Research Institute Awards $5M to Combat COVID-19 Using Cloud, AI Digital Transformation Institute ( DTI) announced the recipients of $5.4 million in research grants to combat the COVID-19 pandemic using cloud and AI. DTI, research consortium jointly managed by the University of Illinois at Urbana-Champaign and the University of California, Berkeley, invited researchers to “take on the challenge of abating COVID-19 and advancing AI-based science and technologies for mitigating future pandemics” earlier this spring.

In a statement, DTI noted that the projects were peer-reviewed based on “scientific merit, prior accomplishments of the principal investigator and co-principal investigators, the use of AI, machine learning, data analytics, and cloud computing in the research project, and the suitability for testing the methods at scale.” After its review process, DTI awarded $5.4 million to research proposals that address COVID-19 across the disciplines of medicine, urban planning, public policy, and computer science. The institute noted that several projects will study COVID-19’s impact on racial, economic, and healthcare disparities. In addition to the research funding, recipients also gain access to DTI’s cloud and data resources to aid in their research.

The awards span several categories, including AI for epidemiology, social good, and clinical use; mathematical modeling, control, and logistics; vaccine and drug discovery; computational biology; imaging/computer vision; intelligent databases and search; and distributed computing.

In the end, DTI awarded 26 research proposals with funding. Here are a few of the chosen projects:

AI for Epidemiology, Social Good, and Clinical Use: 

  • Housing Precarity, Eviction, and Inequality in the Wake of COVID-19 (University of California, Berkeley)
  • Improving Fairness & Equity in COVID-19 Policy Applications of Machine Learning (Carnegie Mellon University)
  • Detection and Containment of Emerging Diseases Using AI Techniques (University of California, Berkeley)
  • COVID-19 Medical Best Practice Guidance System (University of Illinois at Urbana-Champaign)

Mathematical Modeling, Control, and Logistics: 

  • Toward Analytics-Based Clinical and Policy Decision Support to Respond to the COVID-19 Pandemic (MIT)
  • Algorithms and Software Tools for Testing and Control of COVID-19 (University of Illinois at Urbana-Champaign)

Vaccine and Drug Discovery: 

  • Machine Learning-Based Vaccine Design and HLA-Based Risk Prediction for Viral Infections (MIT)
  • Data-Driven, High-Dimensional Design for Trustworthy Drug Discovery (University of California, Berkeley)

Computational Biology: 

  • 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 (University of Illinois at Urbana-Champaign)
  • AI-Enabled Deep Mutational Scanning of Interaction Between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor (University of Illinois at Urbana-Champaign)

Imaging/Computer Vision: 

  • Medical Imaging Domain-Expertise Machine Learning for Interrogation of COVID-19 (University of Chicago)
  • Machine Learning Support for Emergency Triage of Pulmonary Collapse in COVID-19 (University of Chicago)
Kate Polit
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