Other Past Events
2026 IMA Public Lecture: Hearing the will of the people in the vote
Thank you to all who attended the 2026 IMA Public Lecture! If you weren't able to join us in person, you can watch a recording of the event on YouTube.
What can mathematics tell us about the way federal elections work? Join the School of Mathematics for the 2026 IMA Public Lecture, where mathematics research, data science and public policy advocacy come together. In “Hearing the will of the people in the vote,” Professor Jonathan Mattingly of Duke University will explore how more than a decade of collaborative applied mathematics research has produced new tools and ideas for quantifying political and racial gerrymandering.
Abstract
The US political system is built on representatives chosen by geographically localized regions. This presents the government with the problem of designing these districts. Every ten years, the US census counts the population, and new political districts must be drawn. The practice of harnessing this administrative process for partisan political gain is often referred to as gerrymandering.
How does one identify and understand gerrymandering? Can we really recognize gerrymandering when we see it? If one party wins over 50% of the vote, is it fair that it wins less than 50% of the seats? What do we mean by fair? Should we expect proportional representation? How can math help illuminate these questions? How does the geopolitical geometry of the state (where which groups live and the shape of the state) inform these answers?
Our ability to answer these questions is still a work in progress and presents many interesting mathematical research questions. Topics include ideas from computation, statistics/statistical physics, combinatorics, high-dimensional probability, Markov Chain theory, and modern data science.
The story thus far has been an interaction between lawyers, mathematicians, computational scientists, and policy advocates. The legal discussion has been increasingly informed by the mathematical framework. And the mathematics has been pushed to be better included in the policy considerations. This back and forth has been important to finding ways to effectively inform the policymakers and courts. The problem of understanding gerrymandering has also prompted the development of a number of new computational algorithms that come with new mathematical questions.
The next round of redistricting analysis will necessarily need to be more refined and nuanced. There is also the opportunity to be less reactive. There are opportunities to try to influence the process by which new maps are drawn before turning to the courts. There is also the possibility to direct the conversation by showing the effect more fully, considering factors such as communities of interest, incumbency, or proposed procedural elements of laws.
For me, these questions began with an undergraduate research program project in 2013 and have led me to testify in a number of cases: Common Cause v. Rucho (that went to the US Supreme Court), Common Cause v. Lewis, Harper v. Lewis, and Harper v. Hall/Moore, to name a few.
This presentation reflects joint work with Gregory Herschlag and a number of other researchers, including many undergraduates, graduate students, and a few high school students.
Jonathan Mattingly
A mathematician and computational scientist who grew up in Charlotte, NC, Jonathan Mattingly studies the long-time behavior of random dynamical systems and computational algorithms. He is particularly interested in Bayesian Sampling Methods, modeling biological systems, and statistical theories of turbulent fluids. He has been a member of the NC School of Science and Math, Yale University, the École Normale Supérieure de Lyon, Princeton University, Stanford University, and is currently the Kimberly J. Jenkins Distinguished University Professor of New Technologies at Duke University. Over the course of his career, Mattingly has received an NSF CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a Sloan Foundation Faculty Fellowship. He is a fellow of the Institute for Mathematical Statistics (IMS), the American Mathematics Society (AMS), Society for Industrial and Applied Mathematics (SIAM), the American Association for the Advancement of Science (AAAS), and the American Academy of Arts and Sciences. His mother grew up by Cedar Lake, and he spends holidays in the Twin Cities.
Order and Function in Complex Systems Conference
Conference on order and function in complex systems: coherent structures, selection and stability
August 10 – 14, 2026
University of Minnesota Twin Cities
Overview
Many systems of interest in the natural sciences and engineering are complex, in that they possess a large number of degrees of freedom, which makes it a priori difficult to predict their behavior. Despite this, the behavior of many complex systems may be largely driven by a small number of coherent states, with a simpler description and a small number of free effective degrees of freedom. The focus of this conference is on methods from dynamical systems, PDE theory, and functional analysis for explaining and predicting the emergence of such coherent structures, as well as understanding their subsequent temporal dynamics. These coherent structures include fronts, waves, vortices or patterned states such as rolls or spots. Their existence and dynamics are essential to a range of applications spanning ecology, fluid dynamics, materials science, biology, and physics. There are nonetheless many mathematical commonalities in the study of coherent structures in these seemingly disparate areas of application. Recent years have seen many significant advances in both the theoretical and experimental understanding of different aspects of coherent structures. The conference will assemble international experts in the dynamics of coherent structures, together with a sizable group of early career researchers, for a week-long meeting covering the many facets of coherent structures research. It will facilitate the cross pollination of ideas key to recent advances in theory, computation, and experiment.
