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Full Professor, Director of Skoltech Applied AI center

**Research Center in Artificial Intelligence in the Direction of Optimization of Management Decisions to Reduce Carbon Footprint**

Evgeny Burnaev graduated from the Moscow Institute of Physics and Technology in 2006. After getting a Candidate of Sciences degree from the Institute for Information Transmission Problem in 2008, he stayed with the Institute as a head of the Data Analysis and Predictive Modeling Lab.

Since 2007 Evgeny Burnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others. The corresponding data analysis algorithms, developed by Evgeny Burnaev and his scientific group, formed a core of the algorithmic software library for metamodeling and optimization. Thanks to the developed functionality, engineers can construct fast mathematical approximations to long-running computer codes (realizing physical models) based on available data and perform design space exploration for trade-off studies. The software library passed the final Technology Readiness Level certification in Airbus. According to Airbus experts, the application of the library “provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process”. Nowadays a spin-off company Datadvance develops a Software platform for Design Space Exploration with GUI based on this algorithmic core.

Since 2016 Evgeny Burnaev is an Associate Professor in Skoltech CDISE and a head of **Advanced Data Analytics in Science and Engineering** group. Since 2022 Evgeny Burnaev is a Full Professor and a director of Skoltech Applied AI center. In 2022 Evgeny got a degree of Doctor of Physical and Mathematical Sciences, Moscow Institute of Physics and Technology.

Evgeny’s current research focuses on the development of new algorithms in machine learning and artificial intelligence such as deep networks for an approximation of physical models, generative modeling, and manifold learning, with applications to computer vision and 3D reconstruction, neurovisualization. The results are published in top computer science conferences (ICML, ICLR, NeurIPS, CVPR, ICCV, and ECCV) and journals.

Prof. Burnaev was a co-organizer of **Machine Learning Summer School (MLSS)** in 2019 and of

**Summer School of Machine Learning (SMILES)** in 2020, with top-lecturers and participants from all over the world.

Evgeny Burnaev was honored with several awards for his research, including Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading **the project “The development of methods for predictive analytics for processing industrial, biomedical and financial data” **(2017), Geometry Processing Dataset Award for the work “ABC Dataset: A Big CAD Model Dataset For Geometric Deep Learning”, Symposium on Geometry Processing (2019), Best Paper Award for the research in eSports at the IEEE Internet of People conference (2019), Ilya Segalovich Yandex Science Prize “The best research director of postgraduate students in the field of computer sciences” (2020), Best Paper Award for the research on modeling of point clouds and predicting properties of 3D shapes at the Int. Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR) (2020), Best cooperation project leader award (Huawei, 2021).

Prof. Burnaev has been a PI and Co-PI of several grants and industrial projects (250 million rubles in total since 2017).

Evgeny Burnaev developed and is teaching three full courses from the Skoltech CDISE curriculum, namely, courses on Machine Learning, Bayesian Machine Learning, and Foundations of Data Science. Seven of his Ph.D. students have successfully defended their theses.

Evgeny’s current research focuses on the development of new algorithms in machine learning and artificial intelligence such as deep networks for the

- approximation of physical models,
- generative modeling, and manifold learning,

