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. 6
- 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:
- Zoom
- Cost:
- Free
- Contact:
- Vera Ioudina
vi11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Assessing hidden states in Basketball Player athletic outcomes and training
Rasim Musal
Texas State University
Abstract: This seminar addresses the challenge of estimating the hidden states of athletes within a
"small n, large p" framework characterized by weak signal to noise ratios. We begin with an
introduction to Hidden Markov Models (HMMs) and the probabilistic foundations underlying filtering
and smoothing calculations. We proceed to evaluate three parameter estimation and state inference
techniques, comparing simulation outcomes across models with known parameters, those estimated
via the Baum-Welch algorithm, and those that fit using a fully Bayesian approach. Finally, we apply this
Bayesian framework to data from the Texas State University women’s basketball team to generate state
predictions and draw parameter inferences regarding player training and athletic performance. Click here for more information
more about event
Rasim Musal
Texas State University
Abstract: This seminar addresses the challenge of estimating the hidden states of athletes within a
"small n, large p" framework characterized by weak signal to noise ratios. We begin with an
introduction to Hidden Markov Models (HMMs) and the probabilistic foundations underlying filtering
and smoothing calculations. We proceed to evaluate three parameter estimation and state inference
techniques, comparing simulation outcomes across models with known parameters, those estimated
via the Baum-Welch algorithm, and those that fit using a fully Bayesian approach. Finally, we apply this
Bayesian framework to data from the Texas State University women’s basketball team to generate state
predictions and draw parameter inferences regarding player training and athletic performance. Click here for more information
Talk Math 2 Me
–
until Nov. 6
- 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 a mini course on radial basis functions. 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. 6
- 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 Only
- Cost:
- Free
- Contact:
- Xiaoxi Shen
rcd67@txstate.edu
Title: Proximal Transport Divergences for Robust Generative Modeling
Abstract: Generative modeling involves finding mappings from simple to sample reference distributions to complex target distributions. In practical application, these target distributions may be quite singular or may not have a density at all, making finding generative flows for them difficult. Proximal optimal transport (OT) divergences are a novel class of discrepancy measures that interpolate between information divergences and OT distances. These divergences provide a principled foundation for the proximal optimization methods commonly used in generative modeling. We establish that these divergences possess key mathematical properties: smoothness, boundedness, and computational tractability. We then demonstrate that generative flows trained with these divergences are well-posed with desirable regularity properties, making them particularly effective for learning distributions on low-dimensional manifolds while maintaining stable and robust training dynamics. Finally, we apply these robust generative models to likelihood-free inference for generating samples from Bayesian posteriors supported on low-dimensional manifolds. Click here for more information
more about event
Abstract: Generative modeling involves finding mappings from simple to sample reference distributions to complex target distributions. In practical application, these target distributions may be quite singular or may not have a density at all, making finding generative flows for them difficult. Proximal optimal transport (OT) divergences are a novel class of discrepancy measures that interpolate between information divergences and OT distances. These divergences provide a principled foundation for the proximal optimization methods commonly used in generative modeling. We establish that these divergences possess key mathematical properties: smoothness, boundedness, and computational tractability. We then demonstrate that generative flows trained with these divergences are well-posed with desirable regularity properties, making them particularly effective for learning distributions on low-dimensional manifolds while maintaining stable and robust training dynamics. Finally, we apply these robust generative models to likelihood-free inference for generating samples from Bayesian posteriors supported on low-dimensional manifolds. Click here for more information
- Location:
- DERR 329; 329
- Cost:
- Free
- Contact:
- Anton Dochtermann
dochtermann@txstate.edu - Campus Sponsor:
- Department of Mathematics
Title: Helly type problems in group theory
ABSTRACT
Helly proved in 1913 that if one has (finitely many but) at least d+1 convex sets in d-dimensionalEuclidean space and knows that any d+1 of them have a common point, then all of them have acommon point. Also d+1 is minimal with this property. Thus the "Helly number of convex sets" ind-dimensional Euclidean space is d+1. This was one of the first results in the geometric transversaltheory subfield of discrete geometry (others being the theorems of Caratheodory and Radon), andthe study of Helly type problems in different settings is still a very active topic.
