Michael Figurnov

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 protein folding. I've worked on AlphaFold system which has been recognized as the solution to the protein folding problem.

Email / Bio / Google Scholar / Twitter / Github

News

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

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

Publications

	High Accuracy Protein Structure Prediction Using Deep Learning John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Kathryn Tunyasuvunakool, Olaf Ronneberger, Russ Bates, Augustin Žídek, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Martin Steinegger, Michalina Pacholska, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book), 30 November - 4 December 2020 blog post / abstract
	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

	Extending the Reparameterization Trick DeepBayes Summer School, 2018 slides / video
	Attention Models for Deep Learning DeepBayes Summer School, 2017 slides / video (in Russian)

Thanks to Jon Barron for the template!