Research Themes

Theme I: Biostatistics Methods

Research interest #1: Statistical modeling of spatially and temporally correlated data. Environmental exposure to pollution, toxins, and hazardous living environments can significantly impact health outcomes. However, data on these exposures often exhibit spatial correlation, skewness, and heterogeneity, which pose challenges for conventional statistical models. In spatially correlated data, individuals closer together are more likely to be similar than those further apart. Ignoring this spatial correlation may lead to biased parameter estimates in the analysis.  The current research aims to bridge this gap by proposing innovative statistical models that can effectively capture the spatial correlation present in the data. The significance of this research lies in its potential to provide more accurate and reliable models for spatially correlated skewed and heterogeneous data. These models could be invaluable in fields such as environmental science, epidemiology, and urban planning, where understanding the spatial dynamics of diverse and unevenly distributed phenomena is critical. 

Research interest #2: Latent variable and trajectory modeling. Many real-world phenomena, such as human development, disease progression, or environmental processes, involve latent factors that significantly influence observed trajectories. The challenge lies in devising models that can accurately capture these latent influences across diverse domains. Latent variable models are instrumental in uncovering underlying structures or hidden variables that might influence observed patterns in the data. Simultaneously, trajectory modeling provides a means to understand the temporal evolution of these latent variables.  The aim is to develop models that can uncover and represent these latent influences accurately. 

Research interest #3: Model checking and diagnosis. Model checking and diagnosis involves advancing the field by creating robust diagnostic tools that not only evaluate the appropriateness of statistical models but also identify and address potential shortcomings. The focus is on enhancing the reliability and accuracy of model assessment, ensuring that statistical models effectively capture the underlying patterns in the data. Through this research interest, we aim to contribute to the development of comprehensive diagnostic methodologies for more accurate and reliable statistical analyses.

Theme II. Applied Quantitative Research

Research interest #1: Health disparity. Our research on health disparities aims to investigate the multifaceted influences on individual and population health outcomes, emphasizing the identification and understanding of disparities in health access, outcomes, and experiences. Within this theme, we assess the interplay of environmental factors, social determinants, and individual characteristics that contribute to health disparities. For instance, we analyze how health disparities manifest in specific areas such as mental health and substance use, cancer epidemiology, and infectious diseases. Through rigorous quantitative methodologies, our research seeks to reveal the complexities of health disparities and provide actionable insights to address inequities.

Research interest #2: Health care utilization pattern. In the area of health care utilization patterns, our research explores how individuals access and utilize healthcare services, aiming to understand and address disparities in healthcare utilization. We investigate the dynamics of healthcare-seeking behavior, considering factors such as socio-economic status, geographical accessibility, and cultural influences that may contribute to disparities. By employing advanced quantitative analyses, we aim to uncover patterns in health care utilization that can inform healthcare delivery strategies, resource allocation, and policy development. Our research examines health care utilization patterns in specific areas such as mental health and substance use services. Through evidence-based insights, we aims to contribute to the reduction of healthcare disparities and the promotion of equitable healthcare access and outcomes.

Research interest #3: Environmental epidemiology. In the area of environmental epidemiology, our quantitative research aims to investigate the relationships between environmental contaminants and human health. This theme focuses on air, water, and soil pollutants, utilizing advanced spatial-temporal analysis methods to discern patterns and trends. Our initiatives include studying the respiratory health impact of climate change, analyzing the spatial-temporal distribution of groundwater pollutants, and examining the influence of environmental factors on infectious disease health outcomes, including for example malaria and the recent case of COVID-19. Through these diverse avenues, our quantitative environmental epidemiology initiatives aim to provide actionable insights for safeguarding public health and promoting sustainable environmental practices.