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Adam Gayoso

PhD Student,
Computational Biology,
UC Berkeley
adamgayoso [at] berkeley [dot] edu

I am a second year graduate student 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 contributor to single-cell Variational Inference (scVI), 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.