PAULIN Daniel

PAULIN Daniel is an Associate Professor at Nanyang Technological University, School of Physical and Mathematical Sciences, Department of Mathematics. His research interests include Monte Carlo simulation methods, Bayesian statistics, diffusion models, uncertainty quantification, and applied probability. He holds a PhD from National University of Singapore and is based in Office N4-02c-117. His profile can be accessed through the department's faculty page.

Daniel Paulin is an Associate Professor at Nanyang Technological University, specializing in Bayesian computation, applied probability, machine learning and optimization, and data assimilation. His research focuses on developing efficient sampling methods for complex distributions, including Markov chain Monte Carlo (MCMC) techniques with dimension-free convergence rates. He also explores stochastic gradient methods and their applications in Bayesian inference and optimization. His work addresses challenges in high-dimensional and multimodal settings, with applications in statistical learning and data assimilation for dynamical systems. Paulin's contributions include theoretical advances in concentration inequalities, Ricci curvature for mixing properties, and randomized algorithms for improved computational efficiency.