I studied physics at Cornell, biophysics at UCSF, and neuroscience at Stanford. At UCSF I was involved in early work in optogenetics with Chris Voigt and Wendell Lim, engineering light-sensitive proteins for the direct control of intracellular signaling using patterned light controlled with computational microscopy. At Stanford I worked on light-field microscopy with the labs of Karl Deisseroth and Marc Levoy, developing this technique for optogenetic experiments in zebrafish and mice. In industry, I founded a startup that developed a high-throughput DNA synthesis pipeline involving high-throughput single molecule cloning, next-gen sequence verification and physical selection by lasers for radically reducing error rates. I was one of the first employees at Cell Design Labs (acquired by Gilead), which developed next-generation engineered T-cell reagents for fighting blood cancers. There I developed novel synthetic notch receptors for direct contact cell-cell antigen sensing. Additionally, I’ve worked with startups applying deep learning to medical diagnostics and phenotypic screening. I'm broadly interested in machine learning systems for assisting in the analysis and engineering of organisms, cells and and biological circuits, especially in moving beyond the data-poor, intuition-driven “artisanal” engineering approaches typical of existing biomedical projects. I believe we can leverage rich new biological data sources (high throughput imaging, sequencing, high-dimensional cytometry, etc.) via deep-learning approaches to one day accelerate the development cycle of therapeutics and diagnostics. I’m additionally interested in low level software infrastructure for deep learning and more academic aspects of representation learning and generative models over images and sequences.