2026 SIAM MDS
SIAM Conference on Mathematics of Data Science (MDS26)
Organizing Committee Co-Chairs: A. Mang (University of Houston), R. Morrison (University of Colorado Boulder), and R. Willett (University of Chicago).
Salt Palace Convention Center, Salt Lake City, Utah, November 16–20, 2026
Conference webpage · Conference flyer
MDS26 is the conference of the SIAM Activity Group on Data Science. It brings together researchers and practitioners from academia, industry, government, and the national laboratories to explore advances in the mathematical foundations of data science. Presentations highlight advances in mathematical, statistical, and computational methods that shape how data are analyzed, modeled, and used to inform decision-making, spanning foundational theory through real-world applications. A particular focus of this edition is the mathematics of data science in high dimensions, with topics such as dimensionality reduction and embeddings, scalability and parallel algorithms, and algebraic and geometric data analysis.
MDS26 will be held jointly with the SIAM Conference on Imaging Science (IS26) and the SIAM International Conference on Data Mining (SDM26).
Included themes and focus topics
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, and 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
Please check the conference webpage for submission and registration deadlines. We invite you to join MDS26 to engage with emerging ideas, share insights, and help define the next generation of mathematics for data science.
2026 BIRS Workshop
Integrating Data- and Physics-Driven Methods for Decision Making under Uncertainty
Co-organized with R. White, L. L. R. Ramirez, and T. Bui-Thanh.
Casa Matemática Oaxaca, May 31–June 5, 2026
This five-day workshop brings together researchers from scientific computing, optimization, statistics, and machine learning to explore the integration of data-driven and physics-based methods for decision making under uncertainty. The aim is to foster new collaborations and identify open challenges at the intersection of these fields, with applications in science and engineering.
2025 CBMS AMML Conference
CBMS Conference: Research at the Interface of Applied Mathematics and Machine Learning
Co-organized with L. Cappanera, Y. He, and M. Wang.
University of Houston, December 8–12, 2025
NSF Award DMS-2430460
This NSF/CBMS Regional Research Conference focused on research at the interface of applied mathematics and machine learning. The principal lecturer was L. Ruthotto (Emory University), who delivered ten lectures organized into three modules: foundations of machine learning and deep neural networks; the applied mathematics underpinning them, including optimization and regularization; and applications of machine learning to inverse problems and high-dimensional partial differential equations. The lecture series was complemented by contributed talks, a poster session, and panel discussions with participants from academia and industry.
An article describing the conference can be found here: SIAM News Article
2025 ChAMELEON Summer School
Computational and mAthematical MEthods in machine LEarning, Optimization and iNference (ChAMELEON)
University of Houston, August 11–15, 2025
NSF Award DMS-2145845
The ChAMELEON summer school provides training in computational and mathematical methods in machine learning, optimization, and inference. It is designed for graduate students and early-career researchers seeking to deepen their understanding of the mathematical foundations underlying modern data science and scientific computing methodologies. The 2025 edition consisted of eight lectures covering inverse problems, spectral operator theory, convex optimization, optimization for machine learning, dynamical systems, and numerical methods, with morning lectures followed by hands-on afternoon sessions, a participant poster session, and research talks by the invited speakers G. Stadler (NYU) and A. Saibaba (NC State). More than 40 participants attended.
2023 Dagstuhl Seminar
Inverse Biophysical Modeling and Machine Learning in Personalized Oncology
Co-organized with G. Biros, B. H. Menze, and M. Schulte.
Schloss Dagstuhl – Leibniz Center for Informatics, January 8–13, 2023
This Dagstuhl Seminar brought together researchers from medical imaging, biophysical modeling, inverse problems, machine learning, and oncology to discuss challenges and opportunities in personalized oncology. The seminar focused on how mathematical modeling, computational methods, and data-driven approaches can be combined to advance patient-specific diagnosis, prognosis, and treatment planning in cancer care. Discussions were organized around four themes: machine learning for data analytics, predictive computational modeling through statistical inversion, the combination of machine learning with biophysical priors, and the translation of these methods to clinical decision-making.