Organizers
- Montie Avery, Emory University
- Grégory Faye, CNRS, Institut de Mathématiques de Toulouse
- Ryan Goh, Boston University
- Matt Holzer, George Mason University
- Gabriela Jaramillo, University of Houston
- Merlin Pelz, University of Minnesota
- Qiliang Wu, Ohio University
Mathematics of Data Science Summer School
In this two-week summer school for advanced undergraduate students, participants will be introduced to the mathematics underlying selected fundamental algorithms in data science, with a focus on methods of unsupervised learning for embedding and clustering data. Students will learn topics from high-dimensional probability, random matrix theory, graph theory, optimal transport, and spectral theory, among others. The material is geared toward students considering graduate study in the mathematics of data science.
Registration and Financial Support
Financial support in the form of reimbursement of travel and living expenses will be provided to facilitate students' participation. To apply, please submit your application materials at the link below. The application deadline is February 16, 2026.
Faculty hosts
Questions?
Email Professor William Leeb at [email protected].
Summer Workshop on Character Sheaves on Loop Groups
Overview
Lusztig's theory of character sheaves for reductive groups provides powerful geometric tools for harmonic analysis over finite fields. In recent years, there has been impressive development in the generalization of character sheaves theory to loop groups. These advances uncover deep geometric structures underlying representation and harmonic analysis over p-adic fields, leading to new perspectives on the local aspect of the arithmetic and geometric Langlands program. We are hosting a workshop with lectures covering a broad range of topics connected with character sheaves on loop groups, its applications, and related future directions.
Registration
Registration and applications for travel support are now being accepted.
Special lecture
George Lusztig (MIT)
Speakers
- Roman Bezrukavnikov (MIT)
- Charlotte Chan (Michigan)
- Ngo Bao Chau (Chicago)
- Gurbir Dhillon (UCLA)
- Tasho Kaletha (Bonn/Michigan)
- David Nadler (Berkeley)
- Cheng-Chiang Tsai (Academia Sinica, Taipei)
- Yakov Varshavsky (Hebrew University)
- David Yang (MIT)
- Zhiwei Yun (MIT)
- Xinwen Zhu (Stanford)
Organizers
- Tsao-Hsien Chen (UMN)
- Mark Macerato (UMN)
- Cheng-Chiang Tsai (Academia Sinica, Taipei)
Doenet Community Workshop 2026
We invite you to participate in a five-day in-person workshop to learn how to create richly interactive classroom activities using the free and open-source Doenet platform. Not only is Doenet a fun way to create cool activities, it is also a community of STEM instructors and authors who strive to engage students' minds and spur active interaction with mathematical ideas. We welcome you to work together with us to develop an open-source community, resources, and tools that enable anyone to create exploratory activities with feedback.
The workshop will include brief introductions and demos of features of the Doenet platform, as well as short presentations on how people are using Doenet. For the majority of the time, participants will work in small groups on projects of interest to them. These groups will be created dynamically each day. Possible groups could focus on:
- creating your first activity in Doenet
- exploring advanced features of Doenet
- developing activities for a specific open-source textbook
- developing new features to add to the Doenet software
- exploring ways to contribute to the Doenet community and project
- planning steps for long-term sustainability of Doenet
All-Department Workshop: The Human Variable
Join us for an all-department workshop! The Human Variable: Navigating Interpersonal Dynamics with Julie Showers
Academic excellence is in our DNA, but a strong department relies on more than just successful proofs and accepted publications. Thriving in mathematics requires a shared ability to navigate the interpersonal friction that naturally arises in the high-intensity environment of academia. We’re continuing our departmental climate event series this spring with an interactive session led by Julie Showers. We hope you’ll join us for a dynamic workshop to develop a shared vocabulary for managing conflict and maintaining strong professional relationships
Workshop Abstract: In many surveys, interpersonal conflict is the primary challenge that university faculty, staff and students find most stressful. This isn't surprising. We move through high-intensity workplaces and social spaces feeling increased pressure from stakeholders (faculty members, students, staff, leaders) to have all the answers, all the time - and to express those answers in exactly the right way. Some of us face particular conflicts that seem impossible to resolve and particular colleagues who seem determined to be difficult. And yet, the ability to experience tension and remain in relationship with those who cause it is a key strength regardless of field, discipline or context. This event explores practical tools to improve our resiliency and efficacy when conflict inevitably arises.
