C3.ai DTI, jointly managed by the University of Illinois at Urbana-Champaign and the University of California, Berkeley, and in partnership with Microsoft Corp., invited researchers in March to take on the challenge of abating COVID-19 and advancing AI-based science and technologies for mitigating future pandemics.
After a rigorous peer review process, C3.ai DTI selected 26 research proposals that address COVID-19 across the disciplines of medicine, urban planning, public policy, and computer science, several of which focus on the study of COVID-19’s impact on racial, economic, and healthcare disparities. A total of $5.4 million in cash will be awarded to the following research projects. In addition, research teams will gain access to the C3 AI™ Suite, Microsoft Azure computing and storage, as well as data resources such as the C3.ai™COVID-19 Data Lake in support of their research.
C3.ai DTI Awards:
- 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 (H. 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 (Asuman 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 (Sanmi Koyejo, University of Illinois at Urbana-Champaign)
“My colleagues and I are grateful for the funding and access to computing resources provided by C3.ai DTI that will allow us to study the impact of social determinants of health on COVID-19 transmission and mortality and to build up future capacity to evaluate and intervene to reduce health inequities,” said Dr. Anna Hotton, epidemiologist and Research Assistant Professor, Department of Medicine at the University of Chicago.
“The enormous potential of AI algorithms to help us derive actionable information and make well-informed decisions is truly exciting,” said Dr. Saurabh Amin, Robert N. Noyce Associate Professor, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. “We have a unique opportunity to design scalable decision-making algorithms to learn how to reopen transportation systems in a safe way and to bridge the gap between desirable policy interventions and realistic operational strategies.”
“The enthusiastic response among scientists and researchers coupled with the diverse, high-quality and compelling proposals we’ve received suggests that we have the potential to alter the course of this global pandemic,” said Thomas M. Siebel, CEO of C3.ai. “In the face of this crisis, the Institute is proud to bring together the best and brightest minds and provide direction and leadership to support objective analysis and AI-based, data-driven science to mitigate COVID-19.”
“It is encouraging to see the large number of high-quality proposals, spanning promising areas of research, on directions of effort needed now more than ever,” said Eric Horvitz, Chief Scientific Officer at Microsoft. “I’m looking forward to following the research as it progresses and hopeful that the funded teams will make significant contributions in the fight against COVID-19.”
C3.ai DTI selected research proposals that will inspire cooperative research and advance machine learning as well as other AI subdisciplines.
Projects were peer reviewed on the basis of 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.
In March of this year, C3.ai, Microsoft, the University of Illinois at Urbana-Champaign, the University of California, Berkeley, Carnegie Mellon University, Lawrence Berkeley National Laboratory, Massachusetts Institute of Technology, National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign, Princeton University, and the University of Chicago established C3.ai DTI, a research consortium dedicated to accelerating the application of artificial intelligence to speed the pace of digital transformation in business, government, and society. Stanford University is the consortium’s newest member.
About C3.ai Digital Transformation Institute
The C3.ai Digital Transformation Institute (@C3Dti) is a research consortium dedicated to accelerating the benefits of artificial intelligence for business, government, and society. The Institute engages the world’s leading scientists to conduct research and train practitioners in the new science of digital transformation, which operates at the intersection of artificial intelligence, machine learning, cloud computing, internet of things, big data analytics, organizational behavior, public policy, and ethics. Consortium members include: C3.ai, Microsoft, and nine leading universities and national laboratories: the University of Illinois at Urbana-Champaign, the University of California, Berkeley, Carnegie Mellon University, Lawrence Berkeley National Laboratory, Massachusetts Institute of Technology, National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign, Princeton University, Stanford University, and the University of Chicago.