A research conference connecting foundational statistics with modern methods for functional, geometric, high-dimensional, and object-valued data, alongside advances in machine learning and artificial intelligence.

9 Invited Sessions: Featuring 29 invited speakers delivering talks across the three days.

Poster Session & Networking: Dedicated poster presentations and networking opportunities designed specifically for early-career scholars, postdocs, and graduate students to showcase ongoing research.

Interdisciplinary Focus: Bridging the gap between mathematical statistics, machine learning, and complex scientific applications.

The scientific scope of the program encompasses a wide range of topics, including: Functional Data Analysis (FDA), Random Objects & Object-Oriented Data Analysis, Geometric & Metric Statistics, Manifold-Valued Data & Riemannian Statistics, Distributional Data Analysis, High-Dimensional Statistics, Network Analysis & Complex Structured Data, Causal Inference, and Statistical Foundations of Machine Learning, Deep Learning, & AI.

IMS Representative(s) on Program Committees: Paromita Dubey