The Michigan Institute for Data and AI in Society has announced the recipients of its 2024 Propelling Original Data Science grants and the Center for Data-Driven Drug Development and Treatment Assessment annual awards.
Nineteen University of Michigan research teams have been selected to share $909,716 for projects that fuse data science and artificial intelligence to spearhead transformations in health care, environmental sustainability and several other key areas.
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“MIDAS is committed to offering robust support that mirrors the complex nature of modern research,” said MIDAS Director H.V. Jagadish. “Through the PODS grants, we aim to harness the potential of Data Science and AI in developing state-of-the-art research methods, fostering the ethical application of AI, and improve health policy and practices with AI.”
Jagadish also is the Edgar F. Codd Distinguished University Professor of Electrical Engineering and Computer Science, and the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science and professor of electrical engineering and computer science in the College of Engineering.
The PODS grants are organized into several focused tracks:
- PODS Track 1: Data Science and AI Methodology and Applications, which includes projects developing AI tools for enhancing U-M soccer performance, optimizing energy distribution and creating models for organic molecule design.
- PODS Track 2: Accelerating Responsible AI Research Ecosystems, which provides awards to further innovation in educational models’ fairness and bias methods, revive declining online knowledge communities and establish frameworks for responsible AI practices.
- PODS Track 3: AI for Health Policy and Healthcare: Impact & Governance, a unique collaboration with U-M’s Institute for Healthcare Policy and Innovation that funds research on trust and governance in clinical AI applications.
Since the initiation of these grants in 2016, more than 75 teams have received more than $12 million from MIDAS to initiate groundbreaking research and foster strategic collaborations. The investments have led to significant achievements, with projects stemming from these grants receiving more than $125 million in additional external funding. This outcome showcases the transformative impact of interdisciplinary AI research.
In this round, MIDAS will provide $710,511 for PODS projects, and $199,205 for DATA awards. The IHPI will provide half of the funding for Track 3 awards; and Track 2 awards are provided by a gift from Microsoft to MIDAS. Additionally, units will provide $130,476 in matching funds for various PODS projects.
In 2024, collaborations will include industry and research leaders such as Microsoft and the IHPI, enhancing the PODS program’s scope to include focused support for responsible AI endeavors and AI’s application in health policy activities.
The DATA awards, supported by the NSF’s Industry-University Cooperative Research Center program, honor teams applying data science and AI in redefining clinical treatment approaches, demonstrating a comprehensive method to propel scientific innovation that addresses societal concerns.
“This year’s awarded projects showcase the power of AI in transcending traditional research and industry limits, advancing our comprehension of complex systems such as global carbon cycles, and refining the design of molecules for energy storage,” said MIDAS executive director Jing Liu, assistant research scientist in the Institute for Data and AI in Society.
“Moreover, the selection highlights the essential ethical application of AI in health care, policymaking and education.”
The 2024 awardees and their schools or colleges are:
PODS Track 1: Data science and AI methodology and applications
- Albert Berahas and Raed Al Kontar (College of Engineering): WinAI: Propelling UM Soccer with Data-Driven AI
- Srijita Das and Van Hai Bui (College of Engineering and Computer Science, UM-Dearborn): Human-in-the-loop multi-agent sequential decision-making based Optimal Operation of Power Distribution System
- David Kwabi and Bryan Goldsmith (College of Engineering), and Yixin Wang (LSA): Extrapolating with Generative Models for Design of Organic Molecules as Energy Carriers
- Vitaliy Popov, Michael Cole and James Cooke (Michigan Medicine), and Mohamed Abouelenien (College of Engineering and Computer Science, UM-Dearborn): Multimodal Modeling of Cognitive Load at Individual and Team Levels in Acute Care Teams using VR Simulations
- Peter Reich (School for Environment and Sustainability) and Mohammed Ombadi (College of Engineering): Combining ecological first principles and AI to better upscale and predict global carbon, nutrient and water cycles on a changing planet
- Katie Skinner (College of Engineering) and Jacob Allgeier (LSA): Machine Learning for Automated Fish Detection and Characterization
- Sabina Tomkins and Grant Schoenebeck (School of Information), Derek Van Berkel (SEAS), and Ariel Hasell and John Ryan (LSA): Distributing Expert Attention in Complementary Systems
- Angela Violi (College of Engineering): AI-driven Accelerated Optimization for the Design of Sustainable Aviation Fuels
- Jon Zelner and Fan Bu (School of Public Health): Neural Posterior Estimation (NPE) approaches for fitting high-dimensional stochastic epidemic models to real-world spatiotemporal disease data
PODS Track 2: Accelerating responsible AI research ecosystems
- Christopher Brooks and Libby Hemphill (School of Information), and Allyson Flaster (Institute for Social Research): Innovating, Applying, and Educating on Fairness and Bias Methods for Educational Predictive Models
- Yan Chen and Qiaozhu Mei (School of Information): Evaluating GenAI and Team-based Solutions to Reverse the Decline of Online Knowledge Communities
- Rita Chin (LSA) and H.V. Jagadish (College of Engineering): A Joint Human-AI Framework for Responsible AI
- Shobita Parthasarathy and Molly Kleinman (Gerald R. Ford School of Public Policy), and Ben Green (School of Information): Advancing Responsible AI by Rethinking the Roles of Marginalized Communities in the Innovation Lifecycle: Developing the UBEC Approach
PODS Track 3: AI for Health Policy and Healthcare: Impact & Governance
- Kayte Spector-Bagdady (Michigan Medicine) and W. Nicholson Price (Law School): Trust, Governance, and Humans in the Loop in Clinical AI
DATA awards
- Cristian Minoccheri (Michigan Medicine): CASM-informed Reinforcement Learning (CASM-RL) to Identify Optimal Treatment Strategies for Sepsis
- Timothy Cernak (College of Pharmacy): Combatting Rapidly Mutating Viral Targets Using Thompson Sampling
- Matthew O’Meara (Michigan Medicine): Docking to Novel pocKets (DoNK): A Dense Synthetic Receptor-Ligand Binding Dataset
- Peter Tessier (College of Pharmacy) and Kayvan Najarian (Michigan Medicine): Generative Artificial Intelligence for Design and Optimization of New Therapeutic Antibodies
- Denise Kirschner and Maral Budak (Michigan Medicine): Model-informed Drug Development for Cancer Using Agent-based Multivariate Modeling
