My work currently involves high-throughput screening of immune-cells at TScan Therapeutics as a computational scientist. My responsibilities include setting up the compute cluster and automating all data processing for our sequencing and other screens. I work on secondary analysis with single-cell immune-cell data to profile immune-cells and identify interesting populations, parsing high-throughput screens, and identifying neo-epitopes for personal cancer profiling.
Previously I was a scientific programmer at the Broad Institute's Foundry for bacterial genetic design, where I designed and produced software for a team of synthetic biologists. Also, I was involved in the SD2 DARPA program with the responsibility to apply machine learning techniques to a variety of high-throughput biophysical experiments.
I had the opportunity to work at Beckman Coulter Genomics (now Genewiz) where I performed variant analysis, differential gene expression, and bioinformatic pipeline development on a variety of species including human.
I achieved my MS in Cell and Molecular Biology at Brandeis University, with a thesis advised by Prof. Sacha Nelson. During this time I studied how differential gene expression changes in motor neurons in the frontal cortex of mice post-removal of a gene for neuronal DNA methylation. Performing laboratory experiments exposed me to the technical variability occurring even in validated protocols and an appreciation for communicating with experimentalists. This has improved my software development when applied to scientific research, whether it is producing a tool for end-user scientific consumption or reproducible and exploratory analytics.
Current research topics include:
High-throughput screening of immune cells
T-cell/B-cell immune profiling at the single-cell level
Epitope discovery and prediction
Past research topics:
Optimal exploration of design of experiments for biological tuning.
Assembly and genetic context evaluation for plasmid constructs.
Pooling plasmids for cost-reduction in next-generation sequencing.
Prediction of whole-cell transcriptional response with machine learning
https://doi.org/10.1093/bioinformatics/btab676COVID-19 Patients Form Memory CD8+ T Cells that Recognize a Small Set of Shared Immunodominant Epitopes in SARS-CoV-2
https://doi.org/10.1016/j.immuni.2020.10.006A Pressure Test to Make 10 Molecules in 90 Days: External Evaluation of Methods to Engineer Biology
https://doi.org/10.1021/jacs.7b13292