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


I am a PhD candidate in the Center for Computational Biology at UC Berkeley co-advised by Aaron Streets and Nir Yosef. I develop deep generative models for single-cell omics data that learn an expressive representation of the data and facilitate common analysis tasks. In my research, I use these models to refine our understanding of immune cells with the goal of characterizing cellular states across different contexts. I am also the co-creator of scvi-tools, which is a Python package that provides: (1) accessible implementations of state-of-the-art single-cell probabilistic models and (2) the building blocks to rapidly develop new models.

During Summer 2022, I was a Research Scientist Intern on the Science team at DeepMind. Previously, I received my BS in Operations Research: Engineering Management Systems and MS in Computer Science from 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.