Welcome.

I'm a computational biologist who develops methods and algorithms for analyzing genomic and transcriptomic data. My research spans virology, RNA biology, and statistical modeling, with a focus on extracting biological insight from complex, large-scale datasets.

Currently, I'm a postdoctoral fellow in the Virus Persistence and Dynamics Section at the NIH Vaccine Research Center (NIAID), applying computational approaches to questions in viral immunology and persistence.

My work combines statistical rigor with tailored method development. I build analytical frameworks for specific biological problems—from unsupervised inference algorithms to automated genomic pipelines—and I'm increasingly incorporating machine learning into my research toolkit.

I earned my Ph.D. in Biomedical Engineering from UC Davis under Dr. Sharon Aviran, where I developed machine learning approaches for mining transcriptome-wide RNA structure-probing datasets. This work combined unsupervised inference methods with scalable algorithms to identify local RNA structures and their associations with protein binding sites across thousands of transcripts. During my doctoral training, I was also heavily involved in teaching and curriculum development in quantitative and computational biology, which led to my recognition as the Dr. Ronald J. Smith Distinguished Teaching Fellow by the UC Davis College of Biological Sciences.

I'm interested in research positions that combine computational method development with biological discovery, particularly in academic research groups, biotech, or biopharma settings where computational approaches inform scientific direction.