By Justin Varney
Michigan Institute for Data and AI in Society
The Michigan Institute for Data and AI in Society has announced the recipients of its Propelling Original Data Science (PODS) grants.
Sixteen University of Michigan research teams have been selected to share more than $1 million for projects that fuse data science and artificial intelligence to advance core methodologies; promote responsible and ethical AI use; apply cutting-edge technologies to transform healthcare research, improve social interactions, make power grids more efficient, improve data quality; and strengthen the U-M research ecosystem, among other key areas.
“MIDAS is committed to supporting U-M faculty and their cutting-edge research,” said MIDAS Director H.V. Jagadish. “Through the PODS grants, we aim to enable innovative uses of data science and AI, foster new collaborations and help researchers secure major external funding.”
This year’s collaborative partners include Microsoft, the U-M Institute for Healthcare Policy and Innovation (IHPI) and the Michigan Institute for Clinical & Health Research (MICHR).
In 2025, MIDAS will provide more than $412,000 in funding for PODS projects. IHPI will fund half of the Track 3B awards, while MICHR will fully support Track 3A. Track 2 awards are made possible by a gift from Microsoft to MIDAS.
Participating units will also contribute approximately $162,000 in matching funds for various PODS projects.
The 2025 PODS Grants are organized into several focused tracks:
- PODS Track 1: Data science and AI methodology and applications – features projects that use AI tools to understand the evolvability of proteins, apply AI-driven storytelling to help people explore “what if” scenarios and improve data quality and causal inference by addressing data creation errors at a population scale.
- PODS Track 2: Accelerating responsible AI research ecosystems (Microsoft) – provides awards to research addressing the societal and environmental impacts of generative AI, builds AI agents to support victim-survivors of non-consensual intimate media and develops frameworks for understanding reliance on generative AI tools.
- PODS Track 3A: AI innovations in clinical & translational sciences (CTS) (MIDAS + MICHR) – supports projects that use AI to enhance research methods and fosters more robust engagement of patients and participants.
- PODS Track 3B: AI impact and governance for health policy and healthcare (MIDAS + IHPI) – funds projects focused on advancing policy and governance for equitable, effective and fair use of AI in healthcare, with a focus on care delivery, patient trust, regulation, research integrity and audit strategies.
- PODS Track 3C: Data science and AI for health science and healthcare research – focuses on advancing data science and AI methods, addressing major research questions with innovative approaches, promoting ethical and reproducible research and strengthening the U-M research ecosystem.
Since 2016, MIDAS has provided funding to U-M faculty and, as of 2024, has supported 94 “high-risk, high-return” projects with more than $13 million to initiate groundbreaking research and foster strategic, cross-disciplinary collaborations. These seeded projects have since attracted more than $130 million in external funding.
“We received a record number of proposals this year, and the caliber of research was truly exceptional,” said MIDAS Executive Director Jing Liu. “What distinguishes this year’s awards is how boldly these projects rethink the role of AI and data science in solving society’s most urgent and complex problems.”
The awarded proposals reflect this shift, spanning everything from patient-centered healthcare and responsible AI governance to breakthroughs in foundational methods and data quality. Collectively, they represent the next generation of research driven by human insight, ethical AI and interdisciplinary collaboration.
The 2025 awardees and their schools or colleges are:
PODS Track 1: Data Science and AI Methodology and Applications
- Harnessing AI for Advancing Data Collection and Population-Scale Causal Inference
William Axinn (Ford School of Public Policy), David Jurgens (School of Information), and James Wagner (Institute for Social Research) - The As-If Machine (AIM): A Multi-Agent RAG System for Reducing Psychological Distance Through Personalized Narrative Simulations
Ceren Budak (School of Information) and Stephanie Preston (College of Literature, Science and the Arts) - SAGE: A Scalable GeoAI Framework for Zero-Shot Mapping of Lithium Mines
Joshua Newell (School of Environment and Sustainability) and Paramveer Dhillon (School of Information) - ML-Powered Anomaly Detection at the Speed of Light: Algorithms and Applications for Secure Power Grid Operations
Shubhanshu Shekhar (College of Engineering) and Vladimir Dvorkin (College of Engineering) - Harnessing Evolutionary Legacies in Protein Space: Evolvability as a new target for molecular optimization
Luis Zaman (College of Literature, Science and the Arts), Robert Woods (Medical School), and Matthew O’Meara (Medical School)
PODS Track 2: Accelerating Responsible AI Research Ecosystems
- Continued Funding for 2024 PODS Awards: Evaluating Solutions to the Decline of Online Knowledge Communities
Yan Chen (School of Information) and Qiaozhu Mei (School of Information) - Governing AI’s Footprint: A Scalable Human-AI Workflow to Extract Zoning Codes for Data Centers and Renewable Energy Sitting
Xiaofan Liang (Taubman College of Architecture and Urban Planning) and Sarah Mills (Taubman College of Architecture and Urban Planning) - Facilitating Appropriate Reliance on Generative AI (GenAI) Tools by Investigating Reliance Decisions and Norms
Q. Vera Liao (College of Engineering) - AI Systems to Combat Non-Consensual Intimate Media (NCIM)
Sarita Schoenebeck (School of Information) and Eric Gilbert (School of Information) - Continued Funding for 2024 PODS Awards: A Joint Human-AI Framework for Responsible AI
Colleen Seifert (College of Literature, Science and the Arts), Rita Chin (College of Literature, Science and the Arts) and H.V. Jagadish (College of Engineering)
PODS Track 3: AI for Health Policy and Healthcare
PODS Track 3A: AI Innovations in Clinical & Translational Sciences (CTS) (MIDAS + MICHR)
- Developing Best Practices for AI-assisted Mixed Methods Analysis
Timothy Guetterman (Michigan Medicine) and Melissa DeJonckheere (Michigan Medicine)
PODS Track 3B: AI impact and governance for health policy and healthcare (MIDAS + IHPI)
- Implementing AI into Anticoagulation Clinical Decision Support
Geoffrey Barnes (Michigan Medicine), Michael Sjoding (Michigan Medicine) and Michael Lanham (Michigan Medicine) - Reducing Unplanned Hospital Readmissions with Causal Machine Learning
Jenna Wiens (College of Engineering) and Vikas Parekh (Michigan Medicine)
PODS 3C: Data science and AI for health science and healthcare research
- Revolutionizing Disease Diagnostics Through the Integration of Physics-Informed Materials Science Methods with Sequence Models
Sharon Glotzer (College of Engineering) - Predictive Modeling and Feature Learning for Large-Scale Neuroimaging Data
Jian Kang (School of Public Health) and Chandra Sripada (Michigan Medicine, College of Literature, Science and the Arts) - Enhancing Drug Combination Therapies Through Heterophilic Link Prediction with Graph Neural Networks
Danai Koutra (College of Engineering) and Sriram Chandrasekaran (College of Engineering, Michigan Medicine)