# Conference: Statistics Beyond Euclid: Functional Data, Random Objects and AI

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of California-Davis (CA) · $20,000

## Abstract

The conference "Statistics Beyond Euclid: Functional Data, Random Objects and AI" will be held on November 13–14, 2026, at the University of California, Davis. Modern scientific data is increasingly captured in complex, non-traditional formats—such as the evolving shape of a virus, the connectivity patterns of a social network, or the continuous movement recorded by a wearable health monitor. Unlike simple numbers or coordinates, these "non-Euclidean" objects do not follow standard geometric rules, rendering traditional statistical tools ineffective for making accurate predictions or quantifying uncertainty. This project supports a landmark conference that brings together world-leading statisticians and artificial intelligence (AI) researchers to develop a new mathematical language for these data types. By integrating rigorous statistical reasoning with modern AI, the conference aims to create reliable methods for analyzing complex structures, ensuring that breakthroughs in technology are grounded in mathematical rigor. These advancements are vital for progress in diverse fields, from biomedical imaging to climate modeling. Furthermore, the project serves a critical national interest by providing travel support and mentorship to graduate students and early-career scientists, ensuring that the next generation of the American workforce is prepared to lead in the rapidly advancing landscape of data science and AI.
 
The conference "Statistics Beyond Euclid: Functional Data, Random Objects and AI" addresses the urgent need for foundational statistical methodology for data residing in general metric and geometric spaces, such as probability distributions, covariance matrices, manifolds, and functional trajectories. While modern deep learning architectures and large language models (LLMs) offer unprecedented computational power, they often lack the rigorous framework necessary to handle structured, non-Euclidean data or to provide valid uncertainty quantification. This 

## Key facts

- **NSF award ID:** 2623506
- **Awardee organization:** University of California-Davis (CA)
- **SAM.gov UEI:** TX2DAGQPENZ5
- **PI:** Jiming Jiang
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, CONFERENCE AND WORKSHOPS
- **Estimated total:** $20,000
- **Funds obligated:** $20,000
- **Transaction type:** Standard Grant
- **Period:** 08/01/2026 → 02/28/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2623506

## Citation

> US National Science Foundation, Award 2623506, Conference: Statistics Beyond Euclid: Functional Data, Random Objects and AI. Retrieved via AI Analytics 2026-07-25 from https://api.ai-analytics.org/grant/nsf/2623506. Licensed CC0.

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