Student Seminars will be held on Thursdays in Bailey 207 (unless otherwise noted) 12:50pm, during common hour throughout Winter '26 term. We will have a pizza lunch at 12:30pm in Bailey 204.
Thursday, February 12th:
Title: Orthogonal Polynomials in Quantum Computing
Abstract: Perfect state transfer is a phenomenon in quantum information theory in which a quantum state placed at one location in a network is transmitted to another location with probability one. Surprisingly, the mathematics underlying this physical process is deeply connected to classical objects from analysis: orthogonal polynomials. In this talk, we will explore how properties of orthogonal polynomials can be used to determine whether perfect state transfer occurs in quantum spin chains. I will introduce the basics of both quantum computing and orthogonal polynomials, so curiosity is the only thing necessary to attend this talk.
February 5th:
Title: Enclose the Horse or The Isoperimetric Inequality for Plane Curves
Abstract: Suppose we have a fixed amount of fencing available to enclose a region of land for a horse. What would be the optimal shape of the region if our goal is to get the maximum amount of area for the horse? If we have to stick to rectangular shapes, we can use differential calculus methods to show that a square would be optimal. But a quick calculation shows that a circle would be even better. If we get to choose any shape we want, is the circle the best we can do? In this talk we will give a classical proof that this is in fact the case (with some small mathematical fine print assumptions).
The proof (due to E. Schmidt (1939)) is simple and elegant and uses the very powerful Green's Theorem from multivariable integral calculus.
Thursday, January 29th:
TITLE: Geometry and Our Universe
Abstract: At the start of the 20th century, Einstein developed his theory of general relativity in an effort to better model gravitation. He gave us what we now know as the Einstein equations, which describe the interaction between the matter and curvature of our space and time, or space-time. In short, the theory says that matter is responsible for bending space-time, and, in turn, this bending affects how matter moves through it. In other words, what we feel as gravitation is simply the desire of matter to move along straight lines in a space-time that has curvature. But how do we make sense of a straight line if our space-time is curved? These curves, which are the natural analogues of straight lines, are called geodesics. The goal of this talk is to explore these two fundamental concepts of differential geometry: geodesics and curvature.
Thursday, January 22nd:
TITLE: Orthogonal Decomposition and the Theory of Voting
ABSTRACT: Remember physics? Given a block on an inclined plane, we can break the vector force F G of gravity on the block into two parts:
with F perpendicular to the plane and F parallel to the plane. This orthogonal decomposition has great explanatory power – predicting whether the block will slide, for example.
The record of all votes cast in an election is also a vector. Orthogonal decomposition can explain why two different voting rules choose different winners for certain elections, yet agree on others. We will use this method to show that two famous voting rules – which first appear to be completely different – actually aggregate votes in the same way, differing only because they pay attention to different subspaces of information.
Thursday, January 15th:
TITLE: When Probability Meets Functional Analysis (and Vice Versa)
ABSTRACT: This talk explores the rich interplay between probability theory and functional analysis through accessible examples and applications, aiming to provide a broad perspective for undergraduate students. We begin with a classical problem in functional analysis: understanding the space of continuous functions on an interval, C[ab], equipped with the uniform norm. Using tools from elementary probability, especially the binomial distribution, we show that the set of polynomials is dense in C[ab]. This leads to a simple and elegant proof that C[ab] is separable. Next, we reverse the perspective and apply functional analytic ideas to probability. We de ne the conditional expectation of a random variable using projections in Hilbert space, a technique grounded in functional analysis. We then present two important applications of conditional expectation: (a) The Levy-Ito decomposition, which describes the structure of sample paths of Levy processes, random processes with independent and stationary increments. (b) The construction of stochastic integrals for continuous semimartingales, including Brownian motion. Using this stochastic integrals, we introduce stochastic dynamical systems driven by Levy processes and analyze exit time problems in systems perturbed by both Brownian motion and jump processes.