CCMS Colloquium: Manuel Reyes (UCI)
2026-04-24 11:00 - 12:15 (America/Los_Angeles) | Davidson Lecture Hall, CMC, 340 E 9th St, Claremont, CA, 91711, United States
CCMS Colloquium invites you to a talk by Manuel Reyes (UCI)

Title: When XY is not equal to YX

Abstract: The commutative property of algebra states that the order of multiplication makes no difference: XY = YX. This property is so ingrained in our experience that it becomes difficult to imagine how algebra would function without it! If the commutative property fails, we step into the territory of noncommutative algebra. Where and why does noncommutative algebra occur? How different is it from our ordinary commutative algebra? This talk will be an introduction to noncommutative algebra, with several examples and a glimpse into current research problems.

Bio: Manny Reyes is an Associate Professor of Mathematics at UC Irvine. His research lies at the intersection of several topics including ring theory, noncommutative geometry, category theory, and quantum foundations. After studying at Westmont College and UC Berkeley, he was a postdoctoral fellow at UC San Diego and a faculty member for several years at Bowdoin College before returning to California. He strives to teach in a way that invites anyone to discover a passion for mathematics, regardless of their past experiences.

From ICON to GenICON: In-Context Operator Learning with Uncertainty Quantification (Siting Liu, UCR)
2026-04-27 16:15 - 17:15 | Emmy Noether Room, Estella 1021, Pomona College,, 610 N. College Ave., Claremont, CA, 91711, United States
Abstract: I will introduce In-Context Operator Networks (ICON), a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at inference time, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for ODEs, PDEs, and mean-field control. I will then present a probabilistic interpretation: under a random differential equation data model, ICON implicitly computes the posterior predictive mean given the context, linking operator learning to Bayesian inference. This motivates GenICON, a generative variant that samples from the posterior predictive for principled uncertainty quantification, yielding a unified Bayesian view of in-context operator learning.

Coordinate ring of the universal centralizer via Demazure operators (Tom Gannon, UCR)
2026-04-28 12:15 - 13:10 | Estella 2099
One of the key objects used in Ngo’s proof of the fundamental lemma is the group scheme of universal centralizers associated to a split reductive group G. In this talk, we’ll discuss forthcoming work, joint with Victor Ginzburg, which describes the coordinate ring of the group scheme of universal centralizers in terms of the root datum of G using Demazure (or divided difference) operators. We will then discuss how our result follows from a more general computation on Weil restriction and, time permitting, we will discuss a potential generalization of our result to Coulomb branches.

CCMS Colloquium: Andrew Fiss (Michigan Technological University)
2026-05-01 11:00 - 12:15 | Davidson Lecture Hall, CMC, 340 E 9th St, Claremont, CA, 91711, United States
CCMS Colloquium invites you to a talk by Andrew Fiss (Michigan Technological University)

Title: “Singing American Math: College Traditions from Book Burnings to Observatory Parties, 1880s-1920s”

Abstract: “Singing Math” is a practice that linked American colleges of the late nineteenth and early twentieth centuries. A part of broader college singing traditions, it stood apart because of its subject matter: mathematical sciences. Noting how math songs were sung outdoors, in theaters, and in observatories, this talk explores stories of textbook burials at Yale and Ohio Wesleyan; theatrical productions at MIT, Purdue, and the Michigan College of Mines; and observatory parties at Vassar Observatory, Lick Observatory, and Harvard Observatory. Overall, it argues that math songs are a form of technical communication, one that enjoyed large reach because of its multiple meanings and varied practices.

Bio: Andrew Fiss is associate professor of technical communication at Michigan Technological University. With an undergraduate degree in mathematics from Vassar College and graduate degrees in history and philosophy of science from Indiana University, he works in history of math, technical communication, and STS, and his book Performing Math: A History of Communication and Anxiety in the American Mathematics Classroom (2021) won the 2023 Best Book Award in Technical or Scientific Communication from the Conference on College Composition & Communication.

An Exact Algorithm for the Unanimous Vote Problem (Feyza Duman Keles, NYU)
2026-05-01 16:15 - 17:15 | Emmy Noether Room, Estella 1021, Pomona College,, 610 N. College Ave., Claremont, CA, 91711, United States
Abstract: Consider n independent, biased coins, each with a known probability of heads. Presented with an ordering of these coins, flip (i.e., toss) each coin once, in that order, until we have observed both a head and a tail, or flipped all coins. The Unanimous Vote problem asks us to find the ordering that minimizes the expected number of flips. Gkenosis et al. [arXiv:1806.10660] gave a polynomial-time approximation algorithm for this problem, where the approximation ratio is the golden ratio. They left open whether the problem was NP-hard. We answer this question by giving an exact algorithm that runs in time O(n log n). The Unanimous Vote problem is an instance of the more general Stochastic Boolean Function Evaluation problem: it thus becomes one of the only such problems known to be solvable in polynomial time. Our proof uses simple interchange arguments to show that the optimal ordering must be close to the ordering produced by a natural greedy algorithm. Beyond our main result, we compare the optimal ordering with the best adaptive strategy, proving a tight adaptivity gap of 1.2 + o(1) for the Unanimous Vote problem.

Voting on relations using pairs information (Michael Orrison, HMC)
2026-05-05 12:15 - 13:10 | Estella 2099
Many aggregation problems ask us to turn individual judgments into a single collective outcome. In this talk, we model each voter’s input as a relation on a set of alternatives, allowing pairwise comparisons to include strict preferences, ties, or incomparability. This perspective gives a common framework for median procedures and scoring methods, including several familiar voting rules. The distance-based side leads naturally to graphs such as the hypercube of relations, while the scoring-based side leads to questions about linear operators on functions on relations. At the center is a natural four-parameter family of scoring matrices whose eigenspace decompositions separate meaningful types of pairwise information, connecting voting theory with graph theory, linear algebra, and harmonic analysis.
This is joint work with Karl-Dieter Crisman, Erin McNicholas, and Kathryn Nyman.

Unraveling Hidden Patterns with Topological Data Analysis (Dr. Elena Wang, University of Fribourg, Switzerland)
2026-05-19 11:30 - 12:30 | Estella 2099, Pomona College, 610 N. College Ave., Claremont, CA, United States
Topology, akin to geometry, delves into the study of the shapes and structures of mathematical spaces, ranging from simple surfaces to intricate collections of functions and objects. In recent years, these foundational concepts have extended beyond pure mathematics to address practical problems in data science, influencing diverse areas such as chemistry, neuroscience, and robotics. This talk aims to demystify the core techniques of Topological Data Analysis (TDA), such as persistent homology, and illustrate their significance through applications in shape analysis, machine learning, and computational biology. We will explore how TDA provides unique insights into data structuring and analysis, offering solutions to complex problems.
Pizza will be served.

ANTC talk -- Michelle Manes (American Institute of Mathematics)
2026-09-08 12:15 - 13:10 | TBA
