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.

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Friday / CS Seminar
January 31st / 12:00 PM
Hybrid / Mudd 3514

Speaker
Daniel Halpern, Harvard University

Talk Title
Why AI Needs Social Choice

Abstract
In many modern AI paradigms, we encounter tasks reminiscent of social choice theory: collecting preferences from individuals and aggregating them into a single joint outcome. However, these tasks differ from traditional frameworks in two key ways: the space of possible outcomes is so enormous that we can only hope to collect sparse inputs from each participant, and the outcomes themselves are often highly complex. This talk explores these challenges through two case studies: Polis, a platform for democratic deliberation (https://arxiv.org/abs/2211.15608), and Reinforcement Learning From Human Feedback (RLHF), a method for fine-tuning LLMs to align with societal preferences (https://arxiv.org/pdf/2405.14758). In both cases, the focus is on evaluating existing methods through an axiomatic lens and designing new methods with provable guarantees.

Biography
Daniel Halpern is a final-year PhD student at Harvard University advised by Ariel Procaccia. He is supported by an NSF Graduate Research Fellowship and a Siebel Scholarship. His research broadly sits at the intersection of algorithms, economics, and artificial intelligence. Specifically, he considers novel settings where groups of people need to make collective decisions, such as summarizing population views on large-scale opinion aggregation websites, using participant data to fine-tune large language models, and selecting panel members for citizens’ assemblies. In each, he develops practical and provably fair solutions to aggregate individual preferences.

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Zoom: https://northwestern.zoom.us/j/96824870974?pwd=QfxpsRpfWcDlx4TXPswbAd8X4Dqhyb.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=2159520b-1d46-4ece-ba17-b268017475c6
Community Connections Topic: Being comfortable with being uncomfortable

In this talk, I will explore the interfacing of simulations, observations, and machine learning techniques to construct a detailed map of Dark Matter in the Milky Way, focusing on the Galactic Center/Halo and dwarf galaxies. For the Galactic Halo, I will present a recent work that reveals a decline in the stellar circular velocity, inducing tensions with established estimates of the Milky Way's mass and Dark Matter content. I will discuss how the underestimated systematic errors in such a common methodology necessitates a revised approach that combines theory, observations, and machine learning. In dwarf galaxies, I will present a novel Graph Neural Network methodology that facilitates the accurate extraction of Dark Matter density profiles, validated against realistic simulations. I will conclude with a discussion on the future trajectory of astroparticle physics, emphasizing the need for the integration of astrophysical probes with experimental Dark Matter research, potentially leading to a better understanding of the nature of Dark Matter.

Lina Necib, Assistant Professor, Massachusetts Institute of Technology

Host: Claude-Andre Faucher-Giguere

Would you like to simplify your machine learning code and minimize repetitive tasks? Scikit-Learn's pipelines can help you organize and streamline your data processing and model training, as well as make your code cleaner and easier to manage. In this workshop, we will cover why and how to use pipelines in your machine learning code.

Prerequisites: Participants should be familiar with Python at the level of the Python Fundamentals Bootcamp, another introductory Python workshop, or be a self-taught Python coder. Basic familiarity with machine learning and Scikit-Learn is required.

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"Using Computational Approaches to Understand the Social and Structural Drivers of Health"

By Michelle Birkett, Associate Professor of Medical Social Sciences (Determinants of Health) and Preventive Medicine and IPR Associate

This event is part of the Fay Lomax Cook Winter 2025 Colloquium Series, where our researchers from around the University share their latest policy-relevant research.

Please note all colloquia this quarter will be held in-person only.

In recent years generative artificial intelligence (GAI) tools have increased in availability and popularity at universities, but not everyone knows how to use them for better library search results. In this 60-minute hybrid session we will demonstrate how to use Microsoft Copilot, a free GAI tool for all NU students and faculty, to improve searches with the gold standard of reliable content, library databases. This session is geared toward participants with little-to-no familiarity with generative artificial intelligence, and will also address the basics of what generative artificial intelligence is, how it works, its risks, and limitations. Students can learn about NU’s Copilot accounts here.

This workshop is presented by Tracy Coyne, Distance Learning and Professional Studies Librarian; Frank Sweis, User Experience Librarian; and Jeannette Moss, User Education Librarian.