Speaker Bio: Julie Showers retired in 2021 after serving as the University of Minnesota's Associate Vice President in the Office for Equity and Diversity. Prior to that, she served as the Director of the Office of Conflict Resolution. Before joining the University, Julie worked for over 25 years in private practice and as a senior officer at Northwest and Delta Air Lines. Now a consultant, she specializes in helping people navigate conflict in healthy and effective ways and serves as an adjunct instructor at the University of Minnesota Law School.
This event is being hosted by the Mathematics Equity & Inclusion Committee. The topic was selected based on Spring 2025 survey results of the School of Mathematics community.
Deep Learning meets PDE Workshop
Overview
Deep learning has revolutionized the landscape of PDE computation. On one hand, many challenging problems arising in the physical sciences are governed by PDEs involving high-dimensional solution spaces or high-dimensional parameter fields, where traditional numerical methods face severe computational bottlenecks. Deep neural networks offer a potentially powerful and efficient new paradigm for tackling such problems. On the other hand, many mathematical models contain unknown parameters or missing terms. Deep learning provides a flexible framework to seamlessly incorporate observational data into model prediction, while offering strategies to quantify and address model uncertainties. Despite these promising developments, the indiscriminate use of deep learning for PDE computation poses significant challenges. Training can be expensive and unstable, and may lead to solutions that lack robustness or physical reliability. Moreover, such approaches often rely on large amounts of high-fidelity data, the generation of which is itself a formidable task.
Given the past decades of intellectual development in classical numerical methods for PDEs, there is immense potential in thoughtfully combining deep learning with established computational techniques. This workshop aims to provide a platform for researchers with backgrounds in both classical numerical analysis and modern scientific machine learning to exchange ideas and technical expertise, and fostering dialogue between the scientific computing and machine learning communities.
Speakers
- Harbir Antil, George Mason University
- Wei Cai, Southern Methodist University
- Eric C. Cyr, Sandia National Laboratories
- Xue Feng, University of California, Los Angeles
- Rongjie Lai, Purdue University
- Fei Lu, Johns Hopkins University
- Chun Liu, Illinois Institute of Technology
- Mitch Luskin, University of Minnesota
- Levon Nurbekyan, Emory University
- Lorenzo Pareschi, Heriot-Watt University
- Tianyun Tang, University of Chicago
- Xiaochuan Tian, University of California, San Diego
- Richard Tsai, University of Texas at Austin
- Jack Xin, University of California, Irvine
- Wuzhe Xu, Purdue University
Organizer
86th William Lowell Putnam Mathematical Competition
This year, the 86th William Lowell Putnam competition will be held on Saturday, December 6th, 2025 in Vincent Hall Room 16 on the East Bank Campus. This is a test of mathematical creativity in problem solving, and is directed to students of science and engineering, as well as mathematics.
The exam consists of two three-hour sessions (9am - noon, and 2pm - 5pm), punctuated by a two-hour lunch break. While taking the exam, each participant works independently, and participation also includes a pledge not to post or discuss the exam questions online or otherwise before 7 p.m. (“8 p.m. EST”, which is the end of the exam on the West Coast) that day. The questions posed in previous editions are posted (together with solutions) on the examination website, and the “Putnam Exam Archive” managed by Kiran Kedlaya displays many more.
You are eligible to compete if you are an undergraduate student currently studying here (even as a visiting student), do not already hold a Bachelor's degree (or its equivalent or superior), and have not already participated in four of the previous competitions.
Practice sessions will be held every week until the date of the exam, starting on October 10th, from 3:00 - 4:30pm. Please see the full practice schedule below for specific practice dates, times, and locations. To RSVP for the practice sessions please fill out this Math Comp and Problem Solving Club Interest Form.
Students are also encouraged to join the Putnam Practice Discord for more information and details about the Putnam exam, and the practice sessions.
Note: you are not required to RSVP to attend Putnam practice sessions, all are welcome!
Full schedule of practice sessions:
- Friday, October 10th, 3:00pm - 4:30 pm, Vincent Hall 207
- Friday, October 17th, 3:00pm - 4:30 pm, Vincent Hall 207
- Friday, October 24th, 3:00pm - 4:30 pm, Vincent Hall 207
- Monday, November 3rd, 3:00pm - 4:30 pm, Vincent Hall 215
- Monday November 10th, 3:00pm - 4:30 pm, Vincent Hall 215
- Friday, November 14th, 3:00pm - 4:30 pm, Vincent Hall 207
- Friday, November 21st, 3:00pm - 4:30 pm, Vincent Hall 207
- Monday, December 1st, 3:00pm - 4:30 pm, Vincent Hall 215
Equivariant, Motivic, and Physical Topology in the Midwest
This three-day conference celebrates the 60th birthday and influential career of Igor Kriz, whose groundbreaking contributions have shaped modern perspectives in homotopy theory and its interactions with physics. This special event will gather leading researchers and early-career mathematicians from around the world for a series of stimulating lectures, discussions, and collaborations.
