For the last few years, I have worked as a Senior Data Scientist at Phase Genomics developing models for the detection of chromosomal abnormalities from proximity ligation sequencing data to identify variants associated with health conditions like cancer and infertility. I earned my Master’s degree in Biology from University of Oregon’s (UO) Applied Bioinformatics and Genomic Master’s Program (2017).
I first became excited about health research working as a biology undergraduate with Drs. Bradshaw and Holzapfel at UO’s Institute of Ecology and Evolution. I worked both independently and with graduate students on research projects studying the evolution of biting in the pitcher plant mosquito, with interest in its implications for fighting the transmission of vector-borne diseases. After graduating with honors from UO in 2014, I began pursuing a Master’s degree in the Bradshaw-Holzapfel lab to continue this research.
During my graduate coursework, I enjoyed learning bioinformatics skills to answer research questions that help fight disease, and transitioned to the Bioinformatics and Genomics Master’s program at UO in 2016. After completing my degree and graduate internship with Dr. Reid Thompson, I joined him and the Portland Genomics (PDXgx) research group to better understand patient response to immunotherapy at the genomic level and develop software to help achieve these goals.
After starting at Phase Genomics in December 2020, I used my bioinformatics skills to assist customers with research projects by analyzing proximity ligation sequencing data from a variety of organisms as a Bioinformatics Research Analyst. In June 2021, after additional training in deep learning principles and methodology, I transitioned to a role as a Data Scientist, focusing on developing both shallow and deep machine learning approaches to identifying structural variants that may contribute to infertility and disease.
To learn more, do not hesitate to reach out to me or explore my work through the links on the sidebar!
Past projects
neoepiscope: flexible, performant neoepitope prediction software accounting for variant phasing- Identification of genomic predictors of response to immunotherapy treatment for cancer
- Prediction of proteasomal cleavage sites for improved neoantigen prediction
- Genomic analyses for a clinical trial assessing the efficacy of pembrolizumab/enzalutamide combination treatment for metastatic, castration-resistant prostate cancer