Results for Machine Learning

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.

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. 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. Obstfeld’s research focuses on utilizing clinical and pre-clinical laboratory data sets for predicting diagnosis and prognosis using statistical and machine learning techniques.

Dr. Master works to understand the biochemical pathways related to inborn errors of metabolism, particularly as they relate to mitochondrial function. He also focuses on bioinformatics and machine learning solutions to problems in mass spectrometry and laboratory medicine.

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. Zhou’s outstanding research interests include mitosis-coupled DNA methylation drift and inference of cell-type-specific epigenetic signals. He developed multiple computational tools for analyzing DNA methylation data and has actively contributed to cancer genomics data analysis.

Dr. Parish-Morris investigates social communication, specifically how vocal communication develops in children and adolescents with autism spectrum disorder. She uses computational approaches and machine learning to identify objective and reliable behavioral markers for use in screening, treatment and intervention response tracking, and to advance biological research.

Dr. Campbell is interested in approaches to improve diagnosis of children with rare genetic diseases. He also focuses on better understanding of the full spectrum of symptoms associated with genetic diseases as well as discovering new diseases.