Bryan Wilder is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University. His research focuses on AI for equitable, data-driven decision making in high-stakes social settings, integrating methods from machine learning, optimization, and social networks. Much of his work is motivated by public health applications, including HIV prevention, maternal and child health, and COVID-19. His dissertation was recognized with the 2021 IFAAMAS Victor Lesser Distinguished Dissertation Award, and his work has been a finalist for best paper awards at ICML, AAMAS, and the INFORMS Doing Good with Good OR competition.

AI2050 Project

Through this fellowship, Bryan will focus on machine learning to support equity in public health. The project will use real-time data sources to track health disparities and ameliorate biases in risk prediction models used in healthcare.

Project Artifacts

B. Wilder, P. Welle. Learning treatment effects while treating those in need. arXiv. 2024.

K. Ren, Y. Byun, B. Wilder. Decision-Focused Evaluation of Worst-Case Distribution Shift. arXiv. 2024.

AI2050 Community Perspective — Bryan Wilder (2024)

Y. Byun, D. Sam, M. Oberst, Z. C. Lipton, and B. Wilder. Auditing fairness under unobserved confounding. arXiv. 2024.

S. Cortes-Gomez, M. Dulce, and B. Wilder. Inference under constrained distribution shifts. arXiv. 2023.

B. Chugg, S. Cortes-Gomez, B. Wilder, and A. Ramdas. Auditing fairness by betting. arXiv. 2023.