Alexander Rodríguez
Assistant Professor, Computer Science and Engineering
University of Michigan
With the increasing availability of real-time multimodal data, a new opportunity has emerged for capturing previously unobservable facets of the spatiotemporal dynamics of epidemics. Epidemic forecasting is a crucial tool for public health decision making and planning. However, our comprehension of how epidemics spread remains limited, primarily due to the intricate interplay of various dynamics, particularly social and pathogen-related complexities. In this talk I will present our research at the intersection of time series analysis, spatiotemporal data mining, scientific ML, and multi-agent systems to enable the integration of data, representation learning, and theoretical knowledge from mechanistic epidemiological models.
Bio:
Alexander Rodríguez is an Assistant Professor in Computer Science at the University of Michigan and holds a PhD from the Georgia Institute of Technology. His research spans the intersection of machine learning, time series analysis, and scientific modeling, with a focus on applications in public health and community resilience. His work has garnered recognition through publications at premier AI conferences and multiple awards, including a best paper award. He has also been named a ‘Rising Star in Data Science’ by the University of Chicago Data Science Institute and a ‘Rising Star in ML & AI’ by the University of Southern California. His homepage is alrodri.engin.umich.edu.