Description

Back to top

High-dimensional high-order/tensor data refers to data organized in the form of large-scale arrays spanning three or more dimensions, which becomes increasingly prevalent across various fields, including biology, medicine, psychology, education, and machine learning. Compared to low-dimensional or low-order data, the distinct characteristics of high-dimensional high-order data poses unprecedented challenges to the statistics community. For the most part, classical methods and theory tailored to matrix data may no longer apply to high-order data. While previous studies have attempted to address this issue by transforming high-order data into matrices or vectors through vectorization or matricization, this paradigm often leads to loss of intrinsic tensor structures, and as a result, suboptimal outcomes in subsequent analyses. Another major challenge stems from the computational side, as the high-dimensional high-order structure introduces severe computational difficulties previously unseen in the matrix counterpart. Many fundamental concepts and methods developed for matrix data cannot be extended to high-order data in a tractable manner; for instance, naive extensions of concepts such as operator norm, singular values, and eigenvalues all become NP-hard to compute. With these challenges in mind, there is an urgent need to develop new statistical methods and theory specifically tailored to handle high-dimensional high-order data.

This workshop provides an interdisciplinary platform for collaboration, facilitating the exchange of advanced research developments and topics in statistical and computational methods for analyzing tensor data. By bringing together statisticians, mathematicians, computer scientists, psychometricians, and machine learning researchers, the program aims to foster development of new interdisciplinary areas at the intersection of statistics, mathematics, psychometrics, and engineering. The workshop aims to contribute to both educational and research endeavors in these emerging fields.

This workshop will include lightning talks and a poster session for early career researchers (including graduate students). If accepted, you will be asked to do both. In order to propose a lightning session talk and a poster, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. The registration form should not be used to propose a lightning session talk or poster.

The deadline for proposing is March 3, 2025. If your proposal is accepted, you should plan to attend the event in-person.

Organizers

Back to top
Y C
Yuxin Chen University of Pennsylvania, Statistics and Data Science
Y G
Yuqi Gu Columbia University, Statistics
C M
Cong Ma University of Chicago, Statistics
A Z
Anru Zhang Duke University, Biostatistics & Bioinformatics and Computer Science

Speakers

Back to top
G A
Genevera Allen Rice University
R C
Rong Chen Rutgers University
D D
David Dunson Duke University
E E
Elene Erosheva Unviersity of Washington
P H
Peter Hoff Duke University
T K
Tracy Ke Harvard University
S K
Sunduz Keles University of Wisconsin-Madison
T K
Tamara Kolda MathSci.ai
L L
Lexin Li University of California, Berkeley
L L
Lek-Heng Lim University of Chicago
Z M
Zongming Ma Yale University
A M
Andrea Montanari Stanford University
A Q
Annie Qu University of California, Irvine
G R
Galen Reeves Duke University
M Y
Ming Yuan Columbia University
C Z
Cun-Hui Zhang Rutgers University
J Z
Ji Zhu University of Michigan

Registration

Back to top

IMSI is committed to making all of our programs and events inclusive and accessible. Contact to request accommodations.

In order to register for this workshop, you must have an IMSI account and be logged in. Please use one of the buttons below to login or create an account.