Propelling Original Data Science (PODS) Grants

MIDAS offers the Propelling Original Data Science (PODS) Grants annually, starting from 2019.  This funding is for projects led by U-M faculty.

REVIEW CRITERIA

• An outcome that directly contributes to fighting the COVID-19 pandemic or its effects on society (e.g.,  data-driven or AI-enabled tools, treatments strategies and recommendations);
• Project feasibility;
• Strength of the research team;
• Strength of the data science component;
• Intellectual merit and contribution to research in the long run.

2020 (special round)

In May, 2020, 7 interdisciplinary teams, chosen from 49 submissions, received funding support for COVID-19 research.  With data science methods at the core, these teams combat the pandemic in varied and creative ways.  Each team received funding of up to $30,000, and the projects will finish by the end of 2020.

The awarded PODS grants and the (co) Principal Investigators are:

• Improved real-time surveillance of COVID-19 patients’ electronic health records using transfer learning and ordinal regression, Andrew Admon (Internal Medicine), Christopher Gillies (Emergency Medicine)
• Markovian And Non-Markovian (Discrete) Spatio-Temporal Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic, Moulinath Banerjee and Ya’acov Ritov (Statistics)
• Estimating county-level age group contact rates from time use data, Jacob Fisher and Yajuan Si (Institute for Social Research)
• From data to knowledge: Applying the principles of data science to understand preventable inequities in care and support among COVID-19 cases in Michigan, Nancy Fleischer (Epidemiology)
• Handling Selective and Imperfect Testing in Design and Inference of COVID-19 Studies, Bhramar Mukherjee (Biostatistics)
• Students’ mobility patterns on campus and the implications for the recovery of campus activities post-pandemic, Quan Nguyen, Christopher Brooks, Daniel Romero (School of Information)
• High-resolution spatial analysis to inform the response to COVID-19 in Michigan, Jon Zelner (Epidemiology)

2019

15 interdisciplinary teams, chosen from 65 proposals, received funding support for projects with data science as the common thread.  Researchers on these projects are from 9 schools and colleges across the Ann Arbor and Dearborn campuses. For this set of projects, MIDAS provided a total of $860K of funding, and cost sharing from U-M departments and faculty amounted to an additional $220K.  This funding mechanism strongly encourages pioneering work based on innovative concepts that promises high reward, major impact, promotion of public interest, and potential for major expansion; in other words,“disruptive” instead of incremental research.

The 2019 PODS grants and the (co) Principal Investigators are:

• CHANGES: Collections, Heterogeneous data, and Next Generation Ecological Studies, Karen Alofs (School for Environment and Sustainability), Andrea Thomer (School of Information), Hernan Lopez-Fernandez (Ecology and Evolutionary Biology and Museum of Zoology).
• Probabilistic Methods to Infer Structure and Dynamics of Illicit Wildlife Trade Networks, Neil Carter (School for Environment and Sustainability), Abigail Jacobs (School of Information and Complex Systems, College of Literature, Science, and the Arts).
• A Data-Driven Framework for Microstructure Optimization of Additively Manufactured Piezoelectric Composites, Lei Chen (Mechanical Engineering, U-M Dearborn), Zhen Hu (Industrial and Manufacturing Engineering, U-M Dearborn).
• Fusing Physics and Deep Learning for Solar Dynamics Forecasting, David Fouhey (Electrical Engineering and Computer Science), Ward Manchester (Climate and Space Sciences and Engineering).
• Probabilistic Modeling of Missing Data to Improve Predictions Using Metabolomics Data, Christopher E. Gillies (Emergency Medicine), Kevin Ward (Emergency Medicine and Biomedical Engineering), Kathleen Stringer (Pharmacy), Xudong Fan (Biomedical Engineering).
• Decoding the Environment of Most Energetic Sources in the Universe, Oleg Y. Gnedin (Astronomy), Xun Huan (Mechanical Engineering).
• Data Science for Quantum Simulation, Emanuel Gull (Physics and Chemistry), Dominika Zgid (Chemistry).
• Towards a Framework for the Characterization of Cellular & Spatial Relationships in Development and Disease, Sue Hammoud (Human Genetics, Urology),Arvind Rao (Computational Medicine and Bioinformatics, Radiation Oncology, Biomedical Engineering).
• Database Learning: A Query Engine That Becomes Smarter Over Time, Barzan Mozafari (Computer Science and Engineering), Reza Soroushmehr (Computational Medicine and Bioinformatics).
• Optimal Design of Data Assimilation for the Prediction of Hydrological Extremes, Ashley Payne (Climate and Space Sciences and Engineering), Yulin Pan (Naval Architecture and Marine Engineering).
• DevEEG: A Robust Repository for Developmental Electroencephalogram Data, Amy Pienta (Inter-university Consortium for Political and Social Research), William Gehring (Psychology).
• Achieving ML Robustness by Leveraging Physics-based Constraints, Atul Prakash (Computer Science and Engineering), Huei Peng (Mechanical Engineering).
• Large Scale Interventions for Reducing Threats to Safety and Trustworthiness on Social Media, Sarita Schoenebeck (School of Information), Eric Gilbert (School of Information), Jenny Radesky (Pediatrics).
• Regularized Regression and Poststratification: Blending Data Science and Survey Methodology to Increase the Reproducibility of Population Genetics Research, Yajuan Si (Institute for Social Research–Statistics), Colter Mitchell (Institute for Social Research–Sociology).
• Incorporation of Multilevel Ontologies of Adverse Events and Vaccines for Vaccine Safety Surveillance, Lili Zhao (Biostatistics), Gary Freed (Pediatrics).