Dr. Amy Kuceyeski is an Associate Professor of Mathematics in Radiology and Neuroscience at WCM. Her research interests lie mainly in statistical and mathematical models of the brain's connectivity network, also known as the connectome. She is interested in using machine learning techniques applied to neuroimaging metrics to better understand, diagnose and treat neurological disorders.
Dr. Mert Sabuncu is Assistant Professor in Electrical and Computer Engineering, with a secondary appointment in Biomedical Engineering at Cornell. Dr. Sabuncu's research interests include:
• Biomedical image analysis, with application focus in neurology/neuroscience
• Machine Learning, pattern recognition, multivariate statistics, Bayesian graphical models, approximate inference
• Data mining, applied to large-scale biomedical datasets, including genetics and imaging modalities
• Computational imaging genetics
• Image processing, computer vision
Dr. James K. Min is Professor of Radiology and Medicine at WCM and the Director of the Dalio Institute of Cardiovascular Imaging (ICI) at NewYork-Presbyterian/WCM. He is a board-certified cardiologist with a clinical focus on cardiovascular imaging and cardiovascular disease prevention. The ICI has many clinical and research-oriented goals, and one is to evaluate computational methods that can augment diagnosis or prognostic risk stratification of cardiovascular disease. Active efforts are focused on computational fluid dynamics and machine learning methods for auto-diagnosis of stroke, auto-diagnosis of coronary ischemia and prediction of future myocardial infarction and death.
Dr. Martin T. Wells, Ph.D., is the Charles A. Alexander Professor of Statistical Sciences at Cornell-Ithaca. He is also a Professor of Social Statistics, Professor of Clinical Epidemiology and Health Services Research at Weill Medical School, an Elected Member of the Cornell Law School Faculty, as well as the Director of Research in the School of Industrial and Labor Relations. His research interests include statistical modeling for high-dimensional data, variable selection, multivariate analysis, Bayesian statistics, causal inference, and statistical methods for analyzing complex biological and legal data. He is also interested in statistical methods in machine learning.