← Meet the rest of the cohort 
Haoran Zhang
Fairness and robustness in clinical machine learning
PhD Student
MIT
Your week at a glance
- Small group — you facilitate Group 5 · Perseus on Day 2 (Data).
- Poster — Day 7 (Sun, Jun 28) · Session B (10:30–11:30 AM).
Haoran Zhang is a PhD candidate in EECS at MIT, advised by Prof. Marzyeh Ghassemi. His research focuses on enabling the reliable and trustworthy deployment of machine learning models in healthcare, by tackling issues such as algorithmic fairness, robust performance under distribution shift, and learning under label noise. His work has appeared in venues including Nature Medicine, The Lancet Digital Health, NeurIPS, ICML, and ICLR, and has been featured by media outlets such as Wired, VICE, and The Boston Globe. Haoran served as General Chair of the Machine Learning for Health (ML4H) Symposium in 2024 and 2025.
Awards & honors
Recognition
- Best Paper Award, ICML 2023 Workshop on Interpretable Machine Learning in Healthcare (Oral Spotlight) (2023)
- 2nd Place in AI Safety & Alignment, Berkeley RDI AgentX Competition (2025)
- NeurIPS 2025 Spotlight, 'Aggregation Hides OOD Generalization Failures from Spurious Correlations' (2025)
- ICLR 2024 Spotlight, 'Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation' (2024)
- Oral Spotlight, NeurIPS 2023 Medical Imaging meets NeurIPS, 'On Mitigating Shortcut Learning for Fair Chest X-ray Classification' (2023)