In the battle against the global health crisis, advanced AI solutions for pandemic response are emerging as game-changers. Cutting-edge machine learning applications for Covid-19 treatment, alongside sophisticated natural language processing techniques, are revolutionizing how we track infection rates and make critical decisions. These technologies are instrumental in determining everything from strategic state reopenings to groundbreaking vaccine development. Now, in a significant boost to these efforts, MIT researchers have secured substantial funding to accelerate the development of novel artificial intelligence techniques designed to enhance medical responses and curb the pandemic's spread.
Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) was established with a visionary mission: to unite the world's foremost scientists in a coordinated, innovative effort to drive digital transformation across businesses, governments, and society. This consortium is dedicated to accelerating research breakthroughs by integrating machine learning, artificial intelligence, internet of things, ethics, and public policy to improve societal outcomes. MIT, under the School of Engineering's auspices, joined forces with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and most recently, Stanford University in this groundbreaking initiative.
The initial call for project proposals specifically targeted the challenge of reducing Covid-19 transmission and advancing knowledge, science, and technologies for pandemic mitigation through AI. From an impressive pool of 200 research proposals, 26 exceptional projects were selected and awarded $5.4 million to continue AI research addressing the multifaceted impacts of Covid-19 in medicine, urban planning, and public policy domains.
The first round of grant recipients was recently announced, featuring five pioneering projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.
"We are honored to contribute to this consortium and collaborate with distinguished colleagues across academia, industry, and healthcare to collectively address the current pandemic and mitigate risks associated with future health crises," states Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. "The resources and expertise provided by the C3.ai DTI enable us to accelerate critical Covid-19 research that could have far-reaching implications."
Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects integrating health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project employing machine learning to identify potential treatments for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, partners with Sendhil Mullainathan of the University of Chicago for a project utilizing machine learning to support emergency triage of Covid-19-induced pulmonary collapse based on X-ray analysis.
Bertsimas's project focuses on developing automated, interpretable, and scalable decision-making systems grounded in machine learning and artificial intelligence to support clinical practices and public policies responding to the Covid-19 pandemic. Regarding economic reopening while containing viral spread, Ozdaglar's research delivers quantitative analyses of targeted interventions for diverse population groups, guiding policies calibrated to different risk levels and interaction patterns. Amin is exploring actionable information design and effective intervention strategies to support safe economic activity resumption and mobility service restoration in urban environments. Dahleh's research innovatively applies machine learning to determine optimal strategies for safeguarding educational institutions against outbreaks. Gifford received funding for his project leveraging machine learning to develop more sophisticated vaccine designs with enhanced population coverage and to create models predicting Covid-19 disease severity based on individual genotypes.
"The enthusiastic engagement of MIT's distinguished research community has been instrumental in the rapid launch and significant progress of the C3.ai Digital Transformation Institute," remarks Thomas Siebel, chair and CEO of C3.ai. "It's a privilege to collaborate with such an accomplished team of innovators."
The following projects represent MIT's recipients of the inaugural C3.ai DTI Awards:
"Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions" — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science
"Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data" — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)
"Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic" — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics
"Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak" — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering
"Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections" — David Gifford, professor of biological engineering and of electrical engineering and computer science
"Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19" — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)
"Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic" — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor
