معرفی
Nikolas Nüsken is a Lecturer in Mathematical Data Science and Computational Mathematics at King’s College London’s Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD from Imperial College London (2018) and previously worked at the Alan Turing Institute and the University of Potsdam’s Collaborative Research Centre 'Scaling Cascades in Complex Systems'.
His research focuses on computational Bayesian inference, stochastic analysis, optimal control, and kernel methods. Key interests include interacting particle systems, optimal transport, and partial differential equations (PDEs). His work bridges theoretical mathematics with applications in machine learning and data science.
Recent research trends involve advancing Monte Carlo methods, Stein variational gradient descent, and tensor-based discretization schemes for solving complex PDEs and stochastic systems. His studies often intersect with robust filtering, deep learning for boundary value problems, and geometric analysis of optimization algorithms.
Nüsken’s contributions include 39 cited publications, with notable work on low-rank maximum likelihood estimation, robust regression for BSDEs, and controlled Monte Carlo diffusions. He collaborates extensively on topics like rough dynamics, ensemble Kalman filtering, and neural Schrödinger–Föllmer flows.
He is affiliated with King’s Probability and Statistics research groups, contributing to experimental design, MCMC methods, and probabilistic modeling. His work emphasizes interdisciplinary applications, blending mathematical rigor with computational innovation.
