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Поздняков Виталий Витальевич

Факультет компьютерных наук

Профиль на hse.ru ↗ тел.: 27320
Публикаций
6
Языков
1
Наград
2
Конференций
0
Профиль Публикации (6) Курсы (6)

Профессиональные интересы

анализ данных и искусственный интеллект

Должности

  • Младший научный сотрудник — Факультет компьютерных наук, Институт искусственного интеллекта и цифровых наук, Научно-учебная лаборатория методов анализа больших данных
  • Старший преподаватель — Факультет компьютерных наук, Департамент анализа данных и искусственного интеллекта
  • Приглашенный преподаватель — НИУ ВШЭ в Нижнем Новгороде, Факультет гуманитарных наук, Департамент фундаментальной и прикладной лингвистики

Био

  • · Начал работать в НИУ ВШЭ в 2021 году.

Образование

  • 2021 · Магистратура: Национальный исследовательский университет "Высшая школа экономики", специальность «Прикладная математика и информатика», квалификация «Магистр»
  • 2012 · Специалитет: Липецкий государственный педагогический университет, специальность «Информационные системы и технологии», квалификация «Инженер»

Опыт работы

  • · 2020: Работает в НИУ ВШЭ с года

Награды и поощрения

  • · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026)
  • · Лучший преподаватель — 2023

Идентификаторы исследователя

Публикации (6)

Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics

2024 · ARTICLE · en

Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We propose a novel Graph Neural Network architecture for optimizing task scheduling by representing the problem on a heterogeneous graph, where nodes denote tasks and locations. Our model minimizes both manufacturing and logistics costs, achieving significant performance improvements over greedy algorithms and comparable results to strong genetic algorithms in large-scale scenarios with up to 20 locations. This work advances the efficiency and flexibility of cloud manufacturing systems, offering practical solutions for dynamic, cost-sensitive environments.

DOI ↗ PDF ↗

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

2024 в печати · ARTICLE · en

Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for Fault Detection and Diagnosis (FDD) in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a new defense strategy based on combining adversarial training and data quantization. This research contributes several insights into securing machine learning within ACS, ensuring robust FDD in industrial processes.

DOI ↗

Graph Neural Networks With Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data

2024 в печати · ARTICLE · en

Timely detection and accurate diagnosis of faults in technological processes can significantly reduce production costs in manufacturing. Modern industrial equipment, equipped with numerous sensors, generates vast amounts of data, providing opportunities for advanced fault detection and diagnosis. While convolutional and recurrent neural networks have achieved state-of-the-art performance, they often overlook the correlations and hidden relationships among sensor signals. To address this, we propose a graph neural network (GNN) architecture that constructs graphs of sensor relationships from data. We evaluated five methods for training different types of adjacency matrices allowing to set certain restrictions on the structure of the graph. The resulting graph structures were analyzed and potential for their use in transfer learning was evaluated. Additionally, we developed an architecture that uses multiple adjacency matrices, which reduces the number of trainable parameters while maintaining high prediction quality. Our models demonstrated state-of-the-art performance on the Tennessee Eastman Process dataset, showcasing their potential for fault diagnosis on multivariate sensor data.

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AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes

2024 · CHAPTER · en

The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and also consider some useful applications. Our demo video is available at: http://tinyurl.com/3by9zcj5

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Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange

2024 в печати · CHAPTER · en

The ability to accurately simulate financial markets is crucial, as it allows researchers and practitioners to rigorously test and refine trading strategies without the high risks associ-ated with real-world experimentation. By leveraging Generative Adversarial Networks (GANs), this research aims to enhance the robustness and effectiveness of trading strategies by providing a controlled environment to assess potential outcomes and strategy resilience under varied market conditions. In this work, we propose the application of GAN s for simu-1ating multidimensional time series in the context of developing and testing trading strategies. We conduct an experimental study to jointly simulate the log-returns of several stocks on Moscow Exchange for Momentum effect evaluation. Compared to traditional methods such as bootstrapping, GANs can better model and interpolate the non-parametric complex nature of the data, providing an increased diverse sample size. This methodology could be beneficial for investors seeking new opportunities to test and tune hyperparameters of other trading strategies.

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SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

2023 · ARTICLE · en

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan.

DOI ↗

Курсы (6)