Public talks + papers

Here is the long list with both public talks, lectures, and papers mixed together.

2024
  • Thulke D., Gao Y., Pelser P., Brune R., Jalota R., Fok F., Ramos M., van Wyk I., Nasir A., Goldstein H., Tragemann T., Nguyen K., Fowler A., Stanco A., Gabriel J., Taylor J., Moro D., Tsymbalov E., de Waal J., Matusov E., Yaghi M., Shihadah M., Ney H., Dugast C., Dotan J., Erasmus D. ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change // arXiv preprint arXiv:2401.09646, January 2024
2022
  • Tsymbalov E. Machine Learning in Weather Forecasting // Summer IT School, Tomsk, Russia, July 2022
2021
  • Tsymbalov E. Artificial Neural Networks: Applications and Challenges // Summer IT School, Tomsk, Russia, July 2021
  • Tsymbalov E. Uncertainty Estimation and Decision-Making: Metrics, Methods, Applicability // OpenTalks.AI 2021, Moscow, Russia, February 5, 2021
2020
  • Tsymbalov E., Fedyanin K., Panov M. Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles // OpenTalks.AI 2020, Moscow, Russia, February 21, 2020.
2019
  • Tsymbalov E., Shapeev A., Panov M. Gaussian Processes and Decorrelation Masks: a Way to Enhance Dropout Uncertainty Estimate // Machine Learning Summer School, Skoltech, Moscow, Russia, August 26 – September 9, 2019
  • Tsymbalov E., Shi Z., Dao M., Suresh S., Li J., Shapeev A. Elastic Strain Engineering of Diamond: Tracking Treasure Down // Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019
  • Tsymbalov E., Makarychev S., Panov M., Shapeev A. Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning Learning // In Proceedings of the 28th International Joint Conference on Artificial Intelligence. Main track, 3599-3605. Macao, China, 2019 (https://www.ijcai.org/proceedings/2019/499)
  • Tsymbalov E., Makarychev S., Panov M., Shapeev A. Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning // 36th International Conference on Machine Learning Workshop on Uncertainty & Robustness in Deep Learning, Long Beach, California, USA, June 14, 2019 (remote)
  • Tsymbalov E., Shi Z., Dao M., Suresh S., Shapeev A., & Li J. Neural networks for elastic strain engineering // “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
  • Itkin I., Gromova A., Sitnikov A., Legchikov D., Tsymbalov E., Yavorskiy R., Novikov A., Rudakov K. User-assisted log analysis for quality control of distributed fintech applications // In 2019 IEEE International Conference On Artificial Intelligence Testing (AITest) 2019 Apr 4 (pp. 45-51). IEEE.-
  • Shi Z., Tsymbalov E., Dao M., Suresh S., Shapeev A., & Li J. (2019). Deep elastic strain engineering of bandgap through machine learning // Proceedings of the National Academy of Sciences, 116(10), 4117-4122.
2018
  • Tsymbalov E., Shi Z., Shapeev A., Li J. Deep Elastic Strain Engineering for Optimization and Exploration of Semiconductors Electronic Properties // III International Workshop on Electromagnetic Properties of Novel Materials, Skoltech, Moscow, Russia, December 18-20, 2018.
  • Shi Z., Tsymbalov E., Shapeev A., Li J. Elastic Strain Engineering Reaches Six Dimensions via Machine Learning // 2018 Materials Research Society Fall Meeting, MIT, Boston, US, November 27, 2018.
  • Gordin V., Tsymbalov E. Compact difference scheme for parabolic and Schrodinger-type equations with variable coefficients //  Journal of Computational Physics, 375: 1451-1468, 2018.
  • Tsymbalov E., Ushakov R., Shapeev A., Panov M.Deep Active Learning: Gaussian Processes to the Rescue! //  3rd Annual MIT-Skoltech Conference: “Collaborative Solutions for Next Generation Education, Science and Technology”, Moscow, Russia, October 15-16, 2018.
  • Tsymbalov E., Shi Z., Shapeev A., Li J. Machine Learning Elastic Strain Engineering // In 3rd Annual MIT-Skoltech Conference: “Collaborative Solutions for Next Generation Education, Science and Technology”, Moscow, Russia, October 15-16, 2018.
  • Tsymbalov, E., Panov, M., Shapeev, A. Dropout-based Active Learning for Regression // In International Conference on Analysis of Images, Social Networks and Texts (pp. 247-258),Springer, Cham., Moscow, Russia, July 5-7, 2018.
  • Tsymbalov E., Panov M., Shapeev A. Dropout-based Active Learning for Regression // 7th Symposium On Conformal & Probabilistic Prediction With Applications (COPA 2018), June 11-13, 2018, Maastricht, Netherlands.
  • Gordin V., Tsymbalov E. 4-th order difference scheme for differential equation with variable coefficients //  Mathematical Models and Computer Simulations, vol. 10, no. 1, pp. 79 – 88, 2018.
