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.
Student Focused
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. 2
- 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 Oct. 30
- 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
Hyun Chul Jang - TXST
From Bell Curves to Circles: The Geometry of Probability
Abstract: How far apart are two probability distributions? This question connects learning from data with geometry, topology, and analysis on spaces of probability distributions. This talk grew out of a research project at the Honors Summer Math Camp (HSMC), organized by Mathworks at Texas State University, where we compared two ways of introducing periodicity into a Gaussian model.
Beginning with simple examples, we introduce the Fisher metric as a way to measure changes in a statistical model. We then wrap a Gaussian bell curve around a circle and ask what happens as it spreads toward the uniform distribution. The two periodic constructions lead to strikingly different geometries: a smooth cap and an infinitely long cusp. Building on that initial comparison, we explore geometric limits and move from one angle to several. Independent coordinates preserve the smooth picture, while allowing correlations creates a completion that can no longer be described by a finite-dimensional manifold.
The talk draws on joint work with Geonhee Cho (arXiv:2610.37526) and emphasizes pictures, examples, and geometric intuition. No prior knowledge of information geometry is assumed.
Faculty, graduate students, and interested undergraduate students are all welcome!
From Bell Curves to Circles: The Geometry of Probability
Abstract: How far apart are two probability distributions? This question connects learning from data with geometry, topology, and analysis on spaces of probability distributions. This talk grew out of a research project at the Honors Summer Math Camp (HSMC), organized by Mathworks at Texas State University, where we compared two ways of introducing periodicity into a Gaussian model.
Beginning with simple examples, we introduce the Fisher metric as a way to measure changes in a statistical model. We then wrap a Gaussian bell curve around a circle and ask what happens as it spreads toward the uniform distribution. The two periodic constructions lead to strikingly different geometries: a smooth cap and an infinitely long cusp. Building on that initial comparison, we explore geometric limits and move from one angle to several. Independent coordinates preserve the smooth picture, while allowing correlations creates a completion that can no longer be described by a finite-dimensional manifold.
The talk draws on joint work with Geonhee Cho (arXiv:2610.37526) and emphasizes pictures, examples, and geometric intuition. No prior knowledge of information geometry is assumed.
Faculty, graduate students, and interested undergraduate students are all welcome!
- Location:
- Zoom
- Cost:
- Free
- Contact:
- Vera Ioudina
vi11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Rethinking Project-Based Learning in Statistics in the Age of Generative AI: From Written Reports to Interactive R Shiny Applications
Luai Al Labadi
University of Toronto Mississauga
Abstract: Project-based learning can provide statistics students with opportunities to design studies, collect and clean data, analyze data using statistical software, collaborate with others, and communicate their findings. These are skills that are difficult to assess fully through traditional tests and examinations.
In this talk, I will describe how project-based learning has developed across two upper-year statistics courses at the University of Toronto Mississauga. In the initial implementation, generative AI tools were prohibited, yet a substantial number of students reported using tools such as ChatGPT. In a later offering of Experimental Design, we permitted the use of generative AI while introducing additional support and checkpoints, including an exploratory data analysis checkpoint, a rough draft with detailed feedback, and selective oral examinations to verify students’ understanding when concerns remained about over-reliance on AI or their understanding of the submitted work. I will discuss findings from two studies published in Teaching Statistics, along with practical challenges related to teaching-assistant support, feedback workload, group accountability, and the implementation of project-based learning in relatively large classes.
Finally, I will describe our current redesign of the project in Surveys, Sampling and Observational Data, where the conventional final written report has been replaced by an interactive R Shiny application. The goal is not to make the assessment AI-proof, but rather to make students’ statistical reasoning, interpretation, and communication more visible and to require them to understand and explain the work they produce. Click here for more information
more about event
Luai Al Labadi
University of Toronto Mississauga
Abstract: Project-based learning can provide statistics students with opportunities to design studies, collect and clean data, analyze data using statistical software, collaborate with others, and communicate their findings. These are skills that are difficult to assess fully through traditional tests and examinations.
