Research in the Salzberg Lab focuses on the development of new computational methods for analysis of DNA from the latest sequencing technologies. Over the years, we have developed and applied software to many problems in gene finding, genome assembly, comparative genomics, evolutionary genomics and sequencing technology itself. Our current work emphasizes analysis of DNA and RNA sequenced with next-generation technology.

Researchers in the Henry Michtalik Lab are interested in patient safety—particularly as it relates to patient census statistics and acute to primary care transitions—and quality improvement and systems management.

The Suchi Saria Lab, part of the Institute for Computational Medicine, explores topics within the fields of machine learning and computational statistics, with a focus on computational solutions for problems in health informatics. Our team investigates the applications of machine learning and computational statistics to domains where one has to draw inferences from observing a complex, real-world system evolve over time. We use Bayesian and probabilistic graphical modeling approaches to address the challenges that emerge with modeling and prediction in real-world temporal systems.