University of Michigan · Computer Science & Engineering

Realizing AI for

We develop use-inspired AI, building new techniques in multi-agent systems and machine learning for domains such as conservation and public health.

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Research areas

Public Health Trustworthy information, interactive agents, and decision support for maternal and reproductive health, built together with clinicians and patients. See work → Conservation Machine learning for wildlife monitoring and environmental health, from interpretable re-identification to geospatial contamination mapping. See work → Physics-Informed Machine Learning Machine learning methods that incorporate physical structure, mechanistic knowledge, and scientific constraints for reliable modeling of complex systems. See work →

Selected work

NeurIPS 2026

Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik, Elizabeth Bondi-Kelly

KDD 2026

RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media

Vaibhav Balloli, Laura Peyton Ellis, Vishala Mishra, Alice M Chi, Alex Peahl, Elizabeth Bondi-Kelly

FAccT 2026

"Where is this coming from?" Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking

Vaibhav Balloli, Julia Erickson, Xinyi Li, Erin van Liemt, Alex Peahl, Elizabeth Bondi-Kelly

All 13 publications →