As part of the Computational and Systems Program at MSKCC, I run a split experimental/computational lab focused on high-content methods in genetics. Interests include analytical models of how genes interact, development of new high-throughput assays, and applications to understanding tissue fibrosis and rheumatoid arthritis.

My postdoctoral research focuses on new experimental approaches enabled by massively parallel single-cell RNAseq. I am co-developer of Perturb-seq, a method for performing pooled genetic screens with rich phenotypic readouts.

Continuing the work from my PhD, I focused on mechanistically understanding why cells behave differently. Using the technology developed during my PhD, I identified how interactions as simple as two proteins binding to each other can lead to large, random differences in behavior between genetically identical cells.

Because they take place at such small scales, processes like gene expression are fundamentally random, meaning that cells that may seem outwardly identical (same genes, same environment) can nevertheless behave quite differently. My PhD studied these "stochastic" effects in cellular behavior. I developed a microfluidic technology for capturing hundreds of cells and watching them over long periods of time, which allowed unprecedented quantitative measurements of cellular behavior. Using probability theory, I also described how stochasticity imposes fundamental constraints on how accurate cells' activities can be.