C3.AI DTI Awards $5.4M for AI Research to Mitigate COVID-19

June 23, 2020

June 23, 2020 — 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,” says William “Bill” Gropp, Director of NCSA.

“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.”

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 Berkeley
  • Improving Fairness & Equity in COVID-19 Policy Applications of Machine Learning — Rayid Ghani, Carnegie Mellon University
  • Modeling the Impact of Social Determinants of Health on COVID-19 Transmission and Mortality to Understand Health Inequities — Anna Hotton, University of Chicago
  • Bringing Social Distancing to Light: Crowd Management Using AI and Interactive Floor Projection — Stefana Parascho, Princeton University
  • Using Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission and COVID-19 Clinical Presentation in Mexico — Stefano Bertozzi, UC Berkeley
  • Detection and Containment of Emerging Diseases Using AI Techniques — Alberto Sangiovanni-Vincentelli, UC Berkeley
  • COVID-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 University
  • Reinforcement Learning to Safeguard Schools and Universities Against the COVID-19 Outbreak — Munther Dahleh, MIT
  • Pandemic-Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions — Saurabh Amin, MIT
  • Toward Analytics-Based Clinical and Policy Decision Support to Respond to the COVID-19 Pandemic — Dimitris Bertsimas, MIT
  • Dynamic Resource Management in Response to Pandemics — Subhonmesh Bose, University of Illinois at Urbana-Champaign
  • Algorithms and Software Tools for Testing and Control of COVID-19 — Prashant Mehta, University of Illinois at Urbana-Champaign
  • Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic — Asu Ozdaglar, MIT
  • Spatial 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 University
  • Machine Learning–Based Vaccine Design and HLA-Based Risk Prediction for Viral Infections — David Gifford, MIT
  • Scoring Drugs: Small Molecule Drug Discovery for COVID-19 Using Physics-Inspired Machine Learning — Teresa Head-Gordon, UC Berkeley
  • Data-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 Chicago
  • Mining 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-Champaign
  • AI-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-Champaign
  • Machine 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

Source: National Center for Supercomputing Applications

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