The conference will spotlight cutting-edge developments in equivariant and motivic homotopy theory, as well as their deep connections to mathematical physics and symplectic topology. Invited talks by prominent experts will explore recent advances and open problems at the rich interface of these fields, honoring the wide-ranging impact of Kriz’s work.
We warmly invite you to be part of this celebration of mathematics, community, and visionary research.
Registration
Speakers
- David Ayala, Montana State University
- Agnès Beaudry, University of Colorado, Boulder
- Jeremiah Heller, University of Illinois at Urbana-Champaign
- Mike Hill, University of Minnesota, Twin Cities
- Po Hu, Wayne State University
- Daniel Kriz, Università degli Studi di Milano
- Sophie Kriz, Princeton University
- Tyler Lawson, University of Minnesota, Twin Cities
- Yunze Lu, University of California, San Diego
- Peter May, University of Chicago
- Morgan Opie, Northwestern University
- JD Quigley, University of Virginia
- Hisham Sati, New York University, Abu Dhabi
Schedule
Friday
- 12:30 p.m. - 1:30 p.m. – Arrival and registration
- 1:30 p.m. - 1:40 p.m. – Very brief welcome remarks
- 1:40 p.m. - 2:30 p.m. – Jeremiah Heller
- 2:30 p.m. - 2:50 p.m. – Break
- 2:50 p.m. - 3:40 p.m. – Po Hu
- 3:40 p.m. - 4:00 p.m. – Break
- 4:00 p.m. - 4:50 p.m. – Mike Hill
- 4:50 p.m. - 5:10 p.m. – Break
- 5:10 p.m. - 6:00 p.m. – Yunze Lu
- 7:00 p.m. – Dinner at Tea House
Saturday
- 8:00 a.m. - 9:00 a.m. – Coffee and light breakfast
- 9:00 a.m. - 9:50 a.m. – Hisham Sati
- 9:50 a.m. - 10:10 a.m. – Break
- 10:10 a.m. - 11:00 a.m. – David Ayala
- 11:00 a.m. - 11:20 a.m. – Break
- 11:20 a.m. - 12:10 p.m. – Agnès Beaudry
- 12:10 p.m. - 2:00 p.m. – Lunch break
- 2:00 p.m. - 2:50 p.m. – Daniel Kriz
- 2:50 p.m. - 3:10 p.m. – Break
- 3:10 p.m. - 4:00 p.m. – JD Quigley
- 4:00 p.m. - 4:20 p.m. – Break
- 4:20 p.m. - 5:10 p.m. – Peter May
Sunday
- 8:00 a.m. - 9:00 a.m. – Coffee and light breakfast
- 9:00 a.m. - 9:50 a.m. – Sophie Kriz
- 9:50 a.m. - 10:10 a.m. – Break
- 10:10 a.m. - 11:00 a.m. – Morgan Opie
- 11:00 a.m. - 11:20 a.m. – Break
- 11:20 a.m. - 12:10 p.m. – Tyler Lawson
- 12:10 p.m. – Closing and departure
Organizers
- Thomas Fiore, University of Michigan-Dearborn
- José Manuel Gómez Guerra, Universidad Nacional de Colombia
- Kyle Ormsby, Reed College
- Maru Sarazola, University of Minnesota, Twin Cities
- Sasha Voronov, University of Minnesota, Twin Cities
- Craig Westerland, University of Minnesota, Twin Cities
Logistics
Getting to UMN
- The Minneapolis-Saint Paul International Airport (MSP) is conveniently located nearby Downtown Minneapolis. The airport is connected to the city center and to the UMN campus by public transport, particularly the Metro Blue Line.
- Metro Transit is the Minneapolis / St. Paul public transportation system – it includes bus and train services that run from very early morning until late in the evening.
- Bicycles and scooter rentals are available from Lime, Spin, and Veo.
Child care
Care.com connects visiting families with experienced local caregivers.
Explore the Twin Cities
Looking for things to do while in Minneapolis? Here are some great resources:
