معرفی
Bryan Wilder is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University (CMU). He directs the Lab for AI and Social Impact (LASI), focusing on AI methods for equitable decision-making in public health, medicine, and human services. Previously, he was a postdoctoral Schmidt Science Fellow at Harvard School of Public Health and earned his PhD in Computer Science from Harvard under Milind Tambe. His work spans algorithmic fairness, optimization, and causal inference, with funding from NSF, NIH, CDC, and Schmidt Futures.
Education: PhD in Computer Science (Harvard, 2021), MSc from USC (2019), BS from UCF (2015).
Key Roles: Chair of EAAMO Board, Co-PI of CDC Innovation Center at CMU, Guest Lecturer at multiple institutions.
Research Interests: Integrating ML, optimization, and causal inference for high-stakes social systems. Current projects include maternal/child health, infectious disease modeling, and fair resource allocation. His lab emphasizes real-world impact through partnerships with public health organizations.
Articles Trends: Recent work addresses federated epidemic surveillance, reinforcement learning for coupled systems, and auditing fairness in AI. Earlier papers focus on HIV prevention interventions, risk-sensitive optimization, and cultural evolution modeling.
Awards: Includes the Schmidt AI2050 Fellowship, IFAAMAS Dissertation Award, and Siebel Scholarship. Over 30+ peer-reviewed papers in top venues (NeurIPS, ICML, AAAI).
Advising & Grants: Supervises PhD students at CMU and USC. Leads projects funded by NSF, NIH, CDC, and Schmidt Futures. Collaborates with global health institutions.
Labs/Teams: LASI lab develops AI tools for equitable decision-making, with applied work in maternal health, HIV prevention, and pandemic response.
