• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Language Proficiency
Address: Moscow, 3 Kochnovsky proezd, room 625
Download CV
ORCID: 0000-0003-1386-8741
ResearcherID: U-8534-2017
Scopus AuthorID: 57194205652
Google Scholar
Working hours
D. Vetrov
Printable version


Have you spotted a typo?
Highlight it, click Ctrl+Enter and send us a message. Thank you for your help!

Michael Figurnov

  • Michael Figurnov has been at HSE since 2015.


Research on acceleration of convolutional neural networks and neural Bayesian methods.



Diploma in Applied Mathematics and Information Science
Lomonosov Moscow State University

Continuing education / Professional retraining / Internships / Study abroad experience

2016 - Google Seattle, USA, intern, supervisor: Li Zhang

2015 - University of Toronto, Canada, visiting student, supervisor: Ruslan Salakhutdinov



Advances in Neural Information Processing Systems 2016 (Барселона). Presentation: PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

A paper on CVPR 2017

Michael Fugurnov's paper written in collaboration with researchers from Google, Carnegie Mellon University and Dmitry Vetrov has been presented on Computer Vision and Pattern Recognition. The conference was held from 21 to 26 July in Honolulu, USA.

Machines Can See: International Summit on Computer Vision

On June 9, a summit was held on computer vision and deep learning ‘Machines can see’, organized by Sistema VC, Visionlabs, and the Strelka Institute. Dmitry Vetrov and Anton Konushin, staff members of HSE Faculty of Computer Science, were among the organizers of and speakers at the conference.

'Machine Learning Algorithm Able to Find Data Patterns a Human Could Not'

In December 2016, five new international laboratories opened up at the Higher School of Economics, one of which was the International Laboratory of Deep Learning and Bayesian Methods. This lab focuses on combined neural Bayesian models that bring together two of the most successful paradigms in modern-day machine learning – the neural network paradigm and the Bayesian paradigm.

Interdisciplinary Seminar of the Strategic Academic Unit 'Mathematics, Computer Science, and Information Technology'

A regular research seminar aimed at sharing the results of research conducted as part of of the Strategic Academic Unit ‘Mathematics, Computer Science, and Information Technology’ and determining prospective interdisciplinary fields was recently held at HSE. This seminar will be organized regularly by different departments within the Strategic Academic Unit.