Results for Data Modeling

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. Goldsmith examines how clinicians can best capture, analyze, and visualize patient physiologic vital sign data to understand underlying disease physiology and make better informed decisions.

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. 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. Zee's statistical methods research includes topics in survival analysis, measurement error, observational data methods, and machine learning. Her clinical research is focused on kidney disease, specifically in clinical and pathology markers of glomerular and chronic kidney disease progression.

Dr. Falk is a Clinical Geneticist who serves as executive director of the Mitochondrial Medicine Frontier Program. Her translational research lab investigates the causes and global metabolic consequences of mitochondrial disease, as well as targeted therapies, in C. elegans, zebrafish, mouse, and human tissue models of genetic-based respiratory chain dysfunction.

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.

Dr. Shu's research focuses on developing and applying suitable statistical methods for assessing comparative safety and effectiveness of medical products.

Dr. Tan studies transcriptional regulation during normal development and disease. This involves the interplay of multiple transcription and epigenetic factors in a 3D chromosomal environment. Using experimental genomics and computational modeling, Dr. Tan investigates transcriptional regulatory networks underlying embryonic hematopoiesis, T cell differentiation, and pediatric leukemia.

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.