I am pursuing my PhD in Bioinformatics and Integrative Genomics at Harvard and am a student in Peter Kharchenko's lab at the Department of Biomedical Informatics. I also collaborate closely with Catherine Wu's lab at the Dana-Farber Cancer Institute. My research interest center around developing computational approaches for interpreting heterogeneity in single cell populations, particularly in the context of hematological malignancies such as CLL and MM. I am currently working on computational algorithms to identify and characterize subpopulations using single cell data.
Outside of research, I teach Computer Science Without Intimidation for 5th graders at The Innovation Institute. I am the founder and lead developer at cuSTEMized. Once in awhile, I try to make time for photography.
My long-term research interests involve the development of a comprehensive understanding of key genetic, epigenetic, and other regulatory mechanisms driving cellular identity and heterogeneity, particularly in the context of cancer and how this heterogeneity shapes tumor progression, therapeutic resistance, and ultimately clinical impact. In order to understand this heterogeneity, novel statistical methods and user-friendly computational software must be developed to enable biologists and researchers to harness the power of big data and other products of future technological advancements.
- Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
- Clonal evolution in patients with chronic lymphocytic leukaemia developing resistance to BTK inhibition.
- Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia.
Teaching and Mentoring
I seek to ensure that students explore and develop their capacity for excellence in bioinformatics through challenging, demanding, and purposeful project-driven teaching and learning. I believe that students must be held to high expectations and be provided with the educational resources and social supports for success. Bi-directional feedback and a culture of dialogue are integral to my teaching and mentoring style.
- SCDE/PAGODA - R package for single cell differential expression and pathway and gene set over-dispersion analysis
- brainmapr - R package to infer spatial location of neuronal subpopulations within the developing mouse brain by integrating single-cell RNA-seq data with in situ RNA patterns from the Allen Developing Mouse Brain Atlas
- LIGER - a light-weight R implementation of the Broad Gene Set Enrichment Analysis algorithm
- UBIT2 - user-friendly bioinformatics webtool for analyzing BioMark RT-QPCR data