Join Math Club in celebrating Pi Day in the Quad. Come buy pie OR pie a professor in the face!
Thursday, March 12
9 am - 4 pm
decorative image
Pi Day is almost here!
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.
In Active Grants
Discover our dynamic faculty and the innovative research they’re driving through, high-impact grant awards.
NEXT in Math
Promotion from Assistant Professor to Associate Professor & Tenure
Hiroaki Tanaka
Promotion from Associate Professor to Professor
Kathleen Melhuish and Hiroko Warshauer
Dr. Susan Morey Receives the University Distinguished Professor Award
This award honors individuals whose performance in teaching, research, and service has been exemplary and recognized at the state, national, and international levels.
@TXST Math
- Location:
- Quad
- Cost:
- Free
- Contact:
- Cameron Farnsworth
clf129@txstate.edu - Campus Sponsor:
- Math Club
Join Math Club in celebrating Pi Day in the Quad. Come buy pie or pie a professor in the face! Thursday, March 12 from 9am til 4pm. @txstmathclub #piday #math #pie #pi
- Location:
- DERR 330; 330
- Cost:
- Free
- Contact:
- Jackson Rebrovich (jdr134@txstate.edu)
The Math Club is a student-led organization for anyone who enjoys problem-solving, logical thinking, and exploring math beyond the classroom. Whether you love tackling challenging puzzles, preparing for competitions, or just want to sharpen your skills with friends, our club offers a fun and supportive environment to learn, collaborate, and grow. No matter your experience level, curiosity is all you need—come think, solve, and discover with us!
Our theme for the semester will be:
"Learn Math with AI'' Click here for more information
more about event
Our theme for the semester will be:
"Learn Math with AI'' Click here for more information
- Location:
- DERR 336; 336
- Cost:
- Free
- Contact:
- Christine Lee
vne11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Tye Lidman from North Carolina State University
Title: Knots and their complements
Abstract: One way to study a knot in three-dimensional space is by looking at the topology of the complement. For knots in the three-sphere, Gordon and Luecke proved that the topology of the knot complement completely determines the knot. We will discuss generalizations of this idea to some other settings. Click here for more information
more about event
Title: Knots and their complements
Abstract: One way to study a knot in three-dimensional space is by looking at the topology of the complement. For knots in the three-sphere, Gordon and Luecke proved that the topology of the knot complement completely determines the knot. We will discuss generalizations of this idea to some other settings. Click here for more information
- Location:
- DERR 338; 338
- Cost:
- Free
- Contact:
- Hamilton Hardison
hhardison@txstate.edu - Campus Sponsor:
- Department of Mathematics
Faculty and students learn about and discuss issues related to the teaching and learning of mathematics and new findings in mathematics education research. Graduate students in the Mathematics Education programs are expected to attend whenever possible; students from other programs are always welcome.
Click here for more information
more about event
- Location:
- DERR 121; 121
- Cost:
- Free
- Contact:
- Cameron Farnsworth
cfarnsworth@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! Click here for more information
more about event
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! Click here for more information
- Location:
- DERR 333; 333
- Cost:
- Free
- Contact:
- Vera Ioudina
vi11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Battle of the Bands: Using R Shiny to Disentangle Contributions in Indie Rock Collaborations
William Cipolli
Colgate University
Abstract: The Data Science Collaboratory at Colgate University addresses the gap in statistical research support common at smaller institutions. To empower instructors, students, and the broader research community, we developed R Shiny web applications that facilitate technically sound analyses paired with foundational learning materials and case studies. We will discuss one such case study where we analyze the music and lyrics of three collaborating indie rock bands to understand their individual contributions, including data collection, ethical considerations, and results that align with publicly available information.
Bio: Dr. Cipolli is an Associate Professor of Mathematics at Colgate University. He specializes in Bayesian nonparametric statistics and develops flexible approaches to modern statistical problems. As the cofounder of Colgate's Data Science Collaboratory, Will collaborates on diverse projects across Biology, Psychology, and Sociology, and he is a lead developer of R Shiny applications for both statistical analysis and education.
Here is the Zoom link for those who cannot attend in person:
https://txstate.zoom.us/j/84190833370?pwd=OzF6cbIZGLqT2fBnUGN8qQwCHSidVf.1
Meeting ID: 84190833370 Passcode: SS_Derr333 Click here for more information
more about event
William Cipolli
Colgate University
Abstract: The Data Science Collaboratory at Colgate University addresses the gap in statistical research support common at smaller institutions. To empower instructors, students, and the broader research community, we developed R Shiny web applications that facilitate technically sound analyses paired with foundational learning materials and case studies. We will discuss one such case study where we analyze the music and lyrics of three collaborating indie rock bands to understand their individual contributions, including data collection, ethical considerations, and results that align with publicly available information.
Bio: Dr. Cipolli is an Associate Professor of Mathematics at Colgate University. He specializes in Bayesian nonparametric statistics and develops flexible approaches to modern statistical problems. As the cofounder of Colgate's Data Science Collaboratory, Will collaborates on diverse projects across Biology, Psychology, and Sociology, and he is a lead developer of R Shiny applications for both statistical analysis and education.
Here is the Zoom link for those who cannot attend in person:
https://txstate.zoom.us/j/84190833370?pwd=OzF6cbIZGLqT2fBnUGN8qQwCHSidVf.1
Meeting ID: 84190833370 Passcode: SS_Derr333 Click here for more information
- Location:
- DERR 336; 336
- Cost:
- Free
- Contact:
- Christine RS Lee
vne11@txstate.edu - Campus Sponsor:
- Department of Mathematics
Rene Cabrera from the University of Texas at Austin
Title. GAN: Dynamics and Mode Collapse
Abstract. Generative Adversarial Networks (GANs) were among the first machine learning algorithms capable of producing remarkably realistic synthetic data. In this talk, we focus on a simplified, toy setting that exposes the core mechanics of the GAN algorithm and its connection to optimal transport theory. We show how, in shallow architectures, GAN training can be interpreted as an approximation of an underlying system of partial differential equations. Within this reduced framework, we analyze a concrete toy example that exhibits pathological behavior analogous to mode collapse. This example illustrates how the PDE viewpoint clarifies the dynamical mechanisms responsible for non-convergence and instability in GAN training.
Title. GAN: Dynamics and Mode Collapse
Abstract. Generative Adversarial Networks (GANs) were among the first machine learning algorithms capable of producing remarkably realistic synthetic data. In this talk, we focus on a simplified, toy setting that exposes the core mechanics of the GAN algorithm and its connection to optimal transport theory. We show how, in shallow architectures, GAN training can be interpreted as an approximation of an underlying system of partial differential equations. Within this reduced framework, we analyze a concrete toy example that exhibits pathological behavior analogous to mode collapse. This example illustrates how the PDE viewpoint clarifies the dynamical mechanisms responsible for non-convergence and instability in GAN training.