University of Ottawa Colloquium in Mathematics and Statistics
Welcome to the University of Ottawa Colloquium in Mathematics and Statistics (UOCMS). This series brings together researchers from diverse institutions to present talks on current developments in mathematics and statistics.
Fall 2025
Time: Thursdays, 1:00-2:00 pm
Location: University of Ottawa, STEM building, room 664 (unless otherwise specified)
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Francesca Bartolucci, Delft University of Technology
Title: Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Date: September 4, 2025
Abstract: Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to learning partial differential equations from data. Conceptually clear when outlined on paper, neural operators require discretization in the transition to computer implementations. This step can compromise their integrity, often causing them to deviate from the underlying operators. This research offers a fresh take on neural operators with a Representation equivalent Neural Operators (ReNO) framework designed to address these issues. At its core is the concept of operator aliasing, which measures inconsistency between neural operators and their discrete representations. More generally, this framework not only sheds light on existing challenges but, given its constructive and broad nature, also potentially offers tools for developing new neural operators. This is a joint work with Rima Alaifari, Emmanuel de Bézenac, Siddhartha Mishra, Roberto Molinaro and Bogdan Raonić.
Slides
Susanne Pumpluen, University of Nottingham
Title: On Skew Constacyclic Codes and their Surprising Connection to Nonassociative Algebra
Date: September 11, 2025
Abstract: Cyclic, constacyclic and skew constacyclic codes are some of the most important and most investigated classes of linear codes (some are now also being used to build quantum error-correcting codes). Their cyclic structure allows us to characterize them using polynomials or skew polynomials, and to describe them as ideals in an algebra. More precisely, each codeword corresponds to a (skew) polynomial in a suitable chosen ideal of its ambient algebra. This ambient algebra may, however, be not associative. This is not well known, as all approaches so far only worked with associative ambient algebras or divert to ambient submodules. In the first part of the talk we will thus set up a cohesive theory that includes the nonassociative case. In the second part, we will address the problem how to classify skew constacyclic codes using the isomorphisms of their ambient algebras. We propose a new definition of equivalence that will result in tighter code classifications than previously presented ones, and will help to de-duplicate codes with the same performance paramenters. We prove with combinatorial methods that the notions of isometry and equivalence defined by Ou-azzou et al. (2025) coincide when the ambient algebras are not associative.
Slides
Emanuele Naldi, University of Genoa
Title: Inexact Jordan–Kinderlehrer–Otto and Proximal-gradient Algorithms in the Wasserstein Space: Links and Differences from the Hilbert Case
Date: September 18, 2025
Abstract: In this talk, we explore the asymptotic convergence properties of inexact Jordan–Kinderlehrer–Otto (JKO) scheme and proximal-gradient algorithm in the Wasserstein space. While the classical JKO scheme assumes exact minimization at each step, practical implementations rely on approximate solutions due to computational constraints. We analyze two types of inexactness: errors in Wasserstein distance and errors in functional evaluations. We establish rigorous convergence guarantees under controlled error conditions. Beyond the inexact setting, we also extend the convergence results by considering varying stepsizes. Our analysis expands previous approaches, providing new insights into discrete Wasserstein gradient flows. We finish the talk with a comparison to the Hilbert space setting, where the proximity operator is nonexpansive, a property that plays a central role in many classical convergence results. In the Wasserstein setting, the nonexpansivity of the proximity operator generally fails, even for geodesically convex functionals. We discuss the class of functions for which this property still holds and highlight potential directions for future research.
Jen Hom, Georgia Institute of Technology
Date: October 2, 2025
Location: Exceptionally, this colloquium will take place in STEM 464.
Anush Tserunyan, McGill University
Date: October 16, 2025
Alejandro Adem, University of British Columbia
Date: October 23, 2025
Florian Dumpert, Federal Statistical Office of Germany
Date: October 30, 2025
Location: Exceptionally, this colloquium will take place in STEM 464.
Bruno Feunou Kamkui, Bank of Canada
Date: November 13, 2025