معرفی
Alexander Mikhailovich Shabalin is a Visiting Lecturer and Research Intern at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science, Institute of Artificial Intelligence and Digital Sciences, Center for Deep Learning and Bayesian Methods, and Department of Big Data and Information Retrieval. He began working at HSE in 2020 and is currently in his second year of postgraduate studies.
Shabalin holds a Master's degree (2023) and Bachelor's degree (2021) in Applied Mathematics and Computer Science from HSE. His approved dissertation topic is "Application of diffusion models to text data" under the supervision of Dmitry Petrovich Vetrov.
His primary research interests focus on Natural Language Processing and self-supervised learning, with particular emphasis on diffusion models for text data. His work bridges theoretical machine learning concepts with practical applications in text analysis and generation. The research trajectory evident in his publications shows a progression from foundational machine learning concepts toward more specialized applications of diffusion models in language processing.
Among his notable achievements are contributions to three significant Massive Open Online Courses (MOOCs) including "Modern Data Analysis: Machine Learning," "Fundamentals of Deep Learning," and "Advanced Deep Learning Methods," demonstrating his commitment to education and knowledge dissemination in artificial intelligence.
As an educator, Shabalin teaches "Deep Learning for Text Data" across multiple programs including Applied Mathematics and Computer Science and Economics. His teaching experience spans various faculties and demonstrates versatility in delivering technical content to diverse student populations.
His professional background includes research at the Center for Deep Learning and Bayesian Methods since 2021, along with previous development experience at Yandex.Cloud and Yandex ML-landing where he worked on infrastructure development and reinforcement learning applications for music recommendations.
