Dr. Bar-Joseph's primary research is in computational and systems biology. Machine learning and statistical algorithms are used to study time series expression experiments and to combine high throughput biological data sources to model biological networks. Faculty Webpage
Dannie Durand
Associate Professor
The Durand group works in comparative genomics, focussing on the evolution of genome organization and functional diversity in vertebrates. Faculty Webpage
Christopher J. Langmead
Affiliated Biological Sciences Faculty
The Langmead laboratory's interests encompass the areas of structural and temporal phenomena in biological systems. Projects range from new techniques for problems in structural biology to modeling of the 3D growth of brain tumors. Faculty Webpage
Jonathan S. Minden
Professor
A major objective in the Minden laboratory is developing new tools for comparative proteomics. An important aspect of this proteomics work is to develop automated image analysis software to detect and rank protein changes during development and disease. Faculty Webpage
Robert F. Murphy
Professor
Computational cell biology projects in the Murphy laboratory include using computational approaches to analyze the patterns of proteins in fluorescence microscope images to create self-populating knowledge bases and models of protein subcellular location. Faculty Webpage
Gordon S. Rule
Professor
Computational interests in the Rule laboratory include developing methods for the rapid assignment of NMR spectral peaks and, in collaboration with Dr. Michael Erdmann of the Computer Science Department, developing geometric methods to detect structural homologies in proteins from NMR data. Faculty Webpage
Russell S. Schwartz
Associate Professor
The development of modeling and simulation techniques for studying self-assembly systems, as well as methods for analyzing and applying genome polymorphism data are a primary area of interest in the Schwartz laboratory. Faculty Webpage
Nathan N. Urban
Associate Professor
In the Urban laboratory, researchers develop highly reduced and highly detailed models of single neurons and neuronal circuits to understand how the properties of these cells and circuits contribute to the generation of phenomena such as oscillations, synchrony and other complex dynamical behaviors. Faculty Webpage
Eric Xing
Affiliated Biological Sciences Faculty
Eric Xing develops statistical models and machine learning algorithms for biological network inference and characterization, cis-regulatory module decoding, regulatory evolution modeling, quantitative trait locus mapping, genome polymorphism patterning, and population genetic analysis. He is also applying these quantitative approaches to investigate the mechanisms of breast cancer development and metazoan morphagenesis. Faculty Webpage
