Math vs. Gerrymandering with Speaker Thomas Weighill
Learn more about Math vs. Gerrymandering with Speaker Thomas Weighill on April 10th!
Learn more about Math vs. Gerrymandering with Speaker Thomas Weighill on April 10th!
The International Conference on Advances in Interdisciplinary Statistics and Combinatorics (AISC) will feature a private event with special sessions covering cutting-edge topics such as Causal Inference, Big Data, Machine Learning, Bayesian Statistics, and more.
The International Conference on Advances in Interdisciplinary Statistics and Combinatorics (AISC) will feature a private event with special sessions covering cutting-edge topics such as Causal Inference, Big Data, Machine Learning, Bayesian Statistics, and more.
The International Conference on Advances in Interdisciplinary Statistics and Combinatorics (AISC) will feature a private event with special sessions covering cutting-edge topics such as Causal Inference, Big Data, Machine Learning, Bayesian Statistics, and more.
This virtual event will feature Ravi Vakil, the Robert Grimmett Professor of Mathematics at Stanford University, who will also assume the role of President of the American Mathematical Society on February 1, 2025. Professor Vakil will discuss "The Mathematics of Doodling." "Doodling is a creative and fundamentally human activity, resulting... Continue reading...
Join us for the Helen Barton Lecture with Professor Tyler Jarvis from Brigham Young University as he explores new perspectives on aliasing in linear regression and its significance in data analysis.
Explore the role of the Gromov-Hausdorff distance in computational topology with researcher Nicolò Zava in this insightful talk.
The primary objective of this virtual conference series is to provide a forum for researchers from academia, industry, and laboratories world-wide to share results on all aspects of recent advances in partial differential equations. The overall goal of this conference series is to promote research in mathematical and computational analysis of differential equations. As a new feature in this conference series, we welcome undergraduate students doing research in differential equations to present their work.
The conference is dedicated to Professor Alfonso Castro in celebration of his 75th birthday and his outstanding contributions to Differential Equations.
Open to the public.
This talk explores how to construct meaningful features from noisy, high-dimensional data by leveraging geometric and invariant structures. First, we introduce a geometric framework for dimension reduction using a power-weighted path metric, which effectively de-noises high-dimensional data while preserving its intrinsic geometric structure. This framework is particularly useful for analyzing single-cell RNA data and for multi-manifold clustering, and we provide theoretical guarantees for the convergence of the associated graph Laplacian operators.