• About Us
    • Leadership
    • What is Computational Biology?
    • What is Automated Science?
    • Computational Biology Careers Website
    • List of Educational Programs in Computational Biology
    • The Hillman Center
    • History
    • CBD at a Glance
    • News and Events
      • News
      • Calendar
      • Departmental Seminars
      • Meetings
    • Department Resources
  • Education
    • Ph.D. in Computational Biology
    • M.S. in Automated Science
    • M.S. in Computational Biology
    • Undergraduate Program in Computational Biology
      • Major in Computational Biology
        • Why Major in Computational Biology?
        • Degree Requirements
        • The Undergraduate Research Pledge
        • Sample Course Sequence for Computational Biology Majors
        • Guidelines for Transfer to Major in Computational Biology
        • Suggested Courses for “Pre-Med” Computational Biology Majors
        • Additional Major in Computational Biology
          • Double-Counting Suggestions for Additional Majors in SCS
      • Minor in Computational Biology
      • Concentration in Computational Biology
      • Visit Us
    • Pre-College Program in Computational Biology
    • Courses Offered
      • Undergraduate Courses Offered
      • Graduate Courses Offered
      • Course Profiles
        • 02-201/601 Programming for Scientists
        • 02-223 Personalized Medicine: Understanding your Own Genome
        • 02-250 Introduction to Computational Biology
        • 02-251 Great Ideas in Computational Biology
        • 02-261 Quantitative Cell and Molecular Biology Laboratory
        • 02-319/719 Genomics and Epigenetics of the Brain
        • 02-331/731 Modeling Evolution
        • 02-402/702 Computational Biology Seminar
        • 02-414/614 String Algorithms
        • 02-425/725 Computational Methods for Proteogenomics and Metabolomics
        • 02-450/750 Automation of Scientific Research
        • 02-500 Undergraduate Research in Computational Biology
        • 02-510/710 Computational Genomics
        • 02-512/02-712 Computational Methods for Biological Modeling and Simulation
        • 02-518/718 Computational Medicine
        • 02-530/730 Cell and Systems Modeling
        • 02-602 Professional Issues in Computational Biology
        • 02-604 Fundamentals of Bioinformatics
        • 02-605 Professional Issues in Automated Science
        • 02-613 Algorithms and Advanced Data Structures
        • 02-620 Machine Learning for Scientists
        • 02-700 M.S. Research
        • 02-701 CPCB Course / Current Topics in Computational Biology
        • 02-715 Advanced Topics in Computational Genomics
        • 02-740 Bioimage Informatics
        • 02-750 Automation of Scientific Research
        • 02-760 Laboratory Methods for Computational Biologists
        • 02-761 Laboratory Methods for Automated Biology I
        • 02-762 Laboratory Methods for Automated Biology II
        • 02-801 Computational Biology Internship
        • 02-900 Ph.D. Thesis Research
  • Research
    • Software
    • Faculty Research Pages
    • Computational Biology Technical Reports
    • White Papers
  • People
    • Faculty
      • Voting Faculty
      • Affiliated Faculty
      • Visiting Faculty
      • Adjunct Faculty
      • Visiting Interns
    • Staff
      • Department Staff
      • Research Staff
    • Fellows and Special Faculty
      • The Lane Fellows Program
      • Current Lane Fellows
      • Past Lane Fellows
      • Postdoctoral Fellows
      • Past Postdoctoral Fellows
      • Special Faculty
    • Alumni
      • Ph.D. Graduates
      • Alumni Profiles
  • Join Us!
    • Life in Pittsburgh
      • Neighborhoods Near Carnegie Mellon University
      • Things to Do in Pittsburgh
    • Positions Available
    • Apply to Ph.D. Program
    • Apply to MSAS
    • Apply to MSCB
    • Apply to Undergraduate Program
    • Apply to be a Lane Fellow
  • Donate!
Find us
help@cbd.cmu.edu
Computational Biology Department Computational Biology Department
  • About Us
    • Leadership
    • What is Computational Biology?
    • What is Automated Science?
    • Computational Biology Careers Website
    • List of Educational Programs in Computational Biology
    • The Hillman Center
    • History
    • CBD at a Glance
    • News and Events
      • News
      • Calendar
      • Departmental Seminars
      • Meetings
    • Department Resources
  • Education
    • Ph.D. in Computational Biology
    • M.S. in Automated Science
    • M.S. in Computational Biology
    • Undergraduate Program in Computational Biology
      • Major in Computational Biology
        • Why Major in Computational Biology?
        • Degree Requirements
        • The Undergraduate Research Pledge
        • Sample Course Sequence for Computational Biology Majors
        • Guidelines for Transfer to Major in Computational Biology
        • Suggested Courses for “Pre-Med” Computational Biology Majors
        • Additional Major in Computational Biology
          • Double-Counting Suggestions for Additional Majors in SCS
      • Minor in Computational Biology
      • Concentration in Computational Biology
      • Visit Us
    • Pre-College Program in Computational Biology
    • Courses Offered
      • Undergraduate Courses Offered
      • Graduate Courses Offered
      • Course Profiles
        • 02-201/601 Programming for Scientists
        • 02-223 Personalized Medicine: Understanding your Own Genome
        • 02-250 Introduction to Computational Biology
        • 02-251 Great Ideas in Computational Biology
        • 02-261 Quantitative Cell and Molecular Biology Laboratory
        • 02-319/719 Genomics and Epigenetics of the Brain
        • 02-331/731 Modeling Evolution
        • 02-402/702 Computational Biology Seminar
        • 02-414/614 String Algorithms
        • 02-425/725 Computational Methods for Proteogenomics and Metabolomics
        • 02-450/750 Automation of Scientific Research
        • 02-500 Undergraduate Research in Computational Biology
        • 02-510/710 Computational Genomics
        • 02-512/02-712 Computational Methods for Biological Modeling and Simulation
        • 02-518/718 Computational Medicine
        • 02-530/730 Cell and Systems Modeling
        • 02-602 Professional Issues in Computational Biology
        • 02-604 Fundamentals of Bioinformatics
        • 02-605 Professional Issues in Automated Science
        • 02-613 Algorithms and Advanced Data Structures
        • 02-620 Machine Learning for Scientists
        • 02-700 M.S. Research
        • 02-701 CPCB Course / Current Topics in Computational Biology
        • 02-715 Advanced Topics in Computational Genomics
        • 02-740 Bioimage Informatics
        • 02-750 Automation of Scientific Research
        • 02-760 Laboratory Methods for Computational Biologists
        • 02-761 Laboratory Methods for Automated Biology I
        • 02-762 Laboratory Methods for Automated Biology II
        • 02-801 Computational Biology Internship
        • 02-900 Ph.D. Thesis Research
  • Research
    • Software
    • Faculty Research Pages
    • Computational Biology Technical Reports
    • White Papers
  • People
    • Faculty
      • Voting Faculty
      • Affiliated Faculty
      • Visiting Faculty
      • Adjunct Faculty
      • Visiting Interns
    • Staff
      • Department Staff
      • Research Staff
    • Fellows and Special Faculty
      • The Lane Fellows Program
      • Current Lane Fellows
      • Past Lane Fellows
      • Postdoctoral Fellows
      • Past Postdoctoral Fellows
      • Special Faculty
    • Alumni
      • Ph.D. Graduates
      • Alumni Profiles
  • Join Us!
    • Life in Pittsburgh
      • Neighborhoods Near Carnegie Mellon University
      • Things to Do in Pittsburgh
    • Positions Available
    • Apply to Ph.D. Program
    • Apply to MSAS
    • Apply to MSCB
    • Apply to Undergraduate Program
    • Apply to be a Lane Fellow
  • Donate!