A Northwestern Zoom Account is required to access this session.

Register Now

Wednesday / CS Seminar
February 5th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Akari Asai, University of Washington

Talk Title
Beyond Scaling: Frontiers of Retrieval-Augmented Language Models

Abstract
Large Language Models (LMs) have demonstrated remarkable capabilities by scaling up training data and model sizes. However, they continue to face critical challenges, including hallucinations and outdated knowledge, which particularly limit their reliability in expert domains such as scientific research and software development. In this talk, I will urge the necessity of moving beyond the traditional scaling of monolithic LMs and advocate for Augmented LMs—a new AI paradigm that designs, trains, and deploys LMs alongside complementary modules to address these limitations. Focusing on my research on Retrieval-Augmented LMs, one of the most impactful and widely adopted forms of Augmented LMs today, I will begin by presenting our systematic analyses of current LM shortcomings and demonstrate how Retrieval-Augmented LMs offer a more effective and efficient path forward. I will then discuss my work to establish new foundations for further reliability and efficiency by designing and training new LMs and retrieval systems to dynamically adapt to diverse inputs. Finally, I will demonstrate the real-world impact of such Retrieval-Augmented LMs through OpenScholar, our fully open Retrieval-Augmented LM designed to assist scientists in synthesizing scientific literature, now used by more than 25,000 researchers and practitioners worldwide. I will conclude by outlining my vision for the future of Augmented LMs, emphasizing advancements in their abilities to handle heterogeneous and diverse modalities, more efficient and effective integration with diverse components, and advancing evaluations with interdisciplinary collaboration.

Biography
Akari Asai is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Her research addresses the limitations of large language models (LMs) by developing advanced systems, such as Retrieval-Augmented LMs, and applying them to real-world challenges, including scientific research and underrepresented languages. Her contributions have received widespread recognition, including multiple paper awards at top NLP and ML conferences, the EECS Rising Stars 2022, and MIT Technology Review's Innovators Under 35 Japan. She has also been honored with the IBM Global Fellowship and several industry grants. Akari actively engages with the research community as a co-organizer of a tutorial and workshops, including the first tutorial on Retrieval-Augmented LMs at ACL 2023, as well as NAACL 2022 Workshop on Multilingual Information Access and NAACL 2025 Workshop on Knowledge-Augmented NLP.

Research/Interest Areas
Natural Language Processing, Machine Learning, Large Language Models
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Zoom: https://northwestern.zoom.us/j/92736097526?pwd=TEoEMxEcDOanxEAoaNdB4ZIxXGsgwV.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7a06abb9-4dd5-402e-a033-b274015b1e07
Community Connections Topic: Lab counterculture

Speaker:

Marianne Green, MD, Vice Dean for Education, Chair, Department of Medical Education, Feinberg School of Medicine, Northwestern University

Title:

Precision Education in Medicine

Abstract:

This presentation explores the transformative potential of precision education in medicine- an innovative approach that leverages comprehensive student and trainee data to tailor educational experiences and ultimately improve patient care. By integrating diverse data sources, including academic history, assessments, and electronic health record information, precision education provides a holistic view of student and trainee performance. The presentation highlights how personalized learning pathways and predictive analytics may enhance student and trainee outcomes, enabling early interventions and continuous feedback for improvement. Discussion of early adopter use cases in medical education highlight the potential and the challenges. Data privacy, ethical considerations, and technical hurdles will be addressed. Looking ahead, emerging technologies like AI promise to revolutionize precision education, paving the way for a fully integrated, data-driven ecosystem that can empower every student and ultimately every physician to reach their full potential with better health outcomes as the primary goal.

Speaker Bio:

Marianne Green, MD is the Raymond H. Curry, MD, Professor of Medical Education at the Northwestern Feinberg School of Medicine. She is also the Vice Dean for Education, the chair of the Department of Medical Education, and Co-Director of the Institute for Artificial Intelligence in Medicine - Center for Medical Education in Data Science and Digital Health. Clinically she is interested in the primary care of adults with acute and chronic illness. Academically she is interested in competency assessment for both undergraduate medical students and practicing physicians.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/94544365225
Passcode: NICO25

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

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AI for Nature: From Science to Impact

Tanya Berger-Wolf, Professor, Computer Science and Engineering and Director, Translational Data Analytics Institute, The Ohio State University

Abstract: Computation has fundamentally changed the way we study nature. New data collection technologies, such as GPS, high-definition cameras, autonomous vehicles under water, on the ground, and in the air, genotyping, acoustic sensors, and crowdsourcing, are generating data about life on the planet that are orders of magnitude richer than any previously collected. Yet, our ability to extract insight from these data lags substantially behind our ability to collect it.

