Adam Gayoso
PhD Candidate,
Computational Biology,
UC Berkeley,
adamgayoso [at] berkeley [dot] edu,

I am a third year PhD candidate in the Center for Computational Biology at UC Berkeley co-advised by Aaron Streets and Nir Yosef. My research focuses on modeling single-cell multiomics data with deep generative models, with the goal of connecting an expressive data representation to common downstream tasks. I am also a core developer of single-cell variational inference tools (scvi-tools), which is a set of probabilistic tools for analyzing single-cell data.

I received both my BS in Operations Research: Engineering Management Systems and MS in Computer Science at Columbia University. At Columbia, I developed computational models for single-cell RNA-sequencing data with the Dana Pe'er Lab including a method to detect doublets in single-cell RNA-sequencing datasets.