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Eisa Mahyari, Ph.D.


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B.S. - Biology and Chemistry double major. Portland State University (PSU), Portland, OR. (2002 – 2006)

M.S. - Biochemistry and Molecular Biology. Oregon Health and Science University (OHSU), Div. Environmental and Biomolecular Systems, Portland, OR. “A six-compartment Physiologically Based Pharmacokinetic (PBPK) Markov-chain Monte Carlo Model to simulate and quantify nicotine metabolism in human populations.” (2006 – 2008).

Ph.D. - Bioinformatics and Computational Biology, OHSU, Department of Medical Informatics and Clinical Epidemiology (DMICE), Division of Bioinformatics and Computational Biology (BCB), Portland, OR. “Robust and reproducible classification of rare cellular subsets/signatures (RCS) in single-cell technologies within a transfer learning framework.” (doi:10.6083/NX9EZJ). Committee members: Shannon K. McWeeney Ph.D., David M. Lewinsohn MD./Ph.D., Michael Mooney Ph.D., Christina Zheng Ph.D., and Evan Lind Ph.D. (2013 – 2018).

Research Statement

I am a computational biologist focused on solving problems related to immunology in the context of infectious disease and oncology. A major research goal of mine is to contribute to multi-disciplinary translational biomedical research. Specifically, leveraging modern technology, computational power, and statistics to bring about new insight and information, as well as testing research hypotheses that were not previously possible. I am profoundly fascinated by the immune system and the mechanisms of immunity to develop treatments and prophylactics, specifically within a precision-medicine framework. To achieve this, robust and reproducible methods are required for computing accurate predictions of intervention effectiveness and provide prognostic insight. This was a motivation for my doctoral research, where I developed a machine learning framework to classify rare cellular subsets in single-cell transcriptomic data, because classification of rare events from high-dimensional data is a difficult and highly variable task for humans and machines. For the near future, I am interesting in continuing to develop robust computational methods to integrate and processes biomedical data as well as addressing specific clinical hypotheses. Long term, I am interested in integrating the mechanisms of immunity and related data to better stratify patients and predicting adverse/positive outcomes.

Professional Experience

2005 - 2007 Research Associate, Research Associate, Clinical Research Investigative Studies Program (CRISP) and Critical Care Academics Associates Program (CCAAP), OHSU. Contribution: Acquisition of clinical data from patient charts for ongoing studies and enrolment of patients into qualifying research in the emergency department and the intensive care units.

2008 - 2012 Sr. Research Associate, Louis Picker M.D., Vaccine & Gene Therapy Institute (VGTI), OHSU. Contribution: Performed experiments, analyzed flow cytometric data, performed statistical tests, and developed reports and presentation martials.

2013 - 2018 Doctoral Student, DMICE-BCB, OHSU. Research Involvement: Genome-based Bioinformatics, Assessment of bioinformatics pipelines identifying genomic structural variants across several sequenced Gibbons. Lucia Carbone Ph.D. Computational phenotyping in cancer cell lines, CyTOF-based computational analysis of phenotypic heterogeneity in breast cancer-derived cell in the context of combinatory anti-cancer drug treatments to identify sensitive phenotypic subtypes within several breast cancer cell lines. Paul T. Spellman Ph.D. Computational immunophenotyping, CyTOF-based computational immunophenotypic analysis of a unique population of tuberculosis (TB)-recognizing T-cells known as mucosal associated invariant T (MAIT) cells. David M. Lewinsohn M.D./Ph.D.

Publications (citations = 133)

Oral & Poster Presentations

Teaching Experience

Research Products

Awards and Honors

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