MScAC talks

Translating Applied Computing Research into Environmental and Public Health Impact

AI and machine learning are playing an increasingly critical role in advancing research at the intersection of environmental science and public health. With the growing availability of high-resolution and high dimensional environmental data, from satellite remote sensing and atmospheric models to low-cost sensor networks and wearable health monitors, there is a unique opportunity to contribute to public good by developing tools that transform complex data and computing into actionable insights.

This talk will highlight real-world applications of AI to environmental exposure assessment and health impact modeling, with a focus on challenges such as climate change, air pollution, wildfire smoke, and emissions from oil and gas operations. Using examples from ongoing interdisciplinary research, we will explore how spatiotemporal machine learning models, deep learning for image analysis, and clustering tools are being used to detect pollution hotspots, assess health risks, and guide both regulatory and public health interventions.

Equally important is the translation of these research findings beyond the academic setting, to communities that are historically overburdened by environmental hazards; to agencies responsible for environmental health protection; and to industry sectors where transparent, data-driven accountability is essential.