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Research Areas - Computational Biology in the Computer Science Department at Carnegie Mellon

 

CSD faculty: Ziv Bar-Joseph (ML/Lane), Jaime Carbonell, Dannie Durand (Bio), Chris Langmead, Russell Schwartz (Bio), Eric Xing (ML/LTI)

Computational biology is an active area of research at Carnegie Mellon, carried out by faculty members who make this their primary research area as well through collaborations between computational and biological scientists. Areas of emphasis include the application of machine learning and data mining techniques to large biological knowledge bases, biological modeling, computational aspects of high-throughput laboratory methodologies for large-scale, systematic studies of protein structure and function, analysis of biological image data and computational genomics.

Carnegie Mellon University offers educational programs in computational biology through the undergraduate Inter-college Computational Biology Degree, the Masters Program in Computational Biology, and a Ph.D. in Computational Biology through the Joint CMU-Pitt Ph.D. Program in Computational Biology.

Pittsburgh is a fertile environment for research combining biology with other disciplines, including activities at centers such as the Ray and Stephanie Lane Center for Computational Biology, Center for ALgorithm ADaptation Dissemination and INtegration (ALADDIN), the Molecular Biosensor and Imaging Center, the Center for the Neural Basis of Cognition, Faculty of Biomedical Engineering, The Pittsburgh NMR Center for Biomedical Research, and the Pittsburgh Supercomputing Center.

Below we include a list of the CSD faculty working in Computational Molecular Biology, along with a brief summary of their research interests. For a more complete list of faculty at Carnegie Mellon working on computational biology problems, please see the list of the Lane Center faculty.


Ziv Bar-Joseph analyzes high throughput biological datasets (especially gene expression and time series gene expression data, and protein-DNA binding data). His work addresses issues ranging from the experimental design level to the systems biology level.

Jaime Carbonell is investigating machine learning and language-technologies inspired methods for predicting 3D protein structure from protein primary sequences, and in automated classification of protein sequences into functional categories.

Dannie Durand studies the evolution of vertebrate genome organization and functional diversity through gene duplication, using combinatorial methods to analyze data obtained from diverse biological data sets via web-based information retrieval techniques.

Christopher Langmead develops algorithms for modeling the dynamics of biological systems. His work addresses issues relevant to the design of new proteins and drugs, to the creation of patient-specific interventions in cancer and acute inflammation.

Russell Schwartz develops mathematical models and algorithms for studying genome variations between members of a species. He also develops methods for modeling self-assembling biological systems.

Eric Xing develops probabilistic inference and learning algorithms for computational biology and statistical genetics, and for generic intelligent systems of a wide range of applications. His works addresses problems ranging from network modeling in systems biology to genetic polymorphisms associated with diseases.

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