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Yurou earned her Bachelor’s degree in Mathematics in Business and Economics from the University of Mannheim, and later completed her Master’s degree in Mathematics with a concentration in Statistics and Applied Mathematics at the Technical University of Munich (TUM).
She is currently working towards her PhD under the guidance of Prof. Mathias Drton in the Mathematical Statistics group at TUM. Her primary research areas are Graphical Models and Causal Discovery.
relAI Research
Learning and Leveraging Causal Models
Causal discovery has broad applications across fields like biology, finance, and robotics. My research focuses on learning causal structures using graphical models, particularly in approximating causal relationships between random variables. While much of the existing research assumes linear causal relationships, I aim to extend these methods to more complex, non-linear cases. In our recent publication (https://proceedings.mlr.press/v246/liang24a.html), we proposed a kernel-based method to approximate causal relationships under a continuous constraint that prevents causal feedback loops, demonstrating strong performance even on relatively small datasets. Additionally, we are exploring models that account for confounders—hidden variables that influence observed random variables. By addressing these challenges, our work seeks to uncover complex causal relationships, paving the way for more robust and realistic applications across various domains.