Tim Green

Also known as tfgg

Tim Green is a research engineer at Google DeepMind who helped lead the development of AlphaFold 2 and AlphaFold 3, the protein structure prediction systems. He studied at the University of Cambridge, completed a PhD at the University of Oxford in 2014 with a thesis on predicting NMR J-coupling in condensed matter, and co-founded the UK civic technology effort Democracy Club. After joining DeepMind in April 2016, he coauthored the Population Based Training paper in 2017 and the AlphaFold papers published in Nature in 2020, 2021 and 2024. He builds AI systems that accelerate scientific discovery and works on making Gemini a partner for scientists.

Now at: Google DeepMind
Works: 20
Citations: 82,157
h-index: 18
Badges: Founder
Bridge

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

2006–2014

Cambridge and an Oxford doctorate

He studies for an MSci at the University of Cambridge from 2006 and for a PhD at the University of Oxford from 2010 to 2014. His thesis is Prediction of NMR J-coupling in condensed matter, and his research interests cover computational materials science and density functional theory.
2010

Co-founding Democracy Club

He co-founds Democracy Club, an effort to improve UK citizens' experience of democracy using technology. The organization credits him with initial work in 2010, alongside Seb Bacon, Edmund von der Burg and Tom Steinberg.
April 2016

Joining DeepMind as research engineer

After a postdoctoral research fellowship at the University of Oxford from 2014 to 2015, he joins DeepMind as a research engineer in April 2016.
2017

Population Based Training and Kinetics

In 2017, he is among the authors of Population Based Training, an asynchronous algorithm that jointly optimises a population of models and their hyperparameters. He is also an author of the DeepMind Kinetics human action video dataset, which has 400 action classes.
2020

AlphaFold at CASP13 and CASP14

He is an author of the January 2020 Nature paper that introduces AlphaFold, which creates high-accuracy structures for 24 of 43 free-modelling domains at CASP13. At CASP14 in 2020, the redesigned system reaches a median score of 92.4 GDT, and he is among the authors named in DeepMind's citation for that work.
2021

AlphaFold 2 in Nature

He helps lead the development of AlphaFold 2 and is an author of its 2021 Nature paper, which predicts protein structures with atomic accuracy. He is also an author of the 2021 Nature paper that applies AlphaFold 2 to 98.5% of human proteins.
2024

AlphaFold 3 for biomolecular complexes

He is an author of the 2024 Nature paper on AlphaFold 3, whose diffusion-based architecture predicts the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. He helps lead its development, and the 2024 Nobel Prize in Chemistry recognizes AlphaFold 2.
As of October 2026

Gemini for science at DeepMind

He is a research engineer at Google DeepMind, building AI systems to accelerate scientific discovery. He works on making Gemini the best partner for science and scientists.