Research Areas

Precision Medicine and Genomic Implementation

Precision medicine aims to tailor disease prevention, diagnosis, and treatment strategies to the unique genetic and clinical background of each individual patient. The increasing availability of large-scale genomic sequencing and electronic health record (EHR) data has created new opportunities to integrate genomic information directly into clinical care. Our research focuses on developing computational and informatics approaches that support the implementation of precision medicine within healthcare systems. This includes the development of clinical decision support tools, methods for genomic interpretation, and strategies for incorporating polygenic risk scores into routine clinical workflows. By integrating genomic and clinical data across diverse patient populations, we seek to improve individualized risk prediction and translational healthcare delivery.

Statistical Genetics and Complex Disease Genomics

Complex human diseases arise through interactions among genetic, environmental, and molecular factors that collectively influence disease susceptibility and progression. Our work applies statistical genetics and computational genomics approaches to identify genetic contributors to complex traits and diseases, including cardiovascular disease, infectious disease susceptibility, immune-related disorders, and pharmacogenomic responses. We utilize large-scale genomic datasets, biobank resources, and population-level analyses to investigate how genetic variation contributes to disease heterogeneity across diverse populations. Through these efforts, we aim to better understand disease mechanisms and identify genomic factors that can inform precision medicine and therapeutic development.

Biomedical Informatics and Large-Scale Data Integration

The growing scale of genomic and clinical datasets requires robust computational infrastructure and innovative analytical frameworks capable of integrating diverse forms of biological and health-related data. Our research focuses on the development and application of bioinformatics pipelines, cloud-based analysis environments, and scalable computational workflows for genomic and multi-omics research. We integrate genomic sequencing, transcriptomic, phenotypic, and EHR-derived data to investigate the molecular basis of human disease and improve translational research efforts. These approaches are applied within large collaborative research initiatives, including national genomic consortia and population-scale precision medicine programs.

Electronic Health Records and Clinical Informatics

Electronic health records provide a valuable resource for linking genomic information with longitudinal clinical outcomes and real-world healthcare data. Our work leverages EHR-linked biobanks and clinical informatics systems to improve disease phenotyping, genomic implementation, and patient-centered decision-making. We develop methods for extracting and harmonizing clinical phenotypes from EHR systems and integrating these data with genomic analyses to support translational research and healthcare innovation. By combining computational informatics approaches with clinical data resources, we aim to improve the identification of disease risk factors and enable more personalized approaches to diagnosis and treatment.

Translational Bioinformatics and Implementation Science

Translating genomic discoveries into meaningful clinical and public health applications requires interdisciplinary approaches that bridge computational biology, genomics, and healthcare implementation. Our research investigates methods for moving genomic findings from discovery-based studies into clinical practice through translational bioinformatics and implementation science frameworks. This includes developing strategies for genomic data harmonization, evaluating the effectiveness of genomic interventions in healthcare systems, and improving communication between clinicians, patients, and genomic technologies. Through these efforts, we seek to accelerate the responsible integration of genomics into precision health initiatives and evidence-based medical practice.

Collaborative Genomics and National Research Networks

Large collaborative research networks provide critical infrastructure for advancing genomic medicine and translational bioinformatics research at scale. Our work contributes to several national NIH and NHGRI-supported initiatives focused on genomic discovery, clinical sequencing, and precision medicine implementation, including the eMERGE Network and the CSER Consortium. These collaborative efforts emphasize genomic data harmonization, cloud computing, clinical genomics infrastructure, and multi-institutional data integration. Participation in these networks enables the development of scalable computational approaches and collaborative resources that support translational genomics research across diverse healthcare and research environments.