My research is intensely focused on enhancing the expressiveness and explainability of Graph Neural Networks (GNNs), particularly by addressing the limitations posed by the Weisfeiler-Leman isomorphism test. This work involves developing innovative mathematical models and software systems that can more accurately interpret and analyze complex data structures found in diverse domains such as social networks, molecular structures, financial systems, and transportation networks.In my professional capacity, I am an experienced software engineer in the finance industry, employing my deep expertise in C++ and Python to develop fast, efficient software solutions that are crucial for high-frequency trading operations.My current efforts are geared towards the technical advancement of GNNs, aiming to refine their performance and applicability, especially in tackling quantitative challenges in AI finance.While I have previously worked on projects within Human-Centered Computing, my primary focus now lies in the technical realms of machine learning, where I also engage with robust algorithms, causality, and statistical learning theory.