About

I am a third-year PhD student at MIT CSAIL, where I am advised by Dr. Marzyeh Ghassemi. My research focuses on methods to construct fair and robust machine learning models which maintain their performance under real-world distribution shifts. I am also interested in the application of such models in the healthcare domain. I previously completed my MSc at the University of Toronto under the co-supervision of Dr. Marzyeh Ghassemi and Dr. Quaid Morris.

Publications

Preprints

• Continuous Time Evidential Distributions for Irregular Time Series
  Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini
  [Paper] [Code]

2023

• "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
  Haoran Zhang*, Harvineet Singh*, Marzyeh Ghassemi, Shalmali Joshi
  International Conference on Machine Learning (ICML) 2023
  [Paper] [Code]

• Change is Hard: A Closer Look at Subpopulation Shift
  Yuzhe Yang*, Haoran Zhang*, Dina Katabi, Marzyeh Ghassemi
  International Conference on Machine Learning (ICML) 2023
  [Paper] [Code]

• Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study
  Haoran Zhang, Thomas Hartvigsen, Marzyeh Ghassemi
  MIT Case Studies in Social and Ethical Responsibilities of Computing (SERC)
  [Paper]

2022

• PAN-cODE: COVID-19 Forecasting Using Conditional Latent ODEs
  Ruian Shi, Haoran Zhang, Quaid Morris
  Journal of the American Medical Informatics Association (JAMIA)
  [Paper] [Code]

• The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
  Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi
  ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2022
  [Paper] [Code] [News]

• Improving the Fairness of Chest X-ray Classifiers
  Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Robert Pfohl, Marzyeh Ghassemi
  Conference on Health, Inference, and Learning (CHIL) 2022
  [Paper] [Code]

• Reading Race: AI Recognises Patient's Racial Identity In Medical Images
  Imon Banerjee, Ananth Reddy Bhimireddy, [...], Zachary Zaiman, Haoran Zhang, Judy W Gichoya
  The Lancet Digital Health
  [Paper] [Code] [News]

• A Comparison of Approaches to Improve Worst-Case Predictive Model Performance over Patient Subpopulations
  Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah
  Scientific Reports
  [Paper] [Code]

2021

• Underdiagnosis Bias of Artficial Intelligence Algorithms Applied to Chest Radiographs in Under-served Patient Populations
  Laleh Seyyed-Kalantari, Haoran Zhang, Matthew McDermott, Irene Y Chen, Marzyeh Ghassemi
  Nature Medicine
  [Paper]

• Learning Optimal Predictive Checklists
  Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi
  Conference on Neural Information Processing Systems (NeurIPS) 2021
  [Paper] [Code] [Poster]

• Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
  Sindhu C.M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi
  Conference on Information and Knowledge Management (CIKM) 2021
  [Paper] [Code] [Poster]

• An Empirical Framework for Domain Generalization in Clinical Settings
  Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi
  ACM Conference on Health, Inference, and Learning (CHIL) 2021
  [Paper] [Code] [Poster]

2020

• An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
  Taylor Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi
  Machine Learning for Health Workshop at NeurIPS 2020 (Proceedings Track)
  [Paper] [Code] [Poster]

• Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization
  Amy Lu, Haoran Zhang, Marzyeh Ghassemi, Alan Moses
  bioRxiv Preprint
  [Paper] [Code]

• Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
  Haoran Zhang*, Amy Lu*, Mohamed Abdalla, Matthew McDermott, Marzyeh Ghassemi
  ACM Conference on Health, Inference, and Learning (CHIL) 2020
  [Paper] [Code] [Poster]

• Identifying Transitional High Cost Users from Unstructured Patient Profiles Written by Primary Care Physicians
  Haoran Zhang, Elisa Candido, Andrew S. Wilton, Raquel Duchen, Liisa Jaakkimainen, Walter Wodchis, Quaid Morris
  Pacific Symposium on Biocomputing (PSB) 2020
  [Paper] [Poster]