In this talk, which is based on joint work with Philip Hackney, we look at some Helly type problems inand around finite group theory. For example, we consider Helly numbers where "convex sets" isreplaced by "intersections of positive systems in a finite root system" and "intersections of Sylowp-subgroups in a finite group", etc. We know the (rather beautiful) precise answers in the formersetting, and have good upper bounds in the latter, although that work is still ongoing. Theseproblems showed up naturally while studying "higher Segal conditions" (in the sense of Dyckerhoffand Kapranov) for "partial groups" (in the sense of Chermak), and I plan to say a little bit about this connection. Click here for more information
more about event
ABSTRACT
Helly proved in 1913 that if one has (finitely many but) at least d+1 convex sets in d-dimensionalEuclidean space and knows that any d+1 of them have a common point, then all of them have acommon point. Also d+1 is minimal with this property. Thus the "Helly number of convex sets" ind-dimensional Euclidean space is d+1. This was one of the first results in the geometric transversaltheory subfield of discrete geometry (others being the theorems of Caratheodory and Radon), andthe study of Helly type problems in different settings is still a very active topic.
In this talk, which is based on joint work with Philip Hackney, we look at some Helly type problems inand around finite group theory. For example, we consider Helly numbers where "convex sets" isreplaced by "intersections of positive systems in a finite root system" and "intersections of Sylowp-subgroups in a finite group", etc. We know the (rather beautiful) precise answers in the formersetting, and have good upper bounds in the latter, although that work is still ongoing. Theseproblems showed up naturally while studying "higher Segal conditions" (in the sense of Dyckerhoffand Kapranov) for "partial groups" (in the sense of Chermak), and I plan to say a little bit about this connection. Click here for more information
Math CATS Tutoring
–
until Nov. 8
- 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. 11
- 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:
- HINE 201; 201
- Cost:
- Free
- Contact:
- Geonhee Cho
gunheecho@txstate.edu
Sungjoon Cho - Korea University
Towards Human-Centered Robotics: Connecting Robots to Humans and Humans to Robots
Abstract: Human-centered robotics seeks to create robots that bring meaningful value to people’s lives while making robotic capabilities easier for people to access and use. This talk explores two complementary directions: connecting robots to humans through natural, expressive, and engaging interaction, and connecting humans to robots through intuitive ways to guide, control, and adapt robot behavior. In the first direction, I will present research on expressive humanoid motion, persona-based interaction, and playful social robots, examining how robots can communicate with people and create meaningful social experiences. In the second, I will discuss motion generation, flexible teleoperation, and system integration as ways to lower the barriers to using robots and put their capabilities in people’s hands. Drawing on projects from our laboratory, I will show how these two directions inform one another, bringing human experience and practical usability into the design of robot intelligence and behavior.
Towards Human-Centered Robotics: Connecting Robots to Humans and Humans to Robots
Abstract: Human-centered robotics seeks to create robots that bring meaningful value to people’s lives while making robotic capabilities easier for people to access and use. This talk explores two complementary directions: connecting robots to humans through natural, expressive, and engaging interaction, and connecting humans to robots through intuitive ways to guide, control, and adapt robot behavior. In the first direction, I will present research on expressive humanoid motion, persona-based interaction, and playful social robots, examining how robots can communicate with people and create meaningful social experiences. In the second, I will discuss motion generation, flexible teleoperation, and system integration as ways to lower the barriers to using robots and put their capabilities in people’s hands. Drawing on projects from our laboratory, I will show how these two directions inform one another, bringing human experience and practical usability into the design of robot intelligence and behavior.
Problem Solvers
–
until Nov. 10
- 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!