Explore the scholarships available through the Department of Mathematics, review the eligibility requirements and apply by the listed deadlines. Your future in mathematics starts with the work you do today.
The application period begins November 1.
Scholarships Available
Student Focused
We have a wide offering of general education courses designed to prepare you to major in Business and STEM (Science, Technology, Engineering, and Mathematics) fields.
Current Faculty Research
Math Happenings
Promotion from Associate Professor to Professor
Suho Oh
Promotion from Assistant Professor to Associate Professor & Tenure
Hamilton Hardison and Cody Patterson
Promotion from Associate Professor of Instruction to Professor of Instruction
Bikai Nie and Amanda Walker
Promotion from Assistant Professor of Instruction to Associate Professor of Instruction
Pritha Chakraborty, Jaroslaw Jaracz and Jackson Rebrovich
Promotion from Lecturer to Assistant Professor of Instruction
Hyun Chul Jang, Yichen Ma, Amy Lawrence-Wallquist, and Le Tran
@TXST Math
Upcoming Events
Math CATS Tutoring
–
until Nov. 17
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event
Math CATS Tutoring
–
until Nov. 13
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event
- Location:
- DERR 328; 328
- Cost:
- Free
- Contact:
- Geonhee Cho
gunheecho@txstate.edu
Geonhee Cho - TXST
A Stochastic Approach to Complex Geometry
Abstract: Continuing last week’s introduction, we will explain how prescribing the Ricci curvature of a Kähler metric leads to the complex Monge–Ampère equation. We will review the role of Kähler potentials and the determinant of the metric matrix, making explicit the passage from a geometric problem to a nonlinear partial differential equation.
We will then discuss an ongoing research direction of the TXST Geometry–Probability group: approaching the complex Monge–Ampère equation through a Hamilton–Jacobi–Bellman (HJB) formulation. HJB equations connect nonlinear PDEs with stochastic optimal control and provide a continuous-time foundation for reinforcement learning. We will explore how this perspective introduces stochastic processes and optimization into the study of Kähler metrics, and discuss the potential conceptual and computational advantages, together with the challenges that remain.
This talk builds on last week’s seminar and provides further background for the November 20 colloquium. The necessary geometric ideas will be briefly reviewed; no prior knowledge of stochastic control or reinforcement learning is assumed.
A Stochastic Approach to Complex Geometry
Abstract: Continuing last week’s introduction, we will explain how prescribing the Ricci curvature of a Kähler metric leads to the complex Monge–Ampère equation. We will review the role of Kähler potentials and the determinant of the metric matrix, making explicit the passage from a geometric problem to a nonlinear partial differential equation.
We will then discuss an ongoing research direction of the TXST Geometry–Probability group: approaching the complex Monge–Ampère equation through a Hamilton–Jacobi–Bellman (HJB) formulation. HJB equations connect nonlinear PDEs with stochastic optimal control and provide a continuous-time foundation for reinforcement learning. We will explore how this perspective introduces stochastic processes and optimization into the study of Kähler metrics, and discuss the potential conceptual and computational advantages, together with the challenges that remain.
This talk builds on last week’s seminar and provides further background for the November 20 colloquium. The necessary geometric ideas will be briefly reviewed; no prior knowledge of stochastic control or reinforcement learning is assumed.
- Location:
- DERR 333; 333
- Cost:
- Free
- Contact:
- Vera Ioudina
vi11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Graduate Student Talk
Duck vs Data: Investigating the Production and Adaptation of Arguments Regarding Civic Issues
Cindy Cattey
Texas State University - PhD Student
Abstract: I investigate how people build and share arguments related to social issues, as well as the adjustments they make to their arguments. Building from Weiland’s (2017; 2022) theoretical writings, I advance the production-oriented perspective of Critical Statistical Literacy with empirical exploration of arguments in response to sociopolitical prompts. Using frameworks I developed to characterize evidence used and overall argument types, I report on the argumentation of five people of diverse personal and statistical backgrounds by describing (a) types of evidence used in arguments, (b) argument types employed, and (c) changes people made to their arguments. I found that not-traditionally-statistical evidence can be leveraged in productive ways and that there are many ways people may adapt their arguments in response to changing contexts. Click here for more information
more about event
Duck vs Data: Investigating the Production and Adaptation of Arguments Regarding Civic Issues
Cindy Cattey
Texas State University - PhD Student
Abstract: I investigate how people build and share arguments related to social issues, as well as the adjustments they make to their arguments. Building from Weiland’s (2017; 2022) theoretical writings, I advance the production-oriented perspective of Critical Statistical Literacy with empirical exploration of arguments in response to sociopolitical prompts. Using frameworks I developed to characterize evidence used and overall argument types, I report on the argumentation of five people of diverse personal and statistical backgrounds by describing (a) types of evidence used in arguments, (b) argument types employed, and (c) changes people made to their arguments. I found that not-traditionally-statistical evidence can be leveraged in productive ways and that there are many ways people may adapt their arguments in response to changing contexts. Click here for more information
Talk Math 2 Me
–
until Nov. 13
- Location:
- DERR 329; 329
- Cost:
- Free
- Contact:
- Ellen Couvillion
ebr21@txstate.edu - Campus Sponsor:
- Department of Mathematics
TalkMath2Me is a seminar for students and by students organized by Pi Mu Epsilon. Talk Math 2 Me provides students, both graduate and undergraduate, an opportunity to present anything related to mathematics to an audience of their peers. The talks can be 15-90 minutes long. Past topics have included talks on impressive student research, famous mathematicians, sports, puzzles and games, and time travel!
