Topics of interests include but are not limited to:

• Geometric Learning and Differential Invariants on Homogeneous Spaces
• Statistical Manifolds and Hessian information geometry
• Renyi Entropy & Information
• Geometric Foliation Structures of Dissipation and Machine Learning
• Geometric Structures of Quantum Information & Processing
• Applied Geometric Learning
• Probability, Information and Topology (fundamentals & applications)
• Divergences in Statistics and Machine Learning
• Geometric Statistics
• Geometric Methods in Hybrid Classical/Quantum Systems 
• Computational Information Geometry and Divergences
• Geometric Methods in Thermodynamics
• The Geometry of Classical & Quantum States
• Geometric Mechanics
• Geometric Green & Quantum Machine Learning
• Stochastic Geometric Dynamics
• New trends in Nonholonomic Systems
• Learning of Dynamic Processes
• Neurogeometry
• PINN (Physics-Informed Neural Network)  with Geometric Structures
• Lie Group in Learning Distributions
• Information Geometry, Toric Manifold (Delzant Theory)
• A symplectic approach to differential equations
• Lie Group Based Method in Robotics & Kalman Filters

• Geometric and Analytical Aspects of Quantization and Non-Commutative Harmonic Analysis on Lie Groups
• Probability and Statistics on manifolds

• Deep learning: Methods, Analysis and Applications to Mechanical Systems
• Integrable Systems and Information Geometry (From Classical to Quantum)
• Computing Geometry & Algebraic Statistics