C3.ai’s Digital Transformation Institute issues awards for data-driven COVID-19 research

C3.ai’s Digital Transformation Institute awarded $5.4 million to 26 researchers to apply artificial intelligence models to COVID-19 mitigation.

The research projects looked at COVID-19 as well as its impact on housing precarity, eviction and inequality as well as the role of social determinants on health and using data science to detect and understand transmission.

C3.ai’s Digital Transformation Institute (DTI) with partners University of Illinois at Urbana-Champaign (UIUC) and the University of California, Berkeley and Microsoft requested papers in March. C3.ai DTI awards include access to the C3 AI Suite, Microsoft Azure compute and storage and data sources such as the C3.ai COVID-19 Data Lake.

“DTI’s mission is to attract the world’s leading scientists to join in a coordinated and innovative effort to advance digital transformation writ large,” said C3.ai CEO Tom Siebel.

By focusing on COVID-19, Siebel said scientists can provide real science and research to make better decisions. “Many decisions that have been made around the world in US have been well intended, but not informed by data,” said Siebel, who added contributed data sets, research, algorithms and software will be available to the public.

Projects were peer reviewed on the basis of scientific merit, prior accomplishments of the investigator, the use of AI, machine learning, data analytics, and cloud computing and methods that could scale.

Here’s a look at some of the research projects awarded.

  • Eviction, and Inequality in the Wake of COVID-19 (Karen Chapple, UCBerkeley)
  • 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)
  • Modeling and Control of COVID-19 Propagation for Assessing and Optimizing Intervention Policies (Vincent Poor, Princeton University)
  • Toward Analytics-Based Clinical and Policy Decision Support to Respond to the COVID-19 Pandemic (Dimitris Bertsimas, MIT)
  • Machine Learning–Based Vaccine Design and HLA-Based Risk Prediction for Viral Infections (David Gifford, MIT)
  • Machine Learning Support for Emergency Triage of Pulmonary Collapse in COVID-19 (Sendhil Mullainathan, University of Chicago)