Mauricio Santillana, PhD, MSc is the director of the Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute at Northeastern University. He is a Professor at both the Physics and Electrical and Computer Engineering Departments at Northeastern University, and an Adjunct Professor at the Department of Epidemiology, at the Harvard T.H. Chan School of Public Health.

Dr. Santillana's research areas include the modeling of geographic patterns of population growth, modeling fluid flow to inform coastal floods simulations and atmospheric global pollution transport models, and most recently, the design and implementation of disease outbreaks prediction platforms and mathematical solutions to healthcare. His research has shown that machine learning techniques can be used to effectively monitor and predict the dynamics of disease outbreaks using novel data sources not designed for these purposes such as: Internet search activity, social media posts, clinician’s searches, human mobility, weather, etc. Dr. Santillana has advised the US CDC, Africa CDC, and the White House on the development of population-wide disease forecasting tools.

Mauricio received a B.S. in Physics with highest honors from Universidad Nacional Autónoma de México in Mexico City, and a Master's and PhD in Computational and Applied Mathematics from the University of Texas at Austin. Mauricio was a Postdoctoral fellow at the Harvard Center for the Environment and later became a lecturer in applied mathematics at the Harvard School of Engineering and Applied Sciences, receiving two awards for excellence in teaching. He became a tenure-track faculty member at Boston Children's Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health. He recently joined the faculty at Northeastern University.

Abstract: I will describe data-driven machine learning methodologies that leverage Internet-based information from search engines (clinicians and the general public), Twitter microblogs, crowd-sourced disease surveillance systems, news alerts, electronic medical records, waste water, and weather information to successfully monitor and forecast disease outbreaks in multiple locations around the globe in near real-time. I will present how these approaches can be used to build early warning systems to anticipate communicable disease outbreaks including COVID-19 outbreaks.

Yen-Chia Hsu is an assistant professor at the Informatics Institute, University of Amsterdam. His research focuses on studying how AI and visual analytics systems can support citizen participation, public engagement, citizen science, and community empowerment, especially in addressing environmental and social issues. He received his Ph.D. in Robotics in 2018 from the Robotics Institute at Carnegie Mellon University (CMU), where he conducted research on using technology to empower local people in tackling air pollution. He received his Master's degree in tangible interaction design in 2012 from the School of Architecture at CMU. Before CMU, he earned his dual Bachelor's degree in both architecture and computer science in 2010 at National Cheng Kung University, Taiwan. More information about him can be found on his website (http://yenchiah.me/).

Abstract: How can scientists co-create AI (Artificial Intelligence) systems with citizens to address environmental and social issues? Recently, it has become an important topic to explore the impact of AI on society. One viable strategy is citizen science, and its previous works have identified expert-based methods of how scientists developed technology for the public to participate in research, such as sustaining participation, verifying data quality, and labeling data. In contrast, there is another community-based perspective that receives significantly less attention: how scientists co-create AI systems with local communities to influence a particular geographical region. This talk will discuss examples and challenges of applying the community-based perspective to create social impact and empower people at a place-based local scale. Three deployed systems focusing on air quality monitoring using different types of data will be presented as examples.