Nick Boyd

I build AI tools that accelerate biological research.

At Atomic AI, I developed models that translate billions of tokens of experimental data into state-of-the-art RNA structure and function predictions. Previously, as CTO of XGenomes, I worked on a fundamentally new approach to DNA sequencing using super-resolution microscopy.

In my PhD at Berkeley (with Ben Recht and Michael Jordan) and brief postdoc at the Broad Institute (with Aviv Regev) I built optimization algorithms and the best algorithms for high-density super-resolution microscopy.

Select Publications

• ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data, bioRxiv, 2023.

• Sequencing by Emergence: Modeling and Estimation, arXiv, 2021.

• Sets as Measures: Optimization and Machine Learning, UC Berkeley, 2018.

• DeepLoco: Fast 3D Localization Microscopy Using Neural Networks, bioRxiv, 2018.

• Saturating Splines and Feature Selection, JMLR, 2018.

• The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems, SIAM Journal on Optimization, 2017.

• Streaming Variational Bayes, NeurIPS, 2013.