with applications to

- computer vision and 3D reconstruction,
- neurovisualization

- Alexander Korotin, Arip Asadulaev, Vage Egiazarian, Alexander Safin, Evgeny Burnaev. Wasserstein-2 Generative Networks. In ICLR 2021: Proceedings of the International Conference on Learning Representations, 2021
- Alexander Korotin, Lingxiao Li, Justin Solomon, Evgeny Burnaev. Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization. In ICLR 2021: Proceedings of the International Conference on Learning Representations, 2021
- Denis Volkhonskiy, Ruslan Rakhimov, Alexey Artemov, Denis Zorin, Evgeny Burnaev. Latent Video Transformers. VISAPP, 2021
- R Rakhimov, E Bogomolov, A Notchenko, F Mao, A Artemov, D Zorin, Evgeny Burnaev. Making DensePose fast and light. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2021), 1869-1877, 2021
- Korotin A. A., Vyugin V.V., Burnaev E.V. Online algorithm for aggregating experts’ predictions with unbounded quadratic loss. Russian Mathematical Surveys, 75 (5):974, 2020
- Barabanau, I.; Artemov, A.; Burnaev, E. and Murashkin, V. (2020). Monocular 3D Object Detection via Geometric Reasoning on Keypoints. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – Volume 5: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 652-659.
- Alanov, A.; Kochurov, M.; Volkhonskiy, D.; Yashkov, D.; Burnaev, E. and Vetrov, D. (2020). User-controllable Multi-texture Synthesis with Generative Adversarial Networks. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – Volume 4: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 214-221.
- Egiazarian, V.; Ignatyev, S.; Artemov, A.; Voynov, O.; Kravchenko, A.; Zheng, Y.; Velho, L. and Burnaev, E. (2020). Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – Volume 4: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 421-428.
- Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva, Denis Zorin, Evgeny Burnaev. Deep Vectorization of Technical Drawings. ECCV 2020
- Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov, Savva Ignatyev, Matthias Niessner, Denis Zorin, Evgeny Burnaev. CAD-Deform: Deformable Fitting of CAD Models to 3D Scans. ECCV 2020
- Ivan Nazarov, Evgeny Burnaev. Bayesian Sparsification of Deep C-valued networks. In Proceedings of International Conference on Machine Learning (ICML), 2020
- Y. Kapushev, I. Oseledets, E. Burnaev. Tensor Completion via Gaussian Process based Initialization. SIAM Journal on Scientific Computing 42(6), A3812-A3824, Society for Industrial and Applied Mathematics, 2020
- Anton D. Morozov, Dmitry O. Popkov, Victor M. Duplyakov, Renata F. Mutalova, Andrei A. Osiptsov, Albert L. Vainshtein, Evgeny V. Burnaev, Egor V. Shel, Grigory V. Paderin. Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast. Journal of Petroleum Science and Engineering. Volume 194, November 2020, 107504
- E. Romanenkova, A. Zaytsev, N. Klyuchnikov, A. Gruzdev, K. Antipina, L. Ismailova, E. Burnaev, A. Semenikhin, V. Koryabkin, I. Simon, D. Koroteev. Real-time data-driven detection of the rock type alteration during a directional drilling. IEEE Geoscience and Remote Sensing Letters, Vol. 17, Issue 11, p. 1861-1865, 2020
- A.S. Smirnov, T.V. Melnikova-Pitskhelauri, M.G. Sharaev, V.Yu. Zhukov, E.L. Pogosbekyan, R.M. Afandiev, A.A. Bozhenko, V.E. Yarkin, I.V. Chekhonin, S.B. Buklina, A.E. Bykanov, A.A. Ogurtsova, A.S. Kulikov, A.V. Bernshtein, E.V. Burnaev, D.I. Pitskhelauri, I.N. Pronin. Resting-state fMRI in preoperative non-invasive mapping in patients with left hemisphere glioma. Burdenko’s journal of neurosurgery 2020, No4, pp. 17-25.
- A. Korotin, V. Vyugin, E. Burnaev. Adaptive Hedging under Delayed Feedback. Neurocomputing, 2019
- N. Klyuchnikov, E. Burnaev. Gaussian process Classification for Variable Fidelity Data. Neurocomputing, 2019
- I. Makhotin, E. Burnaev, D. Koroteev. Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing. Journal of Petroleum Exploration and Production Technology, pp. 1-7, 2019
- O. Sudakov, E. Burnaev, D. Koroteev. Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks. Computers and Geosciences, Volume 127, June 2019, Pages 91-98
- E. Kanin, A. Osiptsov, A. Vainshtein, E. Burnaev. A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data. Journal of Petroleum Science and Engineering, 2019
- P. Temirchev, M. Simonov, R. Kostoev, E. Burnaev, I. Oseledets, A. Akhmetov, A. Margarit, A. Sitnikov, D. Koroteev. Deep Neural Networks Predicting Oil Movement in a Development Unit. J. Petrol. Science and Engineering, 2019
- N. Klyuchnikov, A. Zaytsev, A. Gruzdev, G. Ovchinnikov, K. Antipova, L. Ismailova, E. Muraleva, E. Burnaev, A. Semenikhin, A. Cherepanov, V. Koryabkin, I. Simon, A. Tsurgan, F. Krasnov, D. Koroteev. Data-driven model for the identification of the rock type at a drilling bit. Journal of Petroleum science and Engineering, vol. 178, p. 506-516, 2019
- A. Kuzina, E. Egorov, E. Burnaev. Bayesian generative models for knowledge transfer in MRI semantic segmentation problems. Journal: Frontiers in Neuroscience, section Brain Imaging Methods, 2019
- N. Khromov, A. Korotin, A. Lange, A. Stepanov, E. Burnaev, A. Somov. Esports Athletes and Players: a Comparative Study. IEEE Pervasive Computing, 2019
- ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky. Latent Convolutional Models. In ICLR 2019: Proceedings of the International Conference on Learning Representations, 2019
- S. Koch, A. Matveev, Z. Jiang, F. Williams, A. Artemov, E. Burnaev, M. Alexa, D. Zorin, D. Panozzo. ABC: A Big CAD Model Dataset for Geometric Deep Learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
- Voinov, O., Artemov, A., Egiazarian, V., Notchenko, A., Bobrovskikh, G., Zorin, D., & Burnaev, E. Perceptual deep depth super-resolution. ICCV, 2019
- S. Ivanov, E. Burnaev. Anonymous walk embeddings. In Proceedings of International Conference on Machine Learning (ICML), 2018
- M. Munkhoeva, E. Kapushev, E. Burnaev, I. Oseledets. Quadrature based features for kernel approximation. Proceedings of NIPS, Spotlight talk, 2018
- E. Burnaev, A. Cichocki, V. Osin. “Fast Multispectral Deep Fusion Networks”, Bull. Pol. Ac.: Tech. 66(4), pp. 875-880 (2018)
- M. Pominova, A. Artemov, M. Sharaev, E. Kondrateva, E. Burnaev, A. Bernstein. Voxelwise 3D Convolutional and Recurrent Neural Networks for Epilepsy and Depression Diagnostics from Structural and Functional MRI Data. Proc. of IEEE International Conference on Data Mining Workshops (ICDMW), p. 299-307, 2018
- S. Ivanov, M. Sharaev, A. Artemov, A. Cichocki, E. Kondratyeva, S. Sushchinskaya, E. Burnaev, A. Bernstein. Learning Connectivity Patterns via Graph Kernels for fMRI-based Depression Diagnostics. Proc. of IEEE International Conference on Data Mining Workshops (ICDMW), p. 308-314, 2018
- Burnaev E., Zaytsev A. Minimax approach to variable fidelity data interpolation. Proceedings of Machine Learning Research 54:652-661, Volume 54: Artificial Intelligence and Statistics, 20-22 April 2017, Fort Lauderdale, FL, USA
- Burnaev E., Zaytsev A. Large Scale Variable Fidelity Surrogate Modeling. Ann Math Artif Intell (2017), pp. 1-20. doi:10.1007/s10472-017-9545-
- Burnaev E., Panin I., Sudret B. Effecient Design of Experiments for Sensitivity Analysis based on Polynomial Chaos Expansions. Ann Math Artif Intell (2017). doi:10.1007/s10472-017-9542-1
- Burnaev E.V., Golubev G.K. On one problem in Multichannel Signal Detection. Problems of Information Transmission, October 2017, Volume 53, Issue 4, pp 368–380
- A. Artemov, E. Burnaev. Optimal estimation of a signal perturbed by a fractional Brownian noise. Theory of Probability and Its Applications, 2016, vol. 60, No. 1, pp. 126-134
- E. Burnaev, M. Belyaev, E. Kapushev. Computationally efficient algorithm for Gaussian Process regression in case of structured samples. Computational Mathematics and Mathematical Physics, 2016, Vol. 56, No. 4, pp. 499–513, 2016
- Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel Prikhodko, Dmitry Vetrov, Dmitry Yarotsky. GTApprox: Surrogate modeling for industrial design. Advances in Engineering Software 102 (2016) 29–39
- Sergey A. Evfratov, Ilya A. Osterman, Ekaterina S. Komarova, Alexandra M. Pogorelskaya, Maria P. Rubtsova, Timofei S. Zatsepin, Tatiana A. Semashko, Elena S. Kostryukova, Andrey A. Mironov, Evgeny Burnaev, Ekaterina Krymova, Mikhail S. Gelfand, Vadim M. Govorun, Alexey A. Bogdanov, Petr V. Sergiev and Olga A. Dontsova. Application of sorting and next generation sequencing to study 5′ – UTR influence on translation efficiency in Escherichia coli. Nucleic Acids Research, 2016, 16 P. doi: 10.1093/nar/gkw1141
- E. Burnaev, V. Vovk. Efficiency of conformalized ridge regression. Proc. of COLT. JMLR W&CP 35:605-622, 2014
- Grihon S., Burnaev E.V., Belyaev M.G. and Prikhodko P.V. Surrogate Modeling of Stability Constraints for Optimization of Composite Structures. Surrogate-Based Modeling and Optimization. Engineering applications. Eds. by S. Koziel, L. Leifsson. Springer, 2013. P. 359-391
- E. Burnaev, A. Zaytsev, V. Spokoiny. The Bernstein-von Mises theorem for regression based on Gaussian processes. Russ. Math. Surv. 68, No. 5, 954-956 (2013)
- E. Burnaev, P. Prikhod’ko. On a method for constructing ensembles of regression models. Automation and Remote Control, Volume 74, Issue 10, pp. 1630-1644, 12 Oct 2013
- A. Zaitsev, E. Burnaev, V. Spokoiny. Properties of the posterior distribution of a regression model based on Gaussian random fields. Automation and Remote Control, Volume 74, Issue 10, pp. 1645-1655, 12 Oct 2013
- E. Burnaev. Disorder Problem for Poisson Process in Generalized Bayesian Setting. Theory Probab. Appl., 53(3), p. 500–518, 2009
- E. Burnaev, E. Feinberg, A. Shiryaev. On Asymptotic Optimality of the Second Order in the Minimax Quickest Detection Problem of Drift Change for Brownian Motion. Theory Probab. Appl., 53(3), 519–536, 2009
- E. Burnaev. Disorder problem for a Poisson process in the generalized Bayesian setting. Russian Mathematical Surveys (2007), 62(4):790
- E. Burnaev. Application of wavelet bases in linear and nonlinear approximation to functions from Besov spaces. Computational Mathematics and Mathematical Physics. December 2006, Volume 46, Issue 12, pp. 2051-2060
- E. Burnaev. Inversion formula for infinitely divisible distributions. Russian Mathematical Surveys (2006), 61(4):772

