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 Sept. 28
- 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 334; 334
- Cost:
- Free
- Contact:
- Vivian Healey
healey@txstate.edu - Campus Sponsor:
- Department of Mathematics
COME LEARN ABOUT MATH EDUCATION RESEARCH AND DR. LEE’S CAREER PATH
Math CATS Tutoring
–
until Sept. 25
- 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
An AI Slop Typology: Embedding Professional Norms for Responsible AI Use in Data Science Courses
Tori Ellison
University of Illinois Urbana-Champaign
Abstract: Since the widespread adoption of generative AI, data science educators have encountered new
and often recognizable patterns in student work, from subtle departures from professional
communication norms to idiosyncratic technical and conceptual errors that differ from the more familiar
mistakes associated with gaps in student understanding. These patterns, and the technical errors and
misalignments with research goals and professional norms that they can produce, can be difficult for
instructors and students alike to identify and articulate, complicating both how educators teach students
to critically evaluate their work and how educators evaluate that work when it is submitted for credit.
In this talk, I introduce an “AI Slop Typology” and accompanying course materials and policies developed
through several years of teaching data science and machine learning. The framework identifies seven
recurring patterns observed in student work and connects each to the professional norms it violates and
the potential consequences of similar work in professional practice. In doing so, the framework helps
students think critically about what responsible, professionally appropriate AI-assisted data science work
should look like.
The framework also offers instructors a potential middle ground course policy between allowing
irresponsible, unchecked AI-like work to go unpenalized and treating AI use primarily as an academic
integrity violation. Predefined, proportional penalties introduce some friction into unchecked AI-assisted
assignment completion while encouraging students to review, verify, and take responsibility for AI-
generated material before submitting it. Click here for more information
more about event
Tori Ellison
University of Illinois Urbana-Champaign
Abstract: Since the widespread adoption of generative AI, data science educators have encountered new
and often recognizable patterns in student work, from subtle departures from professional
communication norms to idiosyncratic technical and conceptual errors that differ from the more familiar
mistakes associated with gaps in student understanding. These patterns, and the technical errors and
misalignments with research goals and professional norms that they can produce, can be difficult for
instructors and students alike to identify and articulate, complicating both how educators teach students
to critically evaluate their work and how educators evaluate that work when it is submitted for credit.
In this talk, I introduce an “AI Slop Typology” and accompanying course materials and policies developed
through several years of teaching data science and machine learning. The framework identifies seven
recurring patterns observed in student work and connects each to the professional norms it violates and
the potential consequences of similar work in professional practice. In doing so, the framework helps
students think critically about what responsible, professionally appropriate AI-assisted data science work
should look like.
The framework also offers instructors a potential middle ground course policy between allowing
irresponsible, unchecked AI-like work to go unpenalized and treating AI use primarily as an academic
integrity violation. Predefined, proportional penalties introduce some friction into unchecked AI-assisted
assignment completion while encouraging students to review, verify, and take responsibility for AI-
generated material before submitting it. Click here for more information
Talk Math 2 Me
–
until Sept. 25
- 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
Cohomology Learning Seminar
–
until Sept. 25
- 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 333; 333
- Cost:
- Free
- Contact:
- Xiaoxi Shen
rcd67@txstate.edu - Campus Sponsor:
- Department of Mathematics
Dr. Zixian Yang
Computer Science Department at TXST
Regret-Queue Length Tradeoff in Online Learning for Two-Sided Queues
Abstract: We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be matched by the platform, at which point, they leave the system. Our objective is to design pricing and matching algorithms that maximize the platform’s profit, while maintaining reasonable queue lengths. As the demand and supply curves governing the price-dependent arrival rates may not be known in practice, we design a novel online-learning-based pricing policy and establish its near-optimality. In particular, we prove a tradeoff among three performance metrics: regret, average queue length, and maximum queue length for . Moreover, barring the permissible range of , we show that this trade-off between regret and average queue length is optimal up to logarithmic factors under a class of policies, matching the optimal one as in (Varma, 2023) which assumes the demand and supply curves to be known. Our proposed policy has two noteworthy features: a dynamic component that optimizes the tradeoff between low regret and small queue lengths; and a probabilistic component that resolves the tension between obtaining useful samples for fast learning and maintaining small queue lengths.
Computer Science Department at TXST
Regret-Queue Length Tradeoff in Online Learning for Two-Sided Queues
Abstract: We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be matched by the platform, at which point, they leave the system. Our objective is to design pricing and matching algorithms that maximize the platform’s profit, while maintaining reasonable queue lengths. As the demand and supply curves governing the price-dependent arrival rates may not be known in practice, we design a novel online-learning-based pricing policy and establish its near-optimality. In particular, we prove a tradeoff among three performance metrics: regret, average queue length, and maximum queue length for . Moreover, barring the permissible range of , we show that this trade-off between regret and average queue length is optimal up to logarithmic factors under a class of policies, matching the optimal one as in (Varma, 2023) which assumes the demand and supply curves to be known. Our proposed policy has two noteworthy features: a dynamic component that optimizes the tradeoff between low regret and small queue lengths; and a probabilistic component that resolves the tension between obtaining useful samples for fast learning and maintaining small queue lengths.
Math CATS Tutoring
–
until Sept. 27
- 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