C3.ai DTI Awards $5.4 Million for AI Research to Mitigate COVID-19 June 26, 2020 Funding Artificial IntelligenceData AnalyticsHealth SciencesInstitutional PartnershipsModeling and Simulation Share this page: Twitter Facebook LinkedIn Email By Sophie Anh Bui The National Center for Supercomputing Applications (NCSA) and the University of Illinois at Urbana-Champaign, alongside fellow consortium members of the C3.ai Digital Transformation Institute (C3.ai DTI), awards $5.4 million to accelerate artificial intelligence (AI) research to mitigate the COVID-19 pandemic. The announcement was made by C3.ai DTI this morning. Twenty-six projects will receive funding for research that strives toward addressing COVID-19 across multiple disciplines including medicine, urban planning, public policy, and computer science, and its impact on racial, economic, and healthcare disparities. In addition, research teams will have access to advanced computing and data resources provided by C3.ai DTI consortium members and partners. NCSA is looking forward to working with UC Berkeley, Microsoft, NERSC, and the other consortium partners to support these exciting projects, taking advantage of our long history of using computing and data to accelerate the progress of research.William “Bill” Gropp, NCSA Director “The C3.ai Digital Transformation Institute, with its vision of cross-institutional and multi-disciplinary collaboration, represents an exciting model to help accelerate innovation in this important new field of study,” says Robert J. Jones, Chancellor of the University of Illinois at Urbana-Champaign. “At this time of a global health crisis, the Institute’s initial research focus will be on applying AI to mitigate the COVID-19 pandemic and to learn from it how to protect the world from future pandemics. C3.ai DTI is an important addition to the world’s fight against this disease and a powerful new resource in developing solutions to all societal challenges.” Read the full release, and find more information about the awards below: AI FOR EPIDEMIOLOGY, SOCIAL GOOD AND CLINICAL USE Housing Precarity, Eviction, and Inequality in the Wake of COVID-19 — Karen Chapple, UC BerkeleyImproving Fairness & Equity in COVID-19 Policy Applications of Machine Learning — Rayid Ghani, Carnegie Mellon UniversityModeling the Impact of Social Determinants of Health on COVID-19 Transmission and Mortality to Understand Health Inequities — Anna Hotton, University of ChicagoBringing Social Distancing to Light: Crowd Management Using AI and Interactive Floor Projection — Stefana Parascho, Princeton UniversityUsing Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission and COVID-19 Clinical Presentation in Mexico — Stefano Bertozzi, UC BerkeleyDetection and Containment of Emerging Diseases Using AI Techniques — Alberto Sangiovanni-Vincentelli, UC BerkeleyCOVID-19 Medical Best Practice Guidance System — Lui Sha, University of Illinois at Urbana-Champaign MATHEMATICAL MODELING, CONTROL, AND LOGISTICS Modeling and Control of COVID-19 Propagation for Assessing and Optimizing Intervention Policies — Vincent Poor, Princeton UniversityReinforcement Learning to Safeguard Schools and Universities Against the COVID-19 Outbreak — Munther Dahleh, MITPandemic-Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions — Saurabh Amin, MITToward Analytics-Based Clinical and Policy Decision Support to Respond to the COVID-19 Pandemic — Dimitris Bertsimas, MITDynamic Resource Management in Response to Pandemics — Subhonmesh Bose, University of Illinois at Urbana-ChampaignAlgorithms and Software Tools for Testing and Control of COVID-19 — Prashant Mehta, University of Illinois at Urbana-ChampaignTargeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic — Asu Ozdaglar, MITSpatial Modeling of COVID-19: Optimizing PDE and Metapopulation Models for Prediction and Spread Mitigation — Zoi Rapti, University of Illinois at Urbana-Champaign VACCINE AND DRUG DISCOVERY Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data — Ziv Bar-Joseph, Carnegie Mellon UniversityMachine Learning–Based Vaccine Design and HLA-Based Risk Prediction for Viral Infections — David Gifford, MITScoring Drugs: Small Molecule Drug Discovery for COVID-19 Using Physics-Inspired Machine Learning — Teresa Head-Gordon, UC BerkeleyData-Driven, High-Dimensional Design for Trustworthy Drug Discovery — Jennifer Listgarten, UC Berkeley COMPUTATIONAL BIOLOGY Medical Imaging Domain-Expertise Machine Learning for Interrogation of COVID-19 — Maryellen Giger, University of ChicagoMining Diagnostics Sequences for SARS-CoV-2 Using Variation-Aware, Graph-Based Machine Learning Approaches Applied to SARS-CoV-1, SARS-CoV-2, and MERS Datasets — Nancy Amato, University of Illinois at Urbana-ChampaignAI-Enabled Deep Mutational Scanning of Interaction Between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor — Diwakar Shukla, University of Illinois at Urbana-Champaign IMAGING/COMPUTER VISION Adding Audio-Visual Cues to Signs and Symptoms for Triaging Suspected or Diagnosed COVID-19 Patients — Narendra Ahuja, University of Illinois at Urbana-ChampaignMachine Learning Support for Emergency Triage of Pulmonary Collapse in COVID-19 — Sendhil Mullainathan, University of Chicago INTELLIGENT DATABASES AND SEARCH COVIDScholar: An NLP Hub for COVID-19 Research Literature — Gerbrand Ceder, UC Berkeley DISTRIBUTED COMPUTING Secure Federated Learning for Clinical Informatics with Applications to the COVID-19 Pandemic — Oluwasanmi Koyejo, University of Illinois at Urbana-Champaign ABOUT NCSA The National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students and collaborators from around the globe use these resources to address research challenges for the benefit of science and society. NCSA has been advancing many of the world’s industry giants for over 35 years by bringing industry, researchers and students together to solve grand challenges at rapid speed and scale.