Data Science at the Intersection of Public Health and the Environment

The Institute for Mathematical and Statistical Innovation (IMSI) and the National Institute of Statistical Sciences (NISS) are organizing a workshop on Data Science at the Intersection of Public Health and the Environment. This event will bring together experts from diverse fields to explore innovative methodologies, foster collaboration, and address pressing challenges in public and environmental health using data science techniques.

Description

Human-natural systems are increasingly interconnected, with data-driven science and engineering enhancing our understanding of these complex interactions. The intersection of environment and public health is of paramount importance, as environmental changes can significantly impact human health outcomes. Statistical and mathematical tools play a crucial role in understanding these intricate relationships, and emergent data sources combined with modern methods offer promising solutions. However, transdisciplinary methodological innovations are necessary to fully tackle these multifaceted challenges.

Key Research Areas:

• New inferential approaches: Summarizing, linking, and analyzing diverse datasets from epidemiological studies, health registries, environmental monitoring, and surveys to uncover shared patterns, trends, associations, and causal relationships.
• Species abundance and diversity modeling: Leveraging big data to assess ecosystem and biodiversity health across different spatial and temporal resolutions.
• Innovative sampling techniques: Designing efficient and representative data collection methods while quantifying variability, bias, and uncertainty in joint environmental and health studies.
• Co-modeling of extremes: Developing methodologies to model the probability and magnitude of rare events in both environmental and human health domains (e.g., floods, wildfires, droughts, pandemics, food insecurity).
• Mathematical modeling of environmental systems: Simulating biological, physical, and chemical processes, hypothesizing tipping points, and integrating causal models to assess intervention impacts.

Workshop Format

This workshop will feature an intensive in-person research studio or ideas lab, fostering high-impact interdisciplinary collaboration. The event will commence with keynote presentations and tutorial sessions, followed by iterative brainstorming and working group formation. This workshop will take place at the Institute for Mathematical and Statistical Innovation (IMSI) on the campus of the University of Chicago.

Structure:

1. Virtual Pre-Workshop Events: Introductory tutorials and dataset overviews from leading experts. More information can be found at NISS, and questions should be directed to them.
2. In-Person Workshop (5 days):
   • Keynote presentations on grand challenges and cutting-edge methodologies.
   • Facilitated small-group discussions alternating with full-group presentations and feedback sessions.
   • Development of concrete research questions, collaborative writing, and initial project ideation.
3. Post-Workshop Activities:
   • Follow-up meetings for research working groups.
   • Themed virtual events (hackathons, data visualization sessions, poster sessions for new researchers).
   • Potential joint IMSI-NISS long-term research programs.

Registration

Registration for this event closed on August 25, 2025. Decisions will be issued in the next few weeks.