Conference: 2026 ASA Statistical Methods in Imaging (SMI) Conference

NSF Award Search · 01002627DB NSF RESEARCH & RELATED ACTIVIT · $25,000 · view on nsf.gov ↗

Abstract

The 2026 ASA Statistical Methods in Imaging (SMI) Conference will take place June 1-3, 2026 at the University of Michigan in Ann Arbor. This annual symposium serves as a vital hub for bringing together researchers to discuss the rapidly evolving role of statistics, mathematics, and AI in imaging science. From medical scans that detect disease to telescopes that capture distant galaxies, images are fundamental to modern discovery. However, extracting reliable and meaningful information from this visual data requires sophisticated mathematical, advanced statistical tools, and novel AI services. This conference fosters the collaboration needed to develop these tools, ensuring that scientific breakthroughs, from earlier disease detection to more accurate climate models, are built on a solid analytical foundation. By hosting this event, the University of Michigan will catalyze interdisciplinary dialogue, support the training of the next generation of data scientists through student awards and travel scholarships, and make advanced research accessible to a wider scientific community. The SMI-2026 conference will highlight the intersection of scientific innovation and cultural enrichment, and demonstrate how mathematical sciences contribute to a broader understanding of the world. The technical program for the SMI 2026 conference is designed to showcase cutting-edge methodological developments and their applications in imaging science. Over three days, the symposium will feature three keynote addresses from leading international experts, twenty special invited sessions, two short courses, student competitions, and networking events. A rigorous peer-review process will be implemented to select the best theoretical and applied papers, with a dedicated competition and award for the best student paper. The conference aims to highlight novel statistical approaches for complex imaging data, including high-dimensional inference, machine learning integration, and the analysis o

Key facts

NSF award ID
2611649
Awardee
Regents of the University of Michigan - Ann Arbor (MI)
SAM.gov UEI
GNJ7BBP73WE9
PI
Ivo D Dinov
Primary program
01002627DB NSF RESEARCH & RELATED ACTIVIT
All programs
Artificial Intelligence (AI), Machine Learning Theory, CONFERENCE AND WORKSHOPS, Biotechnology
Estimated total
$25,000
Funds obligated
$25,000
Transaction type
Standard Grant
Period
06/01/2026 → 11/30/2026