Guangquan (Jason) Su
I am an Associate Researcher and Principal Investigator with research interests in understanding and addressing environmental health issues of low income communities and communities of color through the following three inter-connected efforts: (1) Identifying fine scale variations in environmental exposure: I have been using my background in Geographic Information System (GIS) and remote sensing to characterize land use and land cover and use them to enhance modeling small area variations in environmental exposures through machine learning techniques. Integrating multiple types of air pollution measures (e.g., governmental continuous but sparse monitoring, fixed-site and mobile saturation monitoring) into a single modeling framework is also part of my research interests to uncover greater variations in spatiotemporal exposures. Designing fixed site and mobile saturation monitoring is also my research interest to enrich data to decrease uncertainty in assessing spatiotemporal exposures. (2) Ubiquitous monitoring and mobile health application: Special interests focus on understanding individual activity space, when and where acute symptoms occurred and associated environmental exposures at and before acute events. Estimation of modes of transport and travel behavior through smartphone tracking is part of the efforts to understand activity space. Developing smartphone-based mobile apps is part of my research interests to customize the function of application and optimize the performance of research project. Comprehensive data linkage and analysis techniques on large datasets are part of my research efforts to apply data science insights for efficient data processing. Efforts include identifying and linking thousands of asthmatic people’s activity spaces and rescue inhaler events across the entire country for several years to hourly weather conditions and air pollution exposures. I also use my strong background in programming to develop online map-based platforms for vulnerable population to increase physical activity and reduce obesity. (3) Biostatistics and environmental health modeling: On acute symptoms, I focus on modeling instantaneous and time-lagged environmental triggers of acute symptoms. On chronic symptoms, I focus on modeling environmental exposure impacts for large cohort of subjects and across large geographic spaces using multi-level modeling skills that take spatial clustering into consideration. In addition to the traditional mixed-effects modeling techniques that identify one way impact (positive or negative), my interest includes investigating thresholds of impact, to help policy makers examine effectiveness of current standards. The interest in environmental health modeling includes understanding not only the detrimental effects from harmful environmental exposures (e.g., air pollution and noise), but also the protective effects from positive attributes (e.g., green space) and the buffering effects of positive attributes on reducing harmful exposure and improving health for the disadvantaged population.