Dr. Tsui's research interests include clinical informatics, natural language processing, artificial intelligence and machine learning, population informatics, data science, signal processing, mobile healthcare, and large real-time clinical production systems. He's published over 100 peer-reviewed papers.
Dr. Li conducts methodological research in causal inference, unmeasured confounding, missing data, longitudinal analysis, survival analysis, mediation, Bayesian analyses, and survey methods.
Dr. Resnick's research focuses on the cell signaling mechanisms of oncogenesis and tumor progression in brain tumors. He studies signaling cascades and alterations to elucidate the molecular and genetic underpinnings in order to develop targeted therapies. As co-director of the Center of Data-Driven Discovery in Biomedicine, he leads a multidisciplinary team building and supporting a scalable, patient-focused healthcare and educational discovery ecosystem.
Dr. Huang works on methodology development to understand the dynamics of disease activities and inform health management using multivariate longitudinal health data. She also works on data integration in Clinical Research Networks.
Dr. Forrest's research focuses on developing novel ways of conducting multi-center pediatric applied clinical research, child health services and outcomes research, pediatric person-reported outcome measure development and application, life course health science, learning health systems science, and policies and programs that promote the lifelong health of children. He has a particular interest in the concept and measurement of health.
Dr. Tunç is a computational scientist investigating the application of machine learning and statistical data analysis in various domains such as digital phenotyping, nature of psychopathology, and neuroimaging. He participates in studies using normative, developmental, and clinical samples to parse heterogeneity in psychiatric disorders by developing novel computational techniques.
Dr. Tasian and his research team use an epidemiologic framework, including randomized trials and multi-institutional observational studies, to examine the etiology of kidney stone disease and the comparative effectiveness of surgical interventions. He also employs machine learning of complex data to improve diagnosis, risk stratification, and prediction of treatment response for children and adults with benign urologic disease.
Whitney Guthrie, PhD, is a clinical psychologist and scientist at the Center for Autism Research. Dr. Guthrie’s research focuses on the early developmental trajectories that characterize autism spectrum disorder with the ultimate goal of improving early detection and intervention.
Dr. Shults works to develop statistical methods for longitudinal data that include semi-parametric approaches to account for subject/cluster level associations and maximum likelihood-based approaches for simulation and analysis of discrete longitudinal outcomes that may have overdispersion.
Dr. Simpao, a pediatric anesthesiologist and clinical informatician, has a strong track record of innovation and research. He's led projects that use visual analytics and machine learning models to derive insights and predict outcomes using large healthcare data sets.
