health agency focused on emerging infectious diseases. Led quantitative modeling and stakeholder execution for a $900M+ hospital-provider merger.
B.S. Mathematics
2018 – 2022Duke University · Durham, NC
Phi Beta Kappa. Conducted research across machine learning (Computer Science), mathematical biology (Mathematics), cardiovascular medicine (School of Medicine), and virology (National Cancer Institute). Summer Business Analyst at McKinsey & Company.
Selected Publications
First-author work across machine learning, ophthalmology, and infectious disease.
Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning
Ophthalmology Science2026
(opens in new tab)Predicting Early Onset of Age-Related Macular Degeneration: A Machine Learning Approach
American Journal of Ophthalmology20252 citations
Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis
ML4H20242 citations
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Get in Touch
I read every email. Research collaborations, questions about the MSTP path, or anything at the seam of machine learning and medicine.