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  • Dr. Habil Zare Dr. Habil Zare [Send a message]
    Member since: December 12, 2013
    My research interests include the areas of bioinformatics and computational biology. I use machine learning techniques to develop novel algorithms, and provide tools for efficient analysis of biological and clinical data, which are interesting examples of complicated, big data. I collaborate with clinicians and biologists by performing computational analysis on such data, and I am most excited when my study leads to a novel biological discovery, or an immediate improvement of clinical practice.
    News items (4)
    Career Center
    Curriculum vitae

    Current address:

    Texas State University
    601 University Drive
    San Marcos, TX
    United States

    Education:

    PhD in Computer Science
    The University of British Columbia
    2010 - 2011
    MSc
    Sharif University of Technology
    2002 - 2004
    BS
    Sharif University of Technology
    1998 - 2002

    Experience:

    Assistant Professor Texas State University 2014 -
    Senior Fellow (postdoctoral) The University of Washington 2012 - 2014
    Research Assistant British Columbia Cancer Agency 2010 - 2011

    Skills:

    R Statistical Programming Language

    Python

    Mathematica and Maple

    LaTeX

    C

    Activities:

    EDITORIAL BOARD, Journal of Biological Research & Development

    Reviewer: Bioinformatics, OMICS: A Journal of Integrative Biology, Neural Information Processing Systems (NIPS), Pacific Symposium on Biocomputing.

    Secretary of Student Mathematical Association at Sharif University of Technology, 2002-2003.

    Interests:

    Bioinformatics

    Computational Biology

    Machine Learning

    Cancer Research

    Genetics

    References:

    Zare H., et al., Data reduction for spectral clustering to analyze high ., throughput flow cytometry data. BMC Bioinformatics 2010, 11:403-413. ("Highly accessed" according to the journal website.)

    Zare H. et al., Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma, American Journal of Clinical Pathology

    Zare H., et al., relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis, BMC Genomics 2013, 14:S14.

    Aghaeepour N., et al, Zare H., Critical assessment of automated flow cytometry data analysis techniques, Nature Methods 2013, 10:228-238.

    Aghaeepour N., el al., Zare H., Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays, Bioinformatics 2012, 28:1009-1016.

    Other:

    SamSPECTRAL, (approved by Bioconductor) Developed and maintain this R package, an implementation of my enhancement to spectral clustering method that is capable of clustering flow cytometry data with 100K events in minutes.

    FeaLect (approved by CRAN) Developed and maintain this R package that implements my novel scoring scheme useful for feature selection.

    Sessional Instructor, Discrete Mathematics 1, Spring 2008.

    Teaching Assistant for 14 different courses in the areas of computer science and mathematics

    Supervising a rotating student in the Noble Lab at University of Washington, leading to a manuscript submit- ted to PLOS Computational Biology.

     

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