Eisa's Science Blog

About Me

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I am computational biologist focused and interested on solving problems related to immunology in the context of infectious disease and oncology. I have recieved my Ph.D. in Bioinformatics and Computational Biology (BCB) from Oregon Health and Science Unitversity (OHSU) in June 2018. I am profoundly fascinated by the immune system and the mechanisms of immunity to develop treatments and prophylactics, specifically within a precision-medicine framework.

My prior training was in Biology and Chemistry (BS 2006) and Biochemistry and Molecular Biology (MS 2008). From 2008 to 2012 I was a group member in Dr. Louis Picker's Lab, where I focused on immunological studies relating to HIV Vaccines. Our work culminated in a critical examination of vaccine-mediated correlates of protection in Rhesus Macaques (Nature Medicine 2012). This experience seeded the interest and desire to contribute to multi-disciplinary translational biomedical research which propelled me to apply and be accepted as a PhD student in the Division of BCB, in the Department of Medical Informatics and Clinical Epidemiology (DMICE).

As a PhD candidate, I worked under the guidance and mentorship of Drs. David M. Lewinsohn, Marielle C. Gold and Shannon K. McWeeney. I contributed to projects and grants studying a population of tuberculosis (TB)-recognizing T-cells known as mucosal associated invariant T (MAIT) cells. A key idea of these studies was that MAIT cells may be harnessed as unconventional TB vaccine targets.

For my Ph.D. thesis (robust and reproducible classification of rare cellular subsets/signatures (RCS) in single-cell technologies within a transfer learning framework), I focused on strategies for computational immune phenotyping using single-cell technologies (i.e., flow cytometry, CyTOF, and RNA-Seq). Specifically, I developed a machine learning framework (the RTL framework, available on Github) to classify rare cellular subsets in single-cell data because classification of rare events from high-dimensional data is a difficult and highly variable task for humans and machines. Such robust and reproducible methods are a major pillar for accurate predictions of treatment/prophylactic effectiveness and prognostics within a precision-medicine framework. For the near future, I am interesting in continuing to develop robust computational methods to integrate and processes biomedical data as well as addressing clinical hypotheses. Long term, I am interested integrating the mechanisms of immunity and related data to better stratify patients and predicting adverse/positive outcomes.