IAS Frontiers Conference on Geometry, Dynamics, and Learning (GDL2026)
Welcome to the IAS Frontiers Conference on Geometry, Dynamics, and Learning
The IAS Frontiers Conference on Geometry, Dynamics, and Learning will bring together an international cohort of leading researchers, early-career investigators, and promising graduate students working at the interface of geometric analysis, dynamical systems, and machine learning. Over the past decade, a convergence of ideas from pure mathematics, data science, and theoretical computer science has catalysed a new research frontier in which tools from differential geometry and dynamical systems theory inform the design, analysis, and interpretability of modern learning algorithms, and, conversely, insights from learning are reshaping foundational questions in geometry and dynamics.
The primary goals of this workshop are to (1) cultivate new intellectual bridges between geometry, dynamical systems, and learning theory; (2) identify shared research challenges and unify disparate methodological frameworks; and (3) accelerate collaborative projects that leverage mathematical rigor to address practical problems in data-driven science and engineering.
Researchers who may be interested in delivering a 30-minute talk at the conference should send the title and abstract of their proposed talk together with a brief biography to [email protected].
Conference Themes:
- Geometric optimization
- Geometric and topological data analysis
- Geometric deep learning
- Shape analysis
- Information geometry
- Optimal transport
- Dissipative dynamics
- Geometric and variational integration
- Geometric methods in dynamical systems
- Infinite-dimensional dynamical systems
- Quantum mechanics and quantum information theory
Organising Committee:
- Cyrus Mostajeran (NTU)
- Juan-Pablo Ortega (NTU)
- Frank Nielsen (Sony)
- Frederic Barbaresco (Thales)
- Kelin Xia (NTU)
- Francois Gay-Balmaz (NTU)
- Florian Rossmannek (NTU)
- Jeremie Houssineau (NTU)
*The organisers may adjust the programme or reschedule talks without prior notice. We thank you for your understanding.