Click here for more information
more about event
- Location:
- DERR 330; 330
- Cost:
- Free
- Contact:
- Ray Treinen
treinen@txstate.edu - Campus Sponsor:
- Department of Mathematics
Ray Treinen will continue our minicourse on radial basis functions. After our time in the book by Fornberg and Flyer, we are now moving on to the paper by Tominec, Larsson, and Heryudono from 2021.
We will have some theoretical results, but the primary goal of this mini course is to develop an understanding of how to use these functions in an interpolation scheme suitable for approximating solutions to partial differential equations.
We will have some theoretical results, but the primary goal of this mini course is to develop an understanding of how to use these functions in an interpolation scheme suitable for approximating solutions to partial differential equations.
Cohomology Learning Seminar
–
until Nov. 13
- Location:
- DERR 336; 336
- Cost:
- Free
- Contact:
- Logan Greenland
logan.greenland@txstate.edu - Campus Sponsor:
- Department of Mathematics
This is a learning seminar intended for discussion and questions throughout, and no prior knowledge is assumed!
- Location:
- Online via Zoom
- Cost:
- Free
- Contact:
- Xiaoxi Shen
rcd67@txstate.edu - Campus Sponsor:
- Department of Mathematics
Dr. Thejani Gamage - University of Massachusetts Amherst
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Abstract: In many high-stakes domains, extreme events carry substantial consequences, yet learning the heavy-tailed distributions that govern them from finite samples remains challenging: the quantities of interest are driven by a few extreme observations, so the tail region is under-sampled relative to its importance. Even generative models tailored for heavy tails capture the tail region inadequately in practice. We propose the Conditional Value-at-Risk (CVaR)-penalized Generative Particle Algorithm (CVaR-GPA), a tail-agnostic algorithm for fine-tuning generative models toward heavy-tailed targets, built as a time discretization of the Wasserstein gradient flow of the Lipschitz-regularized KL divergence penalized by a CVaR discrepancy term. Such a flow can be initialized from the output samples of the pre-trained model, which are then transported along the gradient descent of the loss functional, without requiring access to the pre-trained model's internal architecture. The Lipschitz-regularized KL divergence requires minimal assumptions on the target, while the CVaR penalty focuses the flow on the tail. The CVaR penalty depends on the target only through a scalar tail statistic, inducing a velocity field that remains active in the under-sampled tail region at a dimension-free estimation cost. The resulting velocity field, and hence the depth of the transport map, implicitly adapts to the target distribution, without any target-specific modifications. Across four targets, including two real-world, high-dimensional datasets (daily streamflow in the Ohio River basin ) and the Fama–French portfolios with tail indices ranging from 1.05 to 3.34, fine-tuning with CVaR-GPA reduces global and tail errors by geometric-mean factors of 14 times and 9.8 times, respectively, across seven pre-trained models spanning GANs, diffusion models, and other generative flows, with a single set of hyperparameters. Click here for more information
more about event
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Abstract: In many high-stakes domains, extreme events carry substantial consequences, yet learning the heavy-tailed distributions that govern them from finite samples remains challenging: the quantities of interest are driven by a few extreme observations, so the tail region is under-sampled relative to its importance. Even generative models tailored for heavy tails capture the tail region inadequately in practice. We propose the Conditional Value-at-Risk (CVaR)-penalized Generative Particle Algorithm (CVaR-GPA), a tail-agnostic algorithm for fine-tuning generative models toward heavy-tailed targets, built as a time discretization of the Wasserstein gradient flow of the Lipschitz-regularized KL divergence penalized by a CVaR discrepancy term. Such a flow can be initialized from the output samples of the pre-trained model, which are then transported along the gradient descent of the loss functional, without requiring access to the pre-trained model's internal architecture. The Lipschitz-regularized KL divergence requires minimal assumptions on the target, while the CVaR penalty focuses the flow on the tail. The CVaR penalty depends on the target only through a scalar tail statistic, inducing a velocity field that remains active in the under-sampled tail region at a dimension-free estimation cost. The resulting velocity field, and hence the depth of the transport map, implicitly adapts to the target distribution, without any target-specific modifications. Across four targets, including two real-world, high-dimensional datasets (daily streamflow in the Ohio River basin ) and the Fama–French portfolios with tail indices ranging from 1.05 to 3.34, fine-tuning with CVaR-GPA reduces global and tail errors by geometric-mean factors of 14 times and 9.8 times, respectively, across seven pre-trained models spanning GANs, diffusion models, and other generative flows, with a single set of hyperparameters. Click here for more information
Math CATS Tutoring
–
until Nov. 15
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event
Problem Solvers
–
until Nov. 17
- Location:
- DERR 336; 336
- Cost:
- Free
- Contact:
- Cameron Farnsworth
clf129@txstate.edu - Campus Sponsor:
- Department of Mathematics
Love a good problem? Like to solve difficult puzzles?
Join professors, graduate students and undergraduates as we tackle problems presented from several mathematical journals. An interest in higher level mathematics is all that is required to join our round table. Offer what you know, learn what you don't in a relaxed environment with some of our department's finest!
Join professors, graduate students and undergraduates as we tackle problems presented from several mathematical journals. An interest in higher level mathematics is all that is required to join our round table. Offer what you know, learn what you don't in a relaxed environment with some of our department's finest!