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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
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Itsik Pe’er, Columbia University @ CMU

May 30, 2019 |
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When:
September 20, 2019 @ 12:00 pm – 1:20 pm
2019-09-20T12:00:00-04:00
2019-09-20T13:20:00-04:00
Where:
Posner Hall Room 153
CPCB Seminar Series Seminars

About: Itsik Pe’er is an associate professor in the Department of Computer Science. His laboratory develops and applies computational methods for the analysis of high-throughput data in germline human genetics. Specifically, he has a strong interest in isolated populations such as Pacific Islanders and Ashkenazi Jews. The Pe’er Lab has developed methodology to identify hidden relatives — primarily in such isolated populations — that involves inferring their past demography, detecting associations between phenotypes and genetic segments co-inherited from the joint ancestors of hidden relatives, and establishing the exceptional utility of whole-genome sequencing in population genetics. With the arrival of high-throughput sequencing methods, Pe’er has focused on characterizing genetic variation that is unique to isolated populations, including the effects of such variation on phenotype.

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