Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
Haoran Zhang
Haoran is a fifth year PhD student in EECS at MIT. He is generally interested in building robust machine learning models that maintain their performance and fairness across out-of-distribution environments, as well as applying such models to the healthcare setting. Haoran previously received his M.Sc. at the University of Toronto under the co-supervision of Dr. Marzyeh Ghassemi and Dr. Quaid Morris, and his B.Eng. from McMaster University.
Kumail Alhamoud
Kumail is pursuing his PhD in EECS at MIT. He is supported by the Jameel Clinic Fellowship. His primary focus lies in developing trustworthy and adaptable machine learning models. He is interested in designing ways to evaluate models under distribution shifts arising in real-world healthcare applications. Before MIT, he completed his BS in Electrical and Computer Engineering at Cornell University, and his MS in Computer Science at King Abdullah University of Science and Technology (KAUST), where he conducted computer vision research with Professor Bernard Ghanem. You will catch him riding his road bike around Massachusetts in his free time.