Conference Information

MDS 2026: SIAM Conference on Mathematics of Data Science

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Submission Date:
2026-05-18
Notification Date:
Conference Date:
2026-11-16
Location:
Salt Lake City, Utah, USA
Viewed: 1402   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

43.6 / 100
Ranked #3,447 of 5,687 conferences · Top 61%

#28 of 69 in Mathematics & Physical Sciences #200 of 337 in Data Mining & Databases

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
35

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-25

Call For Papers

MDS 2026 (SIAM Conference on Mathematics of Data Science) is an academic conference held in Salt Lake City, Utah, USA on 2026-11-16. The paper submission deadline is 2026-05-18.

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
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