معرفی
Aleksandr Algasov is a Junior Researcher at the Computational Engineering department within the School of Engineering Sciences at Lappeenranta University of Technology. His work focuses on applying machine learning techniques to analyze and model X-ray absorption spectra, contributing to advancements in computational materials science and spectral analysis.
Research Interests: Aleksandr specializes in the intersection of machine learning and materials science, particularly in developing computational tools for X-ray spectroscopy. His research involves creating algorithms for spectral data interpretation, leveraging descriptor-based analysis and supervised learning methods to understand material properties at the atomic level.
Publications: His recent work includes studies on XANES spectral analysis, catalyst characterization, and the development of PyFitit software—a tool for quantitative X-ray absorption data processing using machine learning. These contributions reflect his focus on data-driven approaches in materials science.
