Data compression methods of generalized linear model
Regression approaches for large-scale high-dimensional data
Bayesian Statistics, particular in Markov chain Monte Carlo
Bayesian Neural Network
Distributional Metrics and its Application in Generative Models
Publikationen
Ding, Z., Ickstadt, K., Klein, N., Munteanu, A., Omlor, S. (2026). Scalable Learning of Multivariate Distributions via Coresets. In: Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS); Tanger, Marokko, 02.05.2026.
Ding, Z., Grunwald, C., Ickstadt, K., Kröninger, K., & La Cagnina, S. (2025). Mcbench: A benchmark suite for monte carlo sampling algorithms. arXiv preprint arXiv:2501.03138.
Ding, Z., Ickstadt, K., Munteanu, A. (2023). Bayesian analysis for dimensionality and complexity reduction.Machine Learning under Resource Constraints, Volume 3 - Applications, pp. 58-70, 2023. ieeexplore.ieee.org/document/10789993
Ding, Z., Omlor, S., Ickstadt, K. et al. Scalable Bayesian p-generalized probit and logistic regression. Adv Data Anal Classif (2024). doi.org/10.1007/s11634-024-00599-1
Lebenslauf
2011-2015: Xi’an Jiaotong University, Bachelor of Science in Quantitative Economics
2017-2020: University of Göttingen, Master of Science in Quantitative Economics
2021-2026: Scientific researcher/PhD, Fakultät Statistik, LS Mathematische Statistik und biometrische Anwendungen, TU Dortmund
2026-Presetn: Post-Doc Researcher, Fakultät Statistik, LS Mathematische Statistik und biometrische Anwendungen, TU Dortmund