The need for understanding is more urgent ever and the challenges are great. We are in the middle of the 6th extinction, losing the planet's biodiversity at an unprecedented rate and scale. In many cases, we do not even have the basic numbers of what species we are losing, which impacts our ability to understand biodiversity loss drivers, predict the impact on ecosystems, and implement policy.

The talk will discuss how AI can turn these data into high resolution information source about living organisms, enabling scientific inquiry, conservation, and policy decisions. It will introduce a new field of science, imageomics, and present a vision and examples of AI as a trustworthy partner both in science and biodiversity conservation, discussing opportunities and challenges.

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Monday / CS Seminar
February 10th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Sidhanth Mohanty

Talk Title
A quest for an algorithmic theory for high-dimensional statistical inference

Abstract

"When does a statistical inference problem admit an efficient algorithm?
There is an emergent body of research that studies this question by trying to understand the power and limitations of various algorithmic paradigms in solving statistical inference problems; for example, convex programming, Markov chain Monte Carlo (MCMC) algorithms, and message passing algorithms to name a few.

Of these, MCMC algorithms are easy to adapt to new inference problems and have shown strong performance in practice, which makes them promising as a universal algorithm for inference. However, provable guarantees for MCMC have been scarce, lacking even for simple stylized models of inference.

In this talk, I will survey some recent strides that I have made with my collaborators on achieving provable guarantees for MCMC in inference, and some new tools we introduced for analyzing the behavior of slow-mixing Markov chains."

Biography

"Sidhanth is broadly interested in theoretical computer science and probability theory, and his primary interests are on the algorithms and complexity of statistical inference, and spectral graph theory.

Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley in 2023 where he was advised by Prasad Raghavendra."

Research/Interest Areas
Theoretical computer science, algorithmic statistics, analysis of Markov chains, spectral graph theory, semidefinite programming, random matrix theory
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Community Connections Topic: Equitable Assessments

Drawing on the Northwestern Engineering whole-brain philosophy and leadership in cognitive science, the Master of Science in Artificial Intelligence program would like to invite you to learn more at our upcoming webinar.

Take this opportunity to join Dr. Kristian Hammond, Professor of Computer Science and director of the MSAI program, as he discusses the complexities of this field, and how this newly offered program at Northwestern Engineering will prepare students for a career in artificial intelligence. At the end of the presentation, we will offer an open Q&A where you will be able to have your specific questions answered. You are also welcome to email your questions to us ahead of the session (msai@northwestern.edu).

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Register Now

Curious about how AI can transform your text data analysis? Large Language Models (LLMs), like those behind ChatGPT, offer powerful ways to extract information from text, such as identifying key individuals, finding information about events, or selecting sections of documents. Learn how LLMs can assist with information extraction, and how they compare with other approaches such as rule-based methods, regular expressions, and pre-trained entity recognition models.

Prerequisites: Open to anyone working with or interested in text data, this workshop will provide hands-on examples in Python, though the concepts apply across various programming languages. While prior experience in natural language processing is helpful, it’s not required to participate.

Register Now

Wednesday / CS Seminar
February 12th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Wenting Zhao

Talk Title
Reasoning in the Wild

Abstract
In this talk, I will discuss how to build natural language processing (NLP) systems that solve real-world problems requiring complex reasoning. I will address three key challenges. First, because real-world reasoning tasks often differ from the data used in pretraining, I will introduce WildChat, a dataset of reasoning questions collected from users, and demonstrate how training on it enhances language models’ reasoning abilities. Second, because supervision is often limited in practice, I will describe my approach to enabling models to perform multi-hop reasoning without direct supervision. Finally, since many real-world applications demand reasoning beyond natural language, I will introduce a language agent capable of acting on external feedback. I will conclude by outlining a vision for training the next generation of AI reasoning models.

