Included Themes

Applications of data science (DS), machine learning (ML), and artificial intelligence (AI) in all scientific disciplines
Approximation theory
Computational linear algebra and tensor methods
Graphs, network science, and discrete structures
High dimensional geometry and topology of data
Interpretability, fairness, explainability of data-driven models
Mathematics of AI and ML
Operator Learning
Optimization and Control
Parallel and high-performance computing
Randomized algorithms
Software, reproducibility & data ecosystems
Statistical learning theory
Uncertainty and probabilistic modeling

Focus Topics

Dimensionality reduction and embeddings
Emergent properties of AI models
Generative AI (theory and applications)
Geometric and topological data analysis
Graph neural networks
Inverse problems
Privacy/interpretability/explainability/ethics/policy of AI, ML, and DS
Parallel/distributed/scalable optimization
Probabilistic graphical models
Uncertainty quantification