Michael Figurnov

I am a Staff Research Scientist at Google DeepMind. Previously 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

	Protein structure prediction with AlphaFold NASSMA 2022 AI4Science, 2022 video
	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!