Events
Past Event
Statistics and Data Science Seminar: "Tensor Time Series: Factor Modeling and Deep Neural Networks"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
Tensor Time Series: Factor Modeling and Deep Neural Networks
Yuefeng Han, Assistant Professor, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame
Abstract: The analysis of tensors (multi-dimensional arrays) has become a vital area in modern statistics and data science, driven by advancements in scientific research and data collection. High-dimensional tensor data arise in diverse applications such as economics, genetics, microbiome studies, brain imaging, and hyperspectral imaging. These tensors are often high-dimensional and high-order, yet key information typically resides in reduced-dimensional subspaces governed by structural properties. This talk explores novel methodologies and theories for tensor time series analysis.
The presentation consists of two parts. The first part introduces a factor modeling framework for high-dimensional tensor time series, leveraging a structure similar to CP tensor decomposition. We propose a computationally efficient estimation procedure incorporating a warm-start initialization and an iterative simultaneous orthogonalization scheme. The algorithm achieves $\epsilon$-accuracy within $\log\log(1/\epsilon)$ iterations. Additionally, we establish inferential results, demonstrating consistency and asymptotic normality under relaxed assumptions. The second part integrates tensor factor models with deep neural networks. Specifically, a Tucker-type low-rank tensor structure is employed as a tensor-augmentation module in neural networks. Extensive experiments demonstrate the integration of this module into transformers and temporal neural networks for tensor time series prediction and tensor-on-tensor regression. The results highlight significant performance improvements, underscoring its potential for advancing time series forecasting.
Time
Friday, January 31, 2025 at 11:00 AM - 12:00 PM
Location
Ruan Conference Room – lower level, Chambers Hall Map
Contact
Calendar
Department of Statistics and Data Science
Northwestern Engineering PhD Hooding and Master's Recognition Ceremony
McCormick School of Engineering and Applied Science
4:00 PM
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Pick-Staiger Concert Hall
Details
The ceremony will take place on Saturday, December 13 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
Time
Saturday, December 13, 2025 at 4:00 PM - 6:00 PM
Location
Pick-Staiger Concert Hall Map
Contact
Calendar
McCormick School of Engineering and Applied Science
Inderpal Bhandari
Northwestern Network for Collaborative Intelligence (NNCI)
12:00 PM
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Guild Lounge, Scott Hall
Details
Join us for NNCI's first Distinguished Speaker event featuring Inderpal Bhandari.
Lunch will be provided. Registration is required.
Time
Tuesday, January 13, 2026 at 12:00 PM - 1:30 PM
Location
Guild Lounge, Scott Hall Map
Contact
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)
U.S. District Court Judge Xavier Rodriguez - NNCI & Pritzker School of Law
Northwestern Network for Collaborative Intelligence (NNCI)
12:15 PM
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Thorne Auditorium, Arthur Rubloff Building
Details
Join us for an in-person Distinguished Speaker event, co-hosted with Pritzker School of Law, featuring Judge Xavier Rodriguez.
Location: Thorne Auditorium, Pritzker School of Law, Chicago Campus
Lunch will be provided. Registration is required.
Time
Tuesday, January 20, 2026 at 12:15 PM - 1:30 PM
Location
Thorne Auditorium, Arthur Rubloff Building Map
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)
"Big-Data Algorithms That Are Not Machine Learning" - Jeffrey Ullman
Northwestern Network for Collaborative Intelligence (NNCI)
12:00 PM
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Ruan Conference Center, Chambers Hall
Details
Join us for an in-person Distinguished Speaker event featuring Jeffrey Ullman, a prominent figure in computer science whose contributions have shaped the foundations of algorithms, databases, and theoretical computing.
Title: "Big-Data Algorithms That Are Not Machine Learning"
Abstract: We shall introduce four algorithms that run very fast on large amounts of data, although typically the answers they give are approximate rather than precise. (1) Locality-sensitive hashing (2) Approximate counting (3) Sampling (4) Counting triangles in graphs.
Lunch will be provided. Registration is required.
Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.
Time
Tuesday, February 10, 2026 at 12:00 PM - 1:30 PM
Location
Ruan Conference Center, Chambers Hall Map
Contact
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)