dennis nieman

about

I'm a postdoctoral researcher in mathematical statistics and machine learning at Humboldt-Universität Berlin.

I work on Bayesian asymptotics, Gaussian processes, variational autoencoders, social choice theory, Lévy process queueing models.

Nieman, D., Szabó, B. (2025+). Adaptive sparse variational approximations for Gaussian process regression. ArXiV preprint.
Nieman, D., Mandjes, M., and Ravner, L. (2025). Input estimation from discrete workload observations in a Lévy-driven storage system. Statistics & Probability Letters 216.
Nieman, D., Szabo, B., and van Zanten, H. (2023). Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression. Electronic Journal of Statistics 17(2):2250-2288.
Nieman, D., Szabo, B., and van Zanten, H. (2022). Contraction rates for sparse variational approximations in Gaussian process regression. Journal of Machine Learning Research 23(205):1-26.

PhD from Vrije Universiteit Amsterdam (2025)

thesis: Asymptotics for variational Gaussian processes
MSc Mathematics at University of Amsterdam (2020) — cum laude

thesis: Input estimation in a discretely observed Lévy-driven storage system
BSc Mathematics at University of Groningen (2018)

thesis: Brownian motion as a weak limit of probability measures

Adaptation for variational Gaussian processes in a contributed session at CFE-CMStatistics 2024
Asymptotics for variational Bayes in a contributed session at Bernoulli-IMS 2024
Contraction rates for variational Bayes in GP regression at EcoSta 2021

Adaptive variational inference for Gaussian processes
short talk at BAYSM 2025
Variational Bayes with inducing points
short talk at conference 'Approximation Methods in Bayesian Analysis', 2023
Frequentist guarantees of variational Bayes in GP regression
poster at ISBA 2022
poster at j-ISBA 2022
contributed talk at BNP Cyprus 2022
Contraction rates for variational Bayes in GP regression
poster at Dutch stochastics meeting 2021
poster at CIRM winter school 'End-to-end Bayesian learning' 2021
contributed talk at ISBA 2021

Project supervision for Mathematical modelling of stochastic systems (VU 2023—2024)
Lecturer Statistics for MNW (VU 2023)
Lecturer and TA for Statistical models (VU 2020—2023)
TA for Experimental design and data analysis (VU 2021—2022)
TA for Probability and statistics (2021—2022) at Amsterdam University College
TA at University of Amsterdam (2018—2020)
TA at University of Groningen (2015—2018)