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.
Welcome to my home page. I am 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. I am working on my thesis in Meromit Singer's group at the cBio Center at Dana-Farber Cancer Institute where I primarily work on dynamics of T cell transit in the context of autoimmunity and cancer immunotherapy by integrating machine learning techniques with rapid experimental validation methods that leverage emerging sequencing technologies. My research interests are autoimmunity, type 1 diabetes, single cell immunogenomics, meta`omics, functional genomics, machine learning, and personalized genomic medicine. My Erdős number is 4. My research is currently externally 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).
During the fall of 2016 I worked in the Gehlenborg Lab in the Department of Biomedical Informatics at HMS on HiGlass. During the summer of 2016 I rotated in the Huttenhower Lab jointly in the Department of Biostatistics at the T.H. Chan School of Public Health and at the Broad Institute of MIT and Harvard where I worked on techniques and pipelines to help with putative protein function prediction algorithms that worked across many metagenomic/metatranscriptomic contexts.
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.