2017
  • Gordin V, Tsymbalov E. Compact difference schemes for weakly-nonlinear parabolic and Schrodinger-type equations and systems // arXiv preprint arXiv:1712.05185. December 14, 2017.
  • Tsymbalov E. Compact high-order difference approximations for rod lateral vibrations equation // International Conference on Computer Simulation in Physics and beyond, October 9-12, 2017, Moscow, Russia
  • Tsymbalov E., Shapeev A. Surrogate modelling of Si and Ge electronic properties under elastic strain // International Conference on Computer Simulation in Physics and beyond, October 9-12, 2017, Moscow, Russia
  • Tsymbalov E., Shi Z., Shapeev A., Li J. Material strain optimization meets machine learning // Gen-Y: Skoltech Young Scientist Cross-Disciplinary Conference, September 27 – October 1, 2017, Sochi, Russia
  • Tsymbalov E., Baymurzina D., Shapeev A. Machine learning strain engineering // Shaping the Future: Big Data, Biomedicine and Frontier Technologies, April 25-26, Skolkovo Innovation Center, Moscow
  • Tsymbalov E. 4th Order Difference Scheme for Diffusion Equation with Variable Coefficient// Interuniversity scientific conference of students, postgraduates and young specialists MIEM NRU HSE, Moscow.  – P. 8. (in Russian)
  • Gordin V., Tsymbalov E. Compact Difference Scheme for the Differential Equation with Piecewise-Constant Coefficient //  Mathematical Models and Computer Simulations, vol. 27, no. 12, pp. 16–28. (in Russian)
2016
  • Gordin V., Tsymbalov E.4th Order Difference Scheme for Differential Equation with Variable Coefficients  // Proc. of XXI All-Russian Conference “Theoretical Foundations and Construction of Numerical Algorithms for Problems of Mathematical Physics”, Abrau-Durso, September 5-11th (in Russian)
  • Tsymbalov E. Churn Prediction for Game Industry Based on Cohort Classification Ensemble  // Proc. of 3rd International Workshop on Experimental Economics and Machine Learning, Moscow, July 18, 2016, CEUR-WS.org, http://ceur-ws.org/Vol-1627/paper8.pdf
  • Tsymbalov E.Game Analytics: Machine Learning to the Rescue!  // Analysis of Images, Social Networks, and Texts Conference, April 7-9th, Yekaterinburg
  • Tsymbalov E.Compact difference approximations for Sturm-Liouville Problem  // Proceedings of the First Student Conference of Computer Science Faculty, NRU HSE, Moscow. P. 26 (in Russian)
  • Tsymbalov E.Multistage Model for LTV Prediction for Free-to-Play Games on Social and Mobile Platforms  // Young Scientist Symposium on Applied Data Analysis, Saint-Petersburg
  • Gorshkov I., Tsymbalov E., Vlasova E., Yavorskiy R. Monitoring and Analysis of Online Community of a University  // Young Scientist Symposium on Applied Data Analysis, Saint-Petersburg
  • Tsymbalov E. Compact Difference Schemes for Sturm-Liouville Problem  // Interuniversity scientific conference of students, postgraduates and young specialists MIEM NRU HSE, Moscow.  – P. 44. (in Russian)
2015
  • Gordin V., Tsymbalov E. Compact Difference Schemes for Rod Lateral Vibrations Equation  // Proceedings of Fourth China-Russia Conference on Numerical Algebra with Applications. – Rostov-on-Don: Southern Federal University Publishing. – P. 110
2014
  • Afanasyev A., Pekov Yu., Tsymbalov E., Vlasova E., Yavorskiy R. Genomics in Russia: education, science, business // Skolkovo Almanac.
  • Gordin V., Tsymbalov E. Compact Difference Schemes for Problems of Mathematical Physics  // 10th AIMS International Conference on Dynamical Systems, Differential Equations and Applications. Abstracts, p. 520.
  • Gordin V., Tsymbalov E. Compact differential schemes for the diffusion and Schrodinger equations. Approximation, stability, convergence, effectiveness, monotony // Journal of Computational Mathematics. Vol. 32, 348-370.
  • Tsymbalov E.Compact Difference Schemes for Evolutionary Equations of Mathematical Physics  // Scientific conference of students and young specialists MIEM NRU HSE. Abstracts, p. 9. (in Russian)
2013
  • Gordin V., Tsymbalov E. Compact Difference Schemes for the diffusion and Schrodinger equations  // Abstracts of XVI All-Russian Conference of Young Scientists “Modern Problems of Mathematical Modeling” (in Russian)
  • Gordin V., Tsymbalov E. Compact Difference Schemes for Linear Problems of Mathematical Physics  // Second China-Russia Conference on Numerical Algebra with Applications, Abstracts of Lecturers and Young Scientists. – Rostov-on-Don: Southern Federal University Publishing. – P. 132
  • Tsymbalov E. Compact Difference Schemes for PDE Solving  // Scientific conference of students and young specialists MIEM NRU HSE. Abstracts, p. 5. (in Russian)