Three newly funded projects will enable researchers to model the spread of COVID-19 for intervention policies, model homelessness caused by the shutdown and target aid for at-risk communities, and develop machine intelligence methods to aid in the interrogation of medical images from COVID-19 patients.
Redwood City, CA; Redmond, WA; Urbana-Champaign, IL; Berkeley, CA, April 28, 2020 – The C3.ai Digital Transformation Institute, a new consortium of leading research universities and companies dedicated to accelerating the socioeconomic benefits of artificial intelligence, today announced the winners of its inaugural round of seed grant awards.
Three research teams, led by interdisciplinary experts from C3.ai Digital Transformation Institute member universities, will be awarded approximately $1 million in total to develop techniques to mitigate COVID-19 and future pandemics for time-sensitive projects, as part of the Institute’s first call for research proposals with an application deadline of April 15. There are a large number of other excellent submissions still under consideration; the full announcement of awardees for COVID-19 mitigation research will take place by June 1. The first time-critical funded research projects and teams are:
“Modeling and Control of COVID-19 Propagation for Assessing and Optimizing Intervention Policies”: A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. As such, this project pursues: 1) analysis and prediction of the spread of COVID-19 through a new mathematical model incorporating virus mutations; and 2) optimal and robust control of the spread of COVID-19 by carefully timed interventions. Expected outcomes could give authorities another tool to better assess the effectiveness of existing or potential countermeasures in limiting the spread of COVID-19. They could also help leaders assess the outcomes of eliminating existing countermeasures. Finally, they could help better prepare for different mutation scenarios, including worst-cases (for the current or a future pandemic).
Research team: H. Vincent Poor, Michael Henry Strater University Professor of Electrical Engineering and Interim Dean, School of Engineering and Applied Science at Princeton University; Simon A. Levin, James S. McDonnell Distinguished University Professor in Ecology and Evolutionary Biology and Director of the Center for BioComplexity at University; Osman Yagan, Associate Research Professor of Electrical and Computer Engineering and Dean’s Early Career Fellow at Carnegie Mellon University; and Joshua Plotkin, Walter H. and Leonore C. Annenberg Professor of the Natural Sciences at University of Pennsylvania.
“Housing Precarity, Eviction, and Inequality in the Wake of COVID-19”: Ensuring housing security is vital to mitigating the spread of the COVID-19 virus and sustaining health, economic security, and family stability. This joint, interdisciplinary project will bring together academics and data scientists to track, analyze, and respond to pandemic-driven spikes in eviction and displacement risks. Doing so requires development of: 1) an innovative system for tracking real-time eviction filings after the outbreak; and 2) a housing precarity risk model using machine learning, to better analyze and predict areas at disproportionate risk of displacement in the wake of the COVID-19 pandemic. This project will provide major new sources of data and inform research and public policy regarding U.S. housing and inequality.
Research team: Karen Chapple, Professor and Chair of the Department of City and Regional Planning at UC Berkeley; Matthew Desmond, Maurice P. During Professor of Sociology at Princeton University; and Joshua Blumenstock, Assistant Professor at the UC Berkeley School of Information and Director of the Data-Intensive Development Lab.
“Medical Imaging Domain-Expertise Machine Learning for Interrogation of COVID”: The COVID-19 pandemic represents a pressing public health need for computational techniques to augment the interpretation of medical images in their role for: 1) surveillance, detection, and triaging of COVID-19 medical images given potential resurgence; 2) differential diagnosis of COVID-19 patients; and 3) prognosis, as well as prediction and monitoring of treatment response, to help in patient management.
While thoracic imaging, including chest radiography and computed tomography (CT), are being re-examined for their role in patient management, the limitations for improved interpretation are partially due to the qualitative interpretation of the images, and thus this project’s aim is to develop machine intelligence methods to aid in the interrogation of medical images from COVID-19 patients. Successful completion of the research will demonstrate cascade-based deep transfer learning between similar but different thoracic disease states (e.g., interstitial diseases to COVID) and a clinical tool to aid in the triaging of COVID patients in terms of detection, treatment planning, and monitoring.
