Colin G. Walsh, MD, MA, is an Assistant Professor of Biomedical Informatics, Medicine, and Psychiatry at Vanderbilt University Medical Center. He is a practicing internist. He received a degree in Mechanical Engineering from Princeton University and his medical degree at the University of Chicago. He completed residency and chief residency in internal medicine at Columbia University Medical Center. He received a degree in Biomedical informatics in postdoctoral fellowship at Columbia University. He joined the faculty at Vanderbilt University in 2015. His research includes: 1) applied predictive modeling to enable behavioral health and prevention; 2) scalable phenotyping for precision medicine; and 3) population health informatics to combat the overdose crisis. When he’s not working on the above, he likes to climb walls.
Current Students and Fellows
Ioana is a second decade PhD student in the Walsh Lab. Her research focuses on predictive deep learning models using multi-modal data.
Barrett is a PhD student from Salt Lake City, Utah. He has a master’s degree in Statistics from Columbia University and prior to moving to Nashville worked as a data analyst at New York-Presbyterian Hospital. He is interested in applying machine learning to better understand depression treatment trajectories. In his free time he enjoys golfing, biking and skiing.
Matt completed his undergraduate degree at Emory University in Economics & Mathematics. He then worked for the electronic health record company Epic Systems as a Technical Service Consultant before returning to the South East to pursue a doctorate in Biomedical Informatics at Vanderbilt. He has technical concentrations in analytics, biostatistics, and machine learning. His research interests include temporal modeling, Recurrent Neural Networks, public health informatics, and feature selection.
Lina is a post-doctoral fellow at Vanderbilt University Medical Center. She received her PhD from Department of Biomedical Informatics at Vanderbilt University. Since biomedical informatics is the holy grail of linking different data sources to understand health, Lina uses different sources of data in her research including structured data, notes, and genetics. Her main interest is applying machine learning and natural language processing to build prediction models and phenotype algorithms.