Lucy Colwell
Lucy is a research scientist at Google Research who works closely with colleagues from GAS and Brain to better understand the relationship between the sequence and function of biological macromolecules. Her broader research interests involve understanding how Google's strengths in experimental design and machine learning can be applied to the discovery and production of proteins for use in a diverse range of applications.
Authored Publications
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Deep diversification of an AAV capsid protein by machine learning
Drew H. Bryant
Ali Bashir
Sam Sinai
Nina K. Jain
Pierce J. Ogden
Patrick F. Riley
George M. Church
Eric D. Kelsic
Nature Biotechnology (2021)
Rethinking Attention with Performers
Valerii Likhosherstov
David Martin Dohan
Peter Hawkins
Jared Quincy Davis
Afroz Mohiuddin
Lukasz Kaiser
Adrian Weller
accepted to ICLR 2021 (oral presentation) (to appear)
Population Based Optimization for Biological Sequence Design
Zelda Mariet
David Martin Dohan
D. Sculley
ICML 2020 (2020)
Evaluating Attribution for Graph Neural Networks
Alexander B Wiltschko
Benjamin Sanchez-Lengeling
Brian Lee
Jennifer Wei
Wesley Qian
Yiliu Wang
Advances in Neural Information Processing Systems 33 (2020)
Model-Based Reinforcement Learning for Biological Sequence Design
David Dohan
Ramya Deshpande
ICLR 2020 (2020)
Deep Learning Classifies the Protein Universe
Drew Bryant
Theo Sanderson
Brandon Carter
D. Sculley
Mark DePristo
Nature Biotechnology (2019)
Using attribution to decode binding mechanism in neural network models for chemistry
Ankur Taly
Federico Monti
Proceedings of the National Academy of Sciences (2019), pp. 201820657
Biological Sequences Design using Batched Bayesian Optimization
Zelda Mariet
Ramya Deshpande
David Dohan
Olivier Chapelle
NeurIPS workshop on Bayesian Deep Learning (2019)