In this talk, I will describe how project-based learning has developed across two upper-year statistics courses at the University of Toronto Mississauga. In the initial implementation, generative AI tools were prohibited, yet a substantial number of students reported using tools such as ChatGPT. In a later offering of Experimental Design, we permitted the use of generative AI while introducing additional support and checkpoints, including an exploratory data analysis checkpoint, a rough draft with detailed feedback, and selective oral examinations to verify students’ understanding when concerns remained about over-reliance on AI or their understanding of the submitted work. I will discuss findings from two studies published in Teaching Statistics, along with practical challenges related to teaching-assistant support, feedback workload, group accountability, and the implementation of project-based learning in relatively large classes.
Finally, I will describe our current redesign of the project in Surveys, Sampling and Observational Data, where the conventional final written report has been replaced by an interactive R Shiny application. The goal is not to make the assessment AI-proof, but rather to make students’ statistical reasoning, interpretation, and communication more visible and to require them to understand and explain the work they produce. Click here for more information
Talk Math 2 Me
–
until Oct. 30
- 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 331; 331
- Cost:
- Free
- Contact:
- Jian Shen
js48@txstate.edu - Campus Sponsor:
- Department of Mathematics
Title: AI-Assisted Proof of the Δ-Conjecture for CIS d-Graphs
Speaker: Dr. Jian Shen (Your AI Translator)
Location: Derrick Hall 331
Abstract:
Let the edges of a complete graph K_n be colored with d colors from {1,2,…,d}, and for each i let G_i be the graph on the same vertex set formed by the edges of color i. In 1978, Gurvich proposed the following conjecture:
Gurvich’s Δ-Conjecture (1978). If every choice of a maximal independent set S_i of G_i, one for each i∈{1,2,…,d}, has nonempty intersection, then the coloring contains no rainbow triangle. Here a rainbow triangle is a triangle whose edges have three different colors.
In a recent arXiv preprint (August 27, 2026), Liu and Tang presented an AI-assisted proof of this conjecture.
In this talk, I will explain the main ideas of their proof, emphasizing the intuition behind the argument. I will aim to make the presentation self-contained and accessible to faculty and graduate students from different areas of mathematics.
Questions for discussion
What does it mean to understand a proof beyond verifying its correctness?
How should we validate AI-assisted proofs and acknowledge human and AI contributions?
Where can AI contribute most effectively to mathematical research?
How should graduate training respond to AI-assisted discovery and proof formalization?
Speaker: Dr. Jian Shen (Your AI Translator)
Location: Derrick Hall 331
Abstract:
Let the edges of a complete graph K_n be colored with d colors from {1,2,…,d}, and for each i let G_i be the graph on the same vertex set formed by the edges of color i. In 1978, Gurvich proposed the following conjecture:
Gurvich’s Δ-Conjecture (1978). If every choice of a maximal independent set S_i of G_i, one for each i∈{1,2,…,d}, has nonempty intersection, then the coloring contains no rainbow triangle. Here a rainbow triangle is a triangle whose edges have three different colors.
In a recent arXiv preprint (August 27, 2026), Liu and Tang presented an AI-assisted proof of this conjecture.
In this talk, I will explain the main ideas of their proof, emphasizing the intuition behind the argument. I will aim to make the presentation self-contained and accessible to faculty and graduate students from different areas of mathematics.
Questions for discussion
What does it mean to understand a proof beyond verifying its correctness?
How should we validate AI-assisted proofs and acknowledge human and AI contributions?
Where can AI contribute most effectively to mathematical research?
How should graduate training respond to AI-assisted discovery and proof formalization?
