Andi Han

Andi Han is a Lecturer in Data Science at The University of Sydney, School of Mathematics and Statistics. Their research areas include Mathematics and artificial intelligence, as well as computational statistics and machine learning. Han teaches courses such as STAT5002 and SNH4002, with associated workshops. Their personal web page provides additional information about their research and teaching activities. The profile page also includes details about their office location and contact information.

Andi Han is a researcher at the University of Sydney and RIKEN AIP, focusing on generative models, optimization, and geometry. Their work explores advanced mathematical frameworks for machine learning, particularly in Riemannian optimization and geometric deep learning. Han's research includes developing efficient methods for non-convex optimization on manifolds, improving variance reduction techniques, and analyzing the geometry of positive definite matrices. They have also contributed to diffusion models, graph neural networks, and probabilistic traffic forecasting. Their publications highlight the intersection of optimization theory, geometry, and machine learning, with an emphasis on scalable and robust algorithms.