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    Mayte Suarez-Farinas, PhD

    Education

    MSc, University of Havana. School of Mathematics and Computer Sciences.

    MSc, Catholic University of Rio de Janeiro. Computational Statistics Group

    PhD, Catholic University of Rio de Janeiro. Computational Statistics Group

    Postdoctoral Studies, The Rockefeller University. Laboratory of Mathematical Physics

    Awards

    2015

    The Irma T. Hirschl / Monique Weill-Caulier Research Award

    2005

    Frederick Seitz Fellowship

    Rockefeller University

    2003

    Woman in Science Postdoctoral Fellowship

    Rockefeller University

    1999

    National Ph.D Fellowship

    Brazilian National Research Council (CNPQ)

    1997

    National MsC. Fellowship

    Brazilian Ministry of Education (CAPES)

    1995

    Abel Award (Best MSc Thesis)

    University of Havana, School of Mathematics and Computer Scs.

    1995

    Diploma de Oro (graduated with Magna Cum Laude Honor)

    University of Havana

    Research

    Dr. Suarez-Farinas works in the interface of biomedical research, translational science and quantitative analysis, developing robust statistical techniques to mine and integrate complex high-throughput data, tailored to the specific disease model, especially on inflammatory skin diseases. She have extensive experience in clinical trials design and statistical and bioinformatics techniques applied to high throughput data, including microarrays, next generation sequencing data, metabolomics and proteomics.

    Dr. Suarez-Farinas’ research focus on using system biology approaches that combine mechanistic clinical trials and genomic data to unravel the pathogenesis of diseases, develop disease biomarkers and define targets for therapeutics.

    Using bioinformatics analysis of high-throughput data, she had developed molecular phenotypes in psoriasis and atopic dermatitis that allows to reliably measure treatment response at the molecular level. This phenotypes facilitates faster screening for effective drugs, unambiguously benchmark multiple drugs in terms of the improvements they show at molecular level.

    She develops predictive tools that uses genomic data to predict long-term treatment response to psoriasis treatment after a short course of treatment.