Aleksandr Khizhik

Responsibilities

Conducting research and preparing materials for publication.

Education

2022

Master's in Applied Mathematics and Information Science
HSE University

Professional Interests

Deep Learning AI Agents anomaly detection machine learning

Postgraduate Studies

1st year of study
Approved topic of thesis: Enhancing Industrial Anomaly Detection through Advanced ML-Based Fault Simulation and Diagnosis

Courses (2026/2027)

Deep Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Eng
Past Courses

Timetable for today

Full timetable

Conferences

2025
XXVI Харитоновские тематические научные чтения (Саров). Presentation: Использование механизмов образования аномалий для генерации синтетических данных и подготовки моделей машинного обучения к диагностике неисправностей в технических системах

Employment history

Reseacher with expertise in AI.
Strong research background in ML, DL, AI agents, and scientific computing. Proven ability to optimize complex systems and drive data-driven innovation.

25

May

2026

HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors

Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.