Welcome to the Center for Integrative Biocomputing and Experimental Mathematics (CIBEM)

Recent advances in the development and application of cutting-edge biomedical technologies have significantly accelerated the generation of complex high-throughput data that potentially enable us to gain new insights into life sciences. However, to analyze and extract meaningful information from such complex data, novel and sophisticated quantitative techniques and approaches must be developed and integrated systematically. The quantitative and computational methods for high-throughput data have become a major component in biomedical research and an indispensable tool in interrogating biological systems in the modern era.

The CIBEM was established in March 2012 within the Department of Biostatistics and Computational Biology (DBCB) to integrate and consolidate available bioinformatics and computational biology resources and expertise at the University of Rochester to meet these challenges. Dr. Hulin Wu, newly named as Dean’s Professor of Biostatistics and Computational Biology, has been appointed as the founding director of the CIBEM. Although most of the Center’s faculty members will have primary appointments in DBCB, the Center’s membership may also include adjunct faculty from other departments.

CIBEM faculty

Mission

  • Build a strong interdisciplinary research program and an academic home for bioinformatics and computational biology to support the Integrated Disease Program (IDP) of the Immunology and Infectious Diseases in the URMC strategic plan by integrating available resources and expertise on the UR campus. The success of the CIBEM will serve as a role model for other IDPs.
  • Proactively participate in and support the Innovative Science Programs (ISPs) in the URMC strategic plan, in particular for the two ISPs, genomics/systems biology and biomedical imaging/biomarkers.
  • Develop and promote state-of-the-art bioinformatics and computational biology methodologies and techniques to analyze and query complex data collected from basic science experiments, clinical studies and translational research by integrating quantitative technologies from statistics, mathematics, computational sciences, informatics, engineering and physics.
  • Develop a PhD/MS education program in bioinformatics and computational biology.