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Ph.D. candidate in Statistics at TU Dortmund, working under the supervision of Prof. Dr. Katja Ickstadt and Dr. Alexander Munteanu. Currently working as a Scientific Researcher at Lamarr Institute for Machine Learning and AI, one of Germany's top AI research centers.
- Bayesian Statistics
- Machine Learning
- Data Reduction Approaches
- High-Dimensional Complex Models
- Statistical Computing
I have been involved in teaching the following courses at TU Dortmund:
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Monte Carlo Simulation Methods
Advanced statistical computing techniques and simulation methods -
Statistical Learning and Big Data Analysis
Machine learning algorithms and big data processing -
Bayesian Statistics
Theoretical foundations and practical applications -
Case Studies in Statistics
Real-world statistical analysis and problem solving
- Advanced: Python, R
- Proficient: SAS, Julia, SQL
- Intermediate: VBA, LaTeX, PySpark, PyTorch
- Bayesian Methods: MCMC, Prior Design, Model Selection
- Machine Learning: Gradient Boosting, Random Forests, SVMs
- Deep Learning: Neural Networks, RNN, LSTM, Bayesian Neural Networks
- Statistical Modeling: GLM, Time Series (ARIMA, GARCH, GJR-GARCH)
- Version Control: Git
- IDEs: PyCharm, RStudio
- Big Data: Hadoop, Spark
- Cloud Computing: AWS
Scalable Bayesian p-Generalized Probit and Logistic Regression
Advances in Data Analysis and Classification (2024)
Zeyu Ding, Katja Ickstadt, Alexander Munteanu, Simon Omlor
Bayesian analysis for dimensionality and complexity reduction
Machine Learning under Resource Constraints (2023)
Zeyu Ding, Katja Ickstadt, Alexander Munteanu
- Email: zeyu.ding@tu-dortmund.de
- GitHub: zeyudsai
- LinkedIn: Zeyu Ding