Research team: Maryellen L. Giger, A.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago; Jonathan Chung, MD, Vice Chair of Quality and Section Chief of Cardiopulmonary Imaging at the University of Chicago; Samuel Armato, Associate Professor of Radiology at the University of Chicago; Ravi Madduri, Computational Scientist in the Mathematics and Computer Science division at Argonne National Laboratory and a senior research fellow at the Computation Institute at the University of Chicago; and Hui Li, Research Associate Professor, University of Chicago.
“These first three research projects represent the breadth of solutions for COVID-19 mitigation that artificial intelligence can bring to bear on fields as disparate as medicine, urban planning, and public policy,” said Thomas M. Siebel, CEO of C3.ai. “Through the C3.ai Digital Transformation Institute, we have the opportunity to change the course of a global pandemic.”
“These projects exemplify the potential breakthroughs that will come from the science of digital transformation at the nexus of machine learning, IoT, and cloud computing,” said S. Shankar Sastry, C3.ai DTI Co-Director and the Thomas M. Siebel Professor in Computer Science and Professor of Electrical Engineering and Computer Sciences, Bioengineering, and Mechanical Engineering at UC Berkeley. “Breakthrough research is almost always a result of intense collaboration to meet intensely demanding goals. With COVID-19, we have our work cut out for us.”
“We want to improve societal resilience in response to the spread of the COVID-19 pandemic through interdisciplinary and inter-university research,” said R. Srikant, C3.ai DTI Co-Director and the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering. at the University of Illinois at Urbana-Champaign. “I am delighted with these first three funded projects, and there will be more to come that show the promise of digital transformation science.”
About the C3.ai Digital Transformation Institute
Established in March 2020 by C3.ai, Microsoft, and leading universities, the C3.ai Digital Transformation Institute is a research consortium dedicated to accelerating the application of artificial intelligence to speed the pace of digital transformation with business, government, and society for broad socioeconomic benefit. C3.ai DTI sponsors and funds the world’s leading scientists to conduct advanced research in the new Science of Digital Transformation, which operates at the intersection of artificial intelligence, machine learning, Internet of Things, big data analytics, and organizational behavior.
The C3.ai Digital Transformation Institute seed grants enable researchers at consortium member universities to develop larger research proposals and grant submissions to government entities and foundations within a leveraged funding model. To maximize the impact of any findings and potential long-term benefits to society, all research supported by the C3.ai DTI will be freely available in the public domain.
The eight C3.ai Digital Transformation Institute consortium member universities and laboratories are: University of Illinois at Urbana-Champaign (UIUC), University of California, Berkeley, Princeton University, University of Chicago, Massachusetts Institute of Technology, Carnegie Mellon University, Lawrence Berkeley National Laboratory, and National Center for Supercomputing Applications at UIUC.
Industry partners include C3.ai and Microsoft. To support the Institute, C3.ai is providing the Institute $57,250,000 in cash contributions over the first five years of operation. C3.ai and Microsoft will contribute an additional $310 million in-kind, including use of the C3 AI Suite and Microsoft Azure computing, storage, and technical resources to support C3.ai DTI research.
The C3.ai Digital Transformation Institute includes:
- Research Awards: Up to 26 cash awards annually, ranging from $100,000 to $500,000 each
- Computing Resources: Access to free Azure Cloud and C3 AI Suite resources
- Visiting Professors & Research Scientists: $750,000 per year to support C3.ai DTI Visiting Scholars
- Curriculum Development: Annual awards to faculty at member institutions to develop curricula that teach the emerging field of Digital Transformation Science
- Data Analytics Platform: C3.ai DTI will host an elastic cloud, big data, development, and operating platform, including the C3 AI Suite hosted on Microsoft Azure for the purpose of supporting C3.ai DTI research, curriculum development, and teaching.
- Educational Program: $750,000 a year to support an annual conference, annual report, newsletters, published research, and website
- Industry Alignment: C3.ai DTI Industry Partners will be established to assure the institute’s operations are aligned to the needs of the private sector.
- Open Source: C3.ai DTI will strongly favor proposals that promise to publish their research in the public domain.
Communications Director, C3.ai Digital Transformation Institute @ Berkeley