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Michael Figurnov
(Михаил Фигурнов)

I am a Staff Research Scientist at DeepMind. Before joining DeepMind, I was a PhD student at the Bayesian Methods Research Group under the supervision of Dmitry Vetrov.

My research interests include deep learning, Bayesian methods, and machine learning for biology. I have worked on AlphaFold which has been recognized as the solution to the protein folding problem.

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

News

July 2021: We have published a Nature paper describing AlphaFold, as well as the source code and a database of predicted structures.

May 2021: I have been promoted to Staff Research Scientist.

Nov 2020: We have announced a new version of AlphaFold at CASP14.

Publications
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar
Nucleic Acids Research, 2022
paper / AlphaFold Protein Structure Database

Applying and improving AlphaFold at CASP14
John Jumper*, Richard Evans*, Alexander Pritzel*, Tim Green*, Michael Figurnov*, Olaf Ronneberger*, Kathryn Tunyasuvunakool*, Russ Bates*, Augustin Žídek*, Anna Potapenko*, Alex Bridgland*, Clemens Meyer*, Simon A. A. Kohl*, Andrew J. Ballard*, Andrew Cowie*, Bernardino Romera-Paredes*, Stanislav Nikolov*, Rishub Jain*, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis*
Proteins: Structure, Function, and Bioinformatics, 2021
paper

Highly accurate protein structure prediction for the human proteome
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A. A. Kohl, Anna Potapenko, Andrew J. Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W. Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis
Nature, 2021
paper / blog post: Putting the power of AlphaFold into the world’s hands

Highly accurate protein structure prediction with AlphaFold
John Jumper*, Richard Evans*, Alexander Pritzel*, Tim Green*, Michael Figurnov*, Olaf Ronneberger*, Kathryn Tunyasuvunakool*, Russ Bates*, Augustin Žídek*, Anna Potapenko*, Alex Bridgland*, Clemens Meyer*, Simon A. A. Kohl*, Andrew J. Ballard*, Andrew Cowie*, Bernardino Romera-Paredes*, Stanislav Nikolov*, Rishub Jain*, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis*
Nature, 2021
paper / code / case study of AlphaFold / blog post "AlphaFold: a solution to a 50-year-old grand challenge in biology" / blog post "Putting the power of AlphaFold into the world's hands"

Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed*, Mihaela Rosca*, Michael Figurnov*, Andriy Mnih*
JMLR, 2020
arxiv / code (TensorFlow) / code (JAX)

Tensor Train Decomposition on TensorFlow (T3F)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V Oseledets
JMLR Open Source Software, 2020
arxiv / code (TensorFlow) / Python package

Measure-Valued Derivatives for Approximate Bayesian Inference
Mihaela Rosca*, Michael Figurnov*, Shakir Mohamed, Andriy Mnih
Bayesian Deep Learning (NeurIPS Workshop) oral, 2019
paper / talk (11 minutes) / code (TensorFlow) / code (JAX)

Variational Autoencoder with Arbitrary Conditioning
Oleg Ivanov, Michael Figurnov, Dmitry Vetrov
ICLR, 2019
arxiv / poster / code

Implicit reparameterization gradients
Michael Figurnov, Shakir Mohamed, Andriy Mnih
NeurIPS spotlight, 2018
arxiv / poster / spotlight video (3 minutes) / spotlight slides / code is integrated into TensorFlow and TensorFlow Probability, eg: Gamma distribution, Beta distribution, Dirichlet distribution, Von Mises distribution, mixture of distributions (set reparameterize=True)

Probabilistic Adaptive Computation Time
Michael Figurnov, Artem Sobolev, Dmitry Vetrov
Bulletin of the Polish Academy of Sciences; Deep Learning: Theory and Practice, 2018
paper / arxiv (slightly older version)

Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov
CVPR, 2017
arxiv / poster / code (TensorFlow)

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Michael Figurnov, Aijan Ibraimova, Dmitry Vetrov, Pushmeet Kohli
NeurIPS, 2016
arxiv / poster / code (Caffe) / code (MatConvNet)

Robust Variational Inference
Michael Figurnov, Kirill Struminsky, Dmitry Vetrov
Advances in Approximate Bayesian Inference, NeurIPS, 2016
arxiv

Talks
Highly Accurate Protein Structure Prediction with AlphaFold
Bayesian Methods Research Group Seminar, 2021
video / slides

Extending the Reparameterization Trick
DeepBayes Summer School, 2018
video / slides

Attention Models for Deep Learning
DeepBayes Summer School, 2017
video (in Russian) / slides


Thanks to Jon Barron for the template!