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
- 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 234; 234
- Cost:
- Free
- Contact:
- Ellen Couvillion
ebr21@txstate.edu - Campus Sponsor:
- Pi Mu Epsilon
Come join Pi Μu Epsilon for our first Math and Tea of the semester! It will take place TOMORROW, Thursday 9-17, from 3:30 - 4:30 PM in Derrick 234. Come chat with faculty, graduate students, and other undergraduate math majors. There will be cookies and tea, of course! Hope to see you there.
Click here for more information
more about event
- 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
Speaker: Dr. Hyun Chul Jang
Title: How Does Geometry Help Machines Learn?
An Introduction to Information Geometry
Abstract:
What does it mean for a machine to learn from data, and where does geometry enter? Starting with the problem of estimating a coin’s bias, we will see how probability predictions can be compared and how this comparison gives rise to a notion of length: the Fisher information metric. We will discover a sphere hidden in a space of probabilities, use this geometry to derive a learning rule called natural gradient descent, and explain its connection to simple classification models.
This introductory talk emphasizes pictures and concrete calculations. Basic calculus will be helpful; no prior knowledge of machine learning or differential geometry is assumed.
Faculty, graduate students, and interested undergraduate students are all welcome!
Title: How Does Geometry Help Machines Learn?
An Introduction to Information Geometry
Abstract:
What does it mean for a machine to learn from data, and where does geometry enter? Starting with the problem of estimating a coin’s bias, we will see how probability predictions can be compared and how this comparison gives rise to a notion of length: the Fisher information metric. We will discover a sphere hidden in a space of probabilities, use this geometry to derive a learning rule called natural gradient descent, and explain its connection to simple classification models.
This introductory talk emphasizes pictures and concrete calculations. Basic calculus will be helpful; no prior knowledge of machine learning or differential 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
Challenges and Opportunities of AI: Evidence on Adoption, Trust, and Bias
Ehsan Mohammadi
University of South Carolina
Abstract: Generative AI and algorithmic systems now shape decisions in health, education, and
scholarship, often faster than anyone can measure what they actually do. In this talk, Dr. Ehsan
Mohammadi, Associate Professor in the School of Information Science at the University of
South Carolina and Director of the Hi Da Research Lab, presents several connected lines of
research on that gap.
He begins with adoption. A survey of academics across 20 countries found heavy use of
generative AI in research and teaching alongside persistent doubts among those same users
about accuracy, transparency, intellectual property, privacy, and bias. He continues with AