The human gut metagenome was recently discovered to encode vast collections of biosynthetic gene clusters with diverse chemical potential, almost none of which are yet functionally validated. Recent work elucidates common microbiome-derived biosynthetic gene clusters encoding peptide aldehydes that inhibit human proteases.
My webpage has moved! I defended my PhD in August of 2020 and am now a postdoctoral researcher at the NCI Cancer Data Science Laboratory at The National Institutes of Health working with Peng Jiang and Eytan Ruppin.
I was formerly a NSF Graduate Research Fellow and PhD Candidate in the Bioinformatics and Integrative Genomics (BIG) Program at Harvard Medical School in The Division of Medical Sciences under the aegis of The Graduate School of Arts and Sciences. My research interests are biomedical informatics, spatial 'omics, computer vision, functional genomics, machine learning, and personalized genomic medicine. My Erdős number is 4. My research was previously funded by a NSF GRFP Fellowship (September 2018-August 2021) and was previously funded by a NIH T32 grant (August 2016-August 2018) and Amazon (March 2017-March 2018).
In 2016 I graduated cum laude with special departmental honors in computer science from Trinity University. My undergraduate thesis research was conducted under Matt Hibbs on osteoblast development and bone maintenance in Mus musculus where I focused on methods to consider tissue context specificity properly when using machine learning to make gene-gene functional relationship predictions. Additionally, from 2015 to 2016 I worked in Carol Bult’s group on the Patient Derived Xenograft (PDX) project at The Jackson Laboratory where I built a data-mining pipeline that aims to better subtype Triple Negative Breast Cancer tumors and computationally predict chemotherapy drug response in them.
Featured examples of my past work are available on this site; full details about my previous scholarship can be found in my CV.