Mathematical Modeler | Senior NLP & AI Engineer | Stochastic Analysis Enthusiast

I am an M.Sc. candidate in Mathematical Modeling at the University of Luxembourg, combining a rigorous mathematical background with 7+ years of industry experience building production-ready Artificial Intelligence, NLP, and Data pipelines. I specialize in bridging the gap between deep theoretical mathematics and practical, real-world machine learning applications.

• Master's Thesis: Conducting stochastic analysis on the Rosenblatt Process, exploring long-range dependence and Malliavin calculus.
• Research Interests: Intersection of ML in Science & Engineering, Physics-Informed ML, Diffusion Models, and Graph Signal Processing.

• Languages & Frameworks: Python, PyTorch, SQL, R, Julia, Docker, Triton, CUDA.
• AI / Machine Learning: Natural Language Processing (NLP), Knowledge Graphs, Generative AI (LLMs, Speech Synthesis), AutoML, Probabilistic Modeling.
• Mathematics: Stochastic Analysis, Spectral Theory, Graph Signal Processing, Low-Rank Approximation.
• Infrastructure: High-Performance Computing (HPC), CI/CD, Data Pipeline Engineering.

Before returning to academia, I spent over 7 years in the industry as a (Senior) Algorithm Engineer across the healthcare, education, and finance sectors in Beijing. I have led teams to build everything from adaptive education platforms to virtual human customer service systems and financial risk prediction models.

I am currently seeking PhD positions or research-oriented roles where I can apply my mathematical background to solve complex data and AI challenges in the natural sciences, healthcare, or robust ML systems.