Dr. Yurii Kolomoitsev serves as a Research Fellow at the Institute of Numerical and Applied Mathematics, University of Göttingen. His research focuses on approximation theory and sampling processes, with active involvement in multiple DFG-funded projects including "Spezielle Glattheitsmaße und Funktionenräume für Sampling und Interpolation" (2025-2027, project KO 5804/3-1) and the Research Training Group 2088 "Discovering structure in complex data" (2015-2024).

His primary research interests span approximation theory, sampling theory, function spaces, Fourier analysis, compressed sensing, and inverse problems. Kolomoitsev investigates fundamental properties of quasi-interpolation operators, moduli of smoothness, and Lebesgue constants, with significant contributions to multivariate approximation and high-dimensional computational methods. His work bridges theoretical analysis with practical applications in data science and signal processing.

Analysis of his 15 most recent publications (2020-2023) reveals consistent focus on asymptotic behavior in approximation processes, particularly examining error estimates in Lp spaces, sparse grid methods, and the interplay between function smoothness and approximation quality. Key trends include the extension of classical results to non-standard spaces (0

Dr. Kolomoitsev holds substantial research funding through the German Research Foundation (DFG), currently leading project KO 5804/3-1 and participating in RTG 2088 until 2024. He actively contributes to the academic community through workshop organization including the annual RTG Workshop series and specialized events on inverse problems and clinical MRI data analysis. His teaching portfolio includes graduate-level courses such as "An Introduction to Compressed Sensing" (2023/24) and "Fourier Analysis and Approximation" (2022).