- Research Assistant, Postgraduate Student:Faculty of Computer Science / Laboratory of Methods for Big Data Analysis
- Maksim Karpov has been at HSE since 2017.
Continuing education / Professional retraining / Internships / Study abroad experience
Young Faculty Support Program (Group of Young Academic Professionals)
Category "New Researchers" (2019)
1st year of study
Approved topic of thesis: Detection and prediction of storage area network anomalies by analysing historical data
Academic Supervisor: Ustyuzhanin, Andrey
- Introduction to Data Analysis (Bachelor’s programme; Faculty of World Economy and International Affairs; 1 year, 4 module)Rus
Project Workshop for Developers, Educational Center «Sirius» (1-14 February 2019)
Mentoring a group of students in the first project workshop from Yandex, HSE and Sirius. The theme of the project: «Segmentation of images of city scenes for controlling self-driving vehicles».
Summer Workshop «Island 10-21», the Far Eastern Federal University (11-13 July 2018)
Crowd-science for solving big data processing problems. Increasingly, in business and science, we are faced with tasks that require data science expertise to solve them. Is it necessary to develop our own expertise in this area? Is it possible to attract external experts? Can I use the expertise of the data science community through platforms such as kaggle, coda lab or Yandex contest? How to formulate a problem in terms of clear and adequate solutions on these platforms. We will tell you about the ways to prepare tasks, features of the platforms and will go from the idea to the publication of the contest on one of them.
- Article M.E. Karpov, K. Arzymatov, V.S. Belavin, A.A. Sapronov, A.E. Ustyuzhanin, Nevolin A. Hybrid approach to design of storage attached network simulation systems // International Journal of Civil Engineering and Technology. 2018. Vol. 9. No. 11. P. 220-226.
- Article Vladislav B., Ustyuzhanin A., Arzymatov K., Karpov M., Сапронов А. А. Tuning hybrid distributed storage system digital twins by reinforcement learning // Advances in Systems Science and Applications. 2018. Vol. 18. No. 4. P. 1-12. doi