Synergizing Topological Deep Learning and Spatio-Temporal Causal Inference to Unpack Health
Official title: SCH: Synergizing Topological Deep Learning and Spatio-Temporal Causal Inference to Unpack Health
Funded at Harvard University D/b/a Harvard School of Public Health · NIH-11417181 · Data updated 2026-11-12
A team at a leading public health school is developing new data tools to better understand how environmental factors like air pollution and extreme weather affect health, using large, complex health data.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Harvard University D/b/a Harvard School of Public Health NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | July 6, 2024 |
| Project end | April 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $281,043 |
| NIH project number | 5R01ES037156-03 |
| Project ID | NIH-11417181 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable to a specific patient population; this is a methodological grant for health data analysis.
✕Less likely to help
Direct patient participation is not described; benefits would come through improved public health insights.
What this research studies
Researchers will combine advanced deep learning with causal methods to analyze big, multi-resolution health data that include both individual and neighborhood information. The work aims to create practical tools and theory that can uncover how environmental exposures influence health outcomes across time and space. The project brings computer scientists and biostatisticians together to build a scalable framework for learning from spatiotemporal data and aggregated outcomes. The goal is to provide clearer insights into how environment shapes health, potentially informing policy and interventions.
Why it matters
Potential benefit: If successful, the tools could help public health officials and researchers pinpoint environmental risks and design targeted strategies to improve health outcomes.
How similar studies have gone: Previous work shows promise for combining topological methods with spatial statistics, but this project aims to advance the framework further.
Where this research is happening
-
Harvard University D/b/a Harvard School of Public Health
Boston, United States Active
Researchers
- Principal investigator: Dominici, Francesca — Harvard University D/b/a Harvard School of Public Health
- Study coordinator: Dominici, Francesca
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.