Neil Thomas

I work at the intersection of AI and biology. I am currently at EvolutionaryScale building AI tools for protein design.

Previously, I was a Research Scientist at Google X. I completed my PhD in Computer Science at UC Berkeley in 2022, advised by Professor Yun S. Song. Prior to that, I was an AI Resident at Google X and a Software Engineer at 23andMe. I received my BS in Engineering Mathematics and Statistics from UC Berkeley.

When I'm not being humbled by biology, I like to be humbled by a variety of hobbies. I like cooking recipes from Alison Roman, climbing rocks, skiing, playing ultimate frisbee, cycling, playing piano, watching comedy, and watering my plants.

email  /  twitter  /  github  /  scholar  /  linkedin

Research Highlights

My research focuses on learning meaningful representations of proteins, with the aim of enabling applications in protein design, functional annotation, and structure prediction. Check out my thesis talk "Browsing in the Library of Babel" for an accessible introduction.

Simulating 500 million years of evolution with a language model

A multi-modal generative protein model that reasons flexibly across protein structure, function, and sequence.

Engineering highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Designed thousands of highly active, diverse nuclease enzymes using neural network models trained on experimental screening data, outperforming a traditional in vitro directed evolution campaign.

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Tunable, realistic, synthetic fitness landscapes for benchmarking protein design.

Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention

Introduces “factored attention,” a simplified attention layer that we use to compare and contrast Potts models and Transformers.

Evaluating Protein Transfer Learning with TAPE

A suite of benchmarking tasks for protein language models.

Research

For an up-to-date list, see Google Scholar

Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency

Minding the gaps: The importance of navigating holes in protein fitness landscapes

Teaching

During my graduate studies at Berkeley I had the privilege of teaching:

• Summer 2022 CS 188: Introduction to Artificial Intelligence
• Fall 2020 Stat 135: Concepts of Statistics

My teaching statement.

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