Cohomology Learning Seminar
–
until Oct. 30
- 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:
- DERR 338; 338
- Cost:
- Free
- Contact:
- Bea Ellis
bea.ellis@txstate.edu - Campus Sponsor:
- Department of Mathematics
Amy Lawrence-Wallquist (Texas State University)
Pedagogical Stance Scales: Development and Initial Use
Abstract: The Pedagogical Stance Scales (PSS) were created to measure developmental mathematics instructors’ asset- and deficit-based conceptions related to their students and the perceptions they have about developmental mathematics. It was hypothesized that pedagogical stance would predict instructional practice choices and possibly moderate the relationships among epistemological beliefs (about the nature of mathematics and how mathematics is learned) and instructional practices in developmental mathematics contexts. This seminar presentation will discuss how the scale was created, how it was used in the dissertation study, some of the results related to the PSS, and plans for further development. Additionally, attendees will respond to a few of the scale items, and we will examine how our group’s mean scores compare to the means found from the collected data.
Pedagogical Stance Scales: Development and Initial Use
Abstract: The Pedagogical Stance Scales (PSS) were created to measure developmental mathematics instructors’ asset- and deficit-based conceptions related to their students and the perceptions they have about developmental mathematics. It was hypothesized that pedagogical stance would predict instructional practice choices and possibly moderate the relationships among epistemological beliefs (about the nature of mathematics and how mathematics is learned) and instructional practices in developmental mathematics contexts. This seminar presentation will discuss how the scale was created, how it was used in the dissertation study, some of the results related to the PSS, and plans for further development. Additionally, attendees will respond to a few of the scale items, and we will examine how our group’s mean scores compare to the means found from the collected data.
- Location:
- Online via Zoom
- Cost:
- Free
- Contact:
- Xiaoxi Shen
rcd67@txstate.edu - Campus Sponsor:
- Department of Mathematics
Dr. Yiwei Wang - University of California at Riverside
Learning Gradient-Flow Dynamics from Distributional Snapshots: An Energy–Dissipation Perspective
Abstract: Learning dynamics from distributional snapshots arises in stochastic modeling, sampling, and generative modeling, where observations are available at discrete time instances without access to individual particle trajectories. In this talk, we present a variational approach for learning the underlying free energies, driving potentials, and interaction kernels of Fokker–Planck-type dynamics. The key idea is to characterize gradient-flow dynamics through the energy–dissipation structure associated with the Fenchel–Young inequality. This provides a nonnegative energy–dissipation gap that vanishes when the observed evolution follows the gradient flow of a candidate free energy. By exploiting this variational structure, we develop several learning objectives, including squared and unsquared energy–dissipation losses. In particular, the unsquared formulation admits a velocity-free representation, while further integration by parts leads to a score-free loss that avoids both velocity reconstruction and density-score estimation. We investigate the sensitivity of these objectives to errors in velocity and density estimation. Numerical experiments demonstrate robust recovery of driving potentials and interaction kernels from unpaired snapshots, with improvements over direct velocity-based fitting in challenging regimes. Click here for more information
more about event
Learning Gradient-Flow Dynamics from Distributional Snapshots: An Energy–Dissipation Perspective
Abstract: Learning dynamics from distributional snapshots arises in stochastic modeling, sampling, and generative modeling, where observations are available at discrete time instances without access to individual particle trajectories. In this talk, we present a variational approach for learning the underlying free energies, driving potentials, and interaction kernels of Fokker–Planck-type dynamics. The key idea is to characterize gradient-flow dynamics through the energy–dissipation structure associated with the Fenchel–Young inequality. This provides a nonnegative energy–dissipation gap that vanishes when the observed evolution follows the gradient flow of a candidate free energy. By exploiting this variational structure, we develop several learning objectives, including squared and unsquared energy–dissipation losses. In particular, the unsquared formulation admits a velocity-free representation, while further integration by parts leads to a score-free loss that avoids both velocity reconstruction and density-score estimation. We investigate the sensitivity of these objectives to errors in velocity and density estimation. Numerical experiments demonstrate robust recovery of driving potentials and interaction kernels from unpaired snapshots, with improvements over direct velocity-based fitting in challenging regimes. Click here for more information
Math CATS Tutoring
–
until Nov. 1
- 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