Шевелев Андрей Александрович
Факультет компьютерных наук
Профессиональные интересы
Должности
- Стажер-исследователь — Факультет компьютерных наук, Институт искусственного интеллекта и цифровых наук, Научно-учебная лаборатория методов анализа больших данных
Био
- · Начал работать в НИУ ВШЭ в 2018 году.
- · Научно-педагогический стаж: 12 лет.
Образование
- 2013 · Магистратура: Новосибирский национальный исследовательский государственный университет, специальность «Экономика», квалификация «Магистр»
- 2011 · Бакалавриат: Новосибирский национальный исследовательский государственный университет, специальность «Математика и информатика», квалификация «Бакалавр»
Опыт работы
- · 2018: curr., Junior Reseacher, High School of Economics
- · 2012: curr., Junior Reseacher, IEIE SB RAS
Идентификаторы исследователя
- Scopus AuthorID:
57219207352
Публикации (4)
An Approach to Finding a Robust Deep Learning Model
2025 · ARTICLE · en
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response to this need, we propose a novel approach for determining model robustness. This approach, supplemented with a model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. This study demonstrates the application of our approach to evaluate the robustness of deep learning models. To this end, we study small models composed of a few convolutional and fully connected layers, using common optimizers because of their ease of interpretation and computational efficiency. We address the influence of training sample size, model weight initialization, and inductive bias on the robustness of deep learning models.
Deep Learning Approaches for LHCb ECAL Reconstruction
2024 · ARTICLE · en
Calorimeters are a crucial component for most detectors mounted on modern colliders. Their tasks include identifying and measuring the energy of photons and neutral hadrons, recording energetic hadronic jets, and contributing to the identification of electrons, muons, and charged hadrons. To fulfill these many tasks while keeping costs reasonable, the calorimeter construction requires good and thoughtful balancing with other components of the detector. Much harder operation conditions during LHC’s high luminosity Run 5 and beyond bring new technological and computational challenges. This requires optimization of technologies, layouts, readouts, reconstruction algorithms to achieve the best overall physics performance for the limited cost. In the traditional approach, the reconstruction of the physical objects in the calorimeter must be matched to the calorimetric showers simulation used. We present a deep learning-based approach to help utilize raw simulated calorimetric data of varying degrees of detail.
Machine Learning in Calorimeter optimization
2021 · ARTICLE · en
The optimization of big industrial setups and the accompanying detailed simulations of internal physical processes require complex and time-consuming simulation calculations. We propose a versatile approach that can alleviate difficulties in solving this problem and show this using an example of electromagnetic calorimeter optimization at a Large Hadron Collider experiment. Our approach consists of a block representation of the calorimeter optimization process from setting sensitive characteristics of modules and their locations to obtaining a quality metric and applying machine learning methods. The main blocks are signal and background particles generation and their propagation to the calorimeter, the generation of electromagnetic showers of signal and noise in modules with a given technology, the combination of signal and noise with the simulation of different luminosities, the energy and spatial reconstruction of the signal and obtaining the final metric. This approach allows us to evaluate the operational characteristics of the calorimeter and find a more suitable configuration with the necessary quality without extensive use of time-consuming resources.
ML-assisted versatile approach to Calorimeter R&D
2020 · ARTICLE · en
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.
Курсы (0)
Нет курсов.