Friday Night AI | AI vs. The Flu: Can Technology Predict and Prevent Epidemics?
As flu season approaches, the challenge of predicting and preventing widespread outbreaks is once again at the top of our—and public health officials’—minds. Recent advances in AI offer promising new approaches for monitoring disease spread, analyzing patterns in real-time, and providing early warnings to help limit infections. But how effective are these tools in accurately forecasting outbreaks and guiding preventive action? Join us for a conversation with epidemiologists and AI researchers as we explore the power of machine learning, big data, and predictive modeling to tackle epidemics like the flu or other infectious diseases. Learn about the tools already in place, the hurdles in predicting complex viral patterns, and the steps needed to make AI a trusted ally in public health.
Rada Mihalcea is the Janice M. Jenkins Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in natural language processing, with a focus on multimodal processing and computational social sciences. She is an ACM Fellow, a AAAI Fellow, and served as ACL President (2018-2022 Vice/Past). She is the recipient of a Sarah Goddard Power award (2019) for her contributions to diversity in science, and the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009).
Alexander Rodriguez is an assistant professor of computer science and engineering at the University of Michigan, Ann Arbor. He received his PhD in computer science from the Georgia Institute of Technology in 2023. His research interests include problems at the intersection of machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. These are primarily motivated by public health, computational epidemiology, and community resilience.
Dr. Adam Lauring is a Professor in the Department of Internal Medicine/Division of Infectious Diseases and the Department of Microbiology & Immunology at the University of Michigan. He received his MD and PhD from the University of Washington in Seattle. Dr. Lauring studies the fundamentals of how viruses mutate and how this drives the evolution of poliovirus, influenza virus, SARS-CoV-2. His more recent work utilizes advanced sequencing of influenza virus and SARS-CoV-2 specimens to understand viral spread across communities and the impact of evolution on vaccine effectiveness.
As flu season approaches, the challenge of predicting and preventing widespread outbreaks is once again at the top of our—and public health officials’—minds. Recent advances in AI offer promising new approaches for monitoring disease spread, analyzing patterns in real-time, and providing early warnings to help limit infections. But how effective are these tools in accurately forecasting outbreaks and guiding preventive action? Join us for a conversation with epidemiologists and AI researchers as we explore the power of machine learning, big data, and predictive modeling to tackle epidemics like the flu or other infectious diseases. Learn about the tools already in place, the hurdles in predicting complex viral patterns, and the steps needed to make AI a trusted ally in public health.
Rada Mihalcea is the Janice M. Jenkins Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in natural language processing, with a focus on multimodal processing and computational social sciences. She is an ACM Fellow, a AAAI Fellow, and served as ACL President (2018-2022 Vice/Past). She is the recipient of a Sarah Goddard Power award (2019) for her contributions to diversity in science, and the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009).
Alexander Rodriguez is an assistant professor of computer science and engineering at the University of Michigan, Ann Arbor. He received his PhD in computer science from the Georgia Institute of Technology in 2023. His research interests include problems at the intersection of machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. These are primarily motivated by public health, computational epidemiology, and community resilience.
Dr. Adam Lauring is a Professor in the Department of Internal Medicine/Division of Infectious Diseases and the Department of Microbiology & Immunology at the University of Michigan. He received his MD and PhD from the University of Washington in Seattle. Dr. Lauring studies the fundamentals of how viruses mutate and how this drives the evolution of poliovirus, influenza virus, SARS-CoV-2. His more recent work utilizes advanced sequencing of influenza virus and SARS-CoV-2 specimens to understand viral spread across communities and the impact of evolution on vaccine effectiveness.
