Results for Computational Modeling

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. Jolley's research lab is focused on visualizing and quantifying congenitally abnormal heart structures using 3D imaging with the driving goal of informing surgical and interventional planning in children and adults with congenital heart disease.

Dr. Sgourakis’ research focuses on understanding the intricate molecular mechanisms that determine the vast repertoire of peptide antigens displayed by the proteins of the Major Histocompatibility Complex for immune surveillance by T cells and Natural Killer cells.

Dr. Alexander-Bloch investigates normal brain development and the altered developmental trajectories that lead to mental illness. His multi-disciplinary research integrates brain imaging, genomics and clinical information.

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. 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. 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. Reid's main project is RADIAL, an adaptive learning education platform that houses filtered knowledge around all subspecialties and delivers a customized curriculum to the end-user using deep learning algorithms.

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. Wang works on biomarker identification using multiparametric magnetic resonance imaging and homo- and hetero-nuclear spectroscopy techniques for pediatric disease research including  tumors, iron overload, cystic fibrosis, muscular dystrophy, autism, mitochondrial, Gaucher’s, and Canavan’s diseases.