Zachary Chase Lipton is an assistant professor of Operations Research and Machine Learning at Carnegie Mellon University. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking with messy data. He is the founder of the Approximately Correct blog and an author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks.
- Summer Cluster: Interpretable Machine Learning, Summer 2022. Visiting Scientist, Program Organizer and Workshop Organizer.