💬 Invited Talks

• 2026.09: Riemannian Deep Learning: Algebraic and Geometric Approaches. IAS Frontiers Conference on Geometry, Dynamics, and Learning (GDL2026), Singapore.
• 2026.08: Riemannian Deep Learning: Foundations, Architectures, and Practice. Machine Learning Summer School (MLSS) 2026, Tübingen, Germany. [Code]
• 2026.07: Hyperbolic Deep Learning: Spaces, Networks, and Applications. 2026 International Workshop on Applied Geometry and Related Topics (IWAG 2026).
• 2025.10: Building Riemannian Deep Learning: Algebraic Approaches. PRCV 2025.
• 2025.06: Extending Normalization into Riemannian Manifolds. Jiangnan University.
• 2025.03: Riemannian Deep Learning: Normalization and Classification. University of Alberta.
• 2024.03: Naïve Riemannian Geometry: A One Hour Tour. Jiangnan University (internal talk).

📖 Courses

To build the mathematical foundations for my research, I have self-studied several mathematics courses, during my master’s, PhD, and MPI-IS visit:

• Mathematical Analysis I, II, III, Real Analysis, Complex Analysis, Functional Analysis;
• Advanced Algebra I, II, Abstract Algebra I;
• Topology, Differential Geometry, Differential Manifolds, Riemannian Geometry, Semi-Riemannian Geometry;
• Differential Equations, Convex Optimization, Numerical Optimization;
• Measure Theory, Statistical Optimal Transport, Optimal Transport, Reproducing Kernel Hilbert Spaces.

💻 Personal Channels

• Differential Equations (1k+ viewers)
• Topology (2k+ viewers)
• Differential Geometry (11k+ viewers)
• Riemannian Geometry (2k+ viewers)
• Measure Theory
• Statistical Optimal Transport (1k+ viewers)
• Reproducing Kernel Hilbert Spaces (1k+ viewers)
• Lectures on Optimal Transport