Sergey Dolgov

Sergey Dolgov is an Associate Professor in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing tensor decomposition methods for high-dimensional problems in numerical analysis, stochastic modeling, and quantum computing. Dr. Dolgov leads projects funded by the EPSRC including work on tensor methods for dynamic programming and Bayesian inverse problems. He is affiliated with the Centre for Mathematics and Algorithms for Data (MAD) and co-organizes workshops on computational mathematics for quantum technologies.

His research interests center on tensor networks, high-dimensional optimization, rare event simulation, and quantum algorithm design. Recent work applies tensor train decompositions to problems in epidemiology, fluid dynamics, and material science, significantly reducing computational complexity.

Dolgov's publications demonstrate consistent focus on tensor-based numerical methods for solving partial differential equations, epidemic modeling, and quantum computing applications. Key trends include the development of efficient algorithms for high-dimensional data and uncertainty quantification.

He leads the Tensor Computation Research Group and collaborates internationally on projects involving quantum-inspired numerical methods. Current work explores tensor networks for solving Navier-Stokes equations and optimizing neural network architectures.