doi.bio/michael_figurnov


Michael Figurnov

Michael Figurnov is a Staff Research Scientist at DeepMind, where his research interests include deep learning, Bayesian methods, and machine learning for biology.

Education and Career

Figurnov was a PhD student at the Bayesian Methods Research Group under the supervision of Dmitry Vetrov at the Higher School of Economics, AI Research Institute, Moscow. He now works at DeepMind, where he has been involved in the development of AlphaFold, which has been recognised as the solution to the protein folding problem.

Publications

Figurnov has published extensively in the fields of machine learning and biology, with notable works including:

Co-Authors

Michael Figurnov has collaborated with numerous researchers in the field, including:

- Kirill Struminsky

Michael Figurnov

Biography

Michael Figurnov is a researcher in the fields of machine learning, bioinformatics, and computer vision. He has worked with Google DeepMind and Lomonosov Moscow State University. He is also a research assistant at the Higher School of Economics and is associated with the Bayesian Methods Research Group at the Faculty of Computer Science.

Research

Figurnov's research focuses on machine learning and its applications in bioinformatics and computer vision. He has published papers on Monte Carlo gradient estimation, neural networks, and protein structure prediction.

Notable Works

- PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions: Figurnov suggests a novel approach to reduce computational costs by eliminating redundant convolutions in convolutional neural networks.

Michael Figurnov

Biography

Michael Figurnov is a researcher in the fields of machine learning, bioinformatics, and computer vision. He has worked with Google DeepMind and Lomonosov Moscow State University. He is also a research assistant at the Higher School of Economics and is associated with the Bayesian Methods Research Group at the Faculty of Computer Science.

Research

Figurnov's research focuses on machine learning and its applications in bioinformatics, specifically protein structure prediction. He has authored and co-authored several papers, including:

AlphaFold

One of Figurnov's notable contributions is his work with the AlphaFold system, which revolutionised protein structure modelling and design. The AlphaFold Protein Structure Database, powered by AlphaFold v2.0 of DeepMind, enabled an unprecedented expansion of the structural coverage of known protein sequences. The AlphaFold 3 model demonstrated improved accuracy over previous tools, making high-accuracy modelling across biomolecular space possible within a unified deep-learning framework.




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