- Excellence Recognition Award from Skoltech president, 2022
- Best cooperation project leader award from Huawei, 2021
- Best Paper Award for the research on modeling of point clouds and predicting properties of 3D shapes at the Int. Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), 2020
- Ilya Segalovich Yandex Science Prize (“The best research director of postgraduate students in the field of computer sciences”), 2020
- Geometry Processing Dataset Award for the work “ABC Dataset: A Big CAD Model Dataset For Geometric Deep Learning”, Symposium on Geometry Processing, 2019
- Best Paper Award for the research in eSports at the IEEE Internet of People conference, 2019
- Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading the project “The development of methods for predictive analytics for processing industrial, biomedical and financial data”, 2017

- Machine Learning, an obligatory course for the 1st year Master students in Data Science
- Bayesian methods of Machine Learning, an elective course for the 1st/2nd year Master students in Data Science
- Foundations of Data Science, an elective course for Ph.D. students

ФИО: Бурнаев Евгений Владимирович

Занимаемая должность: профессор

Преподаваемые дисциплины: Машинное обучение, Байесовские методы машинного обучения, Основы наук о данных

Ученая степень:

- Кандидат физико-математических наук, 2008, Институт проблем передачи информации им. А.А. Харкевича РАН (05.13.17 – теоретические основы информатики)
- Доктор физико-математических наук, 2022, Московский физико-техничесий институт (05.13.18 – Мат. моделирование, численные методы и комплексы программ)

Ученое звание (при наличии): Доцент

Общий стаж работы: более 18 лет

Стаж работы по специальности: более 18 лет