About Us

cbd-picnic

Our Vision

Robert F. Murphy
Head, Computational Biology Department

Computational biology is a critically important and growing field that is essential to biomedical research.  The Computational Biology Department at Carnegie Mellon is part of the internationally-recognized School of Computer Science, and draws upon the incredible energy and expertise in the entire School.  It is an ideal place to be educated in this essential discipline.

The approach to computational biology that we take at Carnegie Mellon is unique in several ways.

First, we strongly believe that computational biology has important contributions to make in framing biological problems in computational terms (see “What is Computational Biology?”) and should not just be focused on helping biomedical researchers “understand their data.”  We place a lot of emphasis on novel ways of how viewing biology through a computational lens has led to research advances.

Second, as befits our being in a School of Computer Science, we emphasize the importance of developing computationally rigorous solutions to problems, which goes hand-in-hand with framing those problems well.  We try to avoid developing a piecemeal or ad hoc solution or relying on current thinking of how a biological process might work and instead try to find a theoretically sound way to compute what is needed directly from primary biomedical data.

Third, we provide an important grounding in both natural and computational sciences.  The role of computational biologists in framing problems requires knowledge of the fundamental principles of both.

Fourth, we believe strongly in the need for computational biology to drive biological experimentation.  There has been enormous discussion in the popular and scientific media about the need for automated analysis of “Big Bio Data” because of its overwhelming size.  While there are many opportunities created by large biomedical datasets, we stress that these datasets do not come close to being sufficient to develop accurate models of complex biological systems.  Most large datasets are currently acquired by choosing a small set of variables and sampling them very thoroughly.  This results in the acquisition of a lot of measurements that could have been predicted from others.  Furthermore, it is not feasible, either in terms of cost or time, to do this type of exhaustive sampling for all combinations of variables.  We believe in the future that iteratively doing modest sets of experiments chosen by computer models rather than individual investigators, a process called active machine learning, will enable more effective research.

Finally, a core value of Carnegie Mellon is its collaborative spirit. Collaborations between experimental and computational biologists is what drives the recent advances in the general area of systems biology. The Computational Biology Department fosters unique opportunities to students to be involved in such collaborations building on the great tradition of interdisciplinary work at CMU. Our students learn from and work with experimental faculty, and they participate in cutting edge research that is jointly performed by computational and experimental researchers.

Together, the embodiment of these principles in our degree programs helps students to develop as independent innovators who will help guide the future of biomedical research, and not just be able to apply today’s methods to today’s problems.

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Contact Us

  • Computational Biology Department
  • Carnegie Mellon University
  • 5000 Forbes Ave GHC 7725, Pittsburgh, PA 15213
  • (412) 268-3480
  • help@cbd.cmu.edu
  • www.cbd.cmu.edu
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