Biography
Wenting Zhao is a Ph.D. candidate in Computer Science at Cornell University, advised by Claire Cardie and Sasha Rush. Her research focuses on the intersection of natural language processing and reasoning, where she develops techniques to effectively reason over real-world scenarios. Her work has been featured in The Washington Post and TechCrunch. She has co-organized several tutorials and workshops, including the VerifAI: AI Verification in the Wild workshop at ICLR 2025 and the Complex Reasoning in Natural Language tutorial at ACL 2023. In 2024, she was recognized as a rising star in Generative AI and was named Intern of the Year at the Allen Institute for AI in 2023.

Research/Interest Areas
natural language processing, AI, reasoning
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Community Connections Topic:

Towards Data-efficient Training of Large Language Models (LLMs)

Baharan Mirzasoleiman, Assistant Professor, Computer Science Department, UCLA

Abstract: High quality data is crucial for training LLMs with superior performance. In this talk, I will present two theoretically-rigorous approaches to find smaller subsets of examples that can improve the performance and efficiency of training LLMs. First, I will present a one-shot data selection method for supervised fine-tuning of LLMs. Then, I'll talk about an iterative data selection strategy to pretrain or fine-tune LLMs on imbalanced mixtures of language data. I'll conclude by showing empirical results confirming that the above data selection strategies can effectively improve the performance of various LLMs during fine-tuning and pretraining.

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Register Now

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Part of the 2025 University Practicum on Supporting Student Success, participants may attend any and all practicum events.

Related Info

Register Now

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Title: Astrophysical Fluid Dynamics at Exascale

Speaker: Jim Stone, Institute for Advanced Study

Abstract: Most of the visible matter in the Universe is a plasma -- that is
a dilute gas of electrons, ions, and neutral particles -- interacting
with both magnetic and radiation fields. Studying the structure
and dynamics of astrophysical systems, from stars and planets, to
galaxies and the large-scale structure of the Universe itself,
usually requires numerical methods to solve the coupled equations
of compressible radiation magnetohydrodynamics (MHD). Robust
numerical algorithms for modeling astrophysical fluids, including
new methods for calculating radiation transport in relativistic
flows, will be discussed. Efficient implementation of these methods
on modern high-performance computing systems is crucial, and an
approach based on the Kokkos programming model that enables performance
portability will be described. Performance on a variety of
architectures of a new adaptive mesh refinement (AMR) astrophysical
MHD code will be given, including scaling on up to 65536 GPUs on
the OLCF Frontier exascale computer. Finally, a case study will
be presented that demonstrates some of the many new insights that
have come from applying computational methods to one particular problem:
how plasma accretes onto the black holes in the centers of galaxies.

Zoom: https://northwestern.zoom.us/j/95581369835

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Speaker:

Morgan Frank, Assistant Professor, Department of Informatics and Networked Systems, University of Pittsburgh

Title:

AI, Complexity, and the Future of Work

Abstract:

Artificial Intelligence has evolved and now challenges our understanding of skills, careers, and the future of work. Using a variety of data on employment, occupations’ skill requirements, millions of resumes, and unemployment data from US states’ unemployment insurance offices, this talk will explore how workers’ skills shape their careers and how automation estimates fit into a framework for career adaptability and the economic resilience of labor markets. Work from this talk comes from a variety of publications in PNAS, Nature Communications, and Science Advances.

Speaker Bio:

Morgan Frank is an Assistant Professor at the School of Computing and Information at the University of Pittsburgh. Morgan is interested in the complexity of AI, the future of work, and the socio-economic consequences of technological change. While many studies focus on phenotypic labor trends, Morgan’s recent research examines how genotypic skill-level processes around AI impact individuals and society. Combining labor research with investigations into the nature of AI research and the social or societal implications of AI adoption, Morgan hopes to inform our understanding of AI’s impact. Morgan has a PhD from MIT’s Media Lab, was a postdoc at MIT IDSS and the IDE, and has a master’s degree in applied mathematics from the University of Vermont where he was a member of the Computational Story Lab.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91407653122
Passcode: NICO25

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

Related Info

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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Biography
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Research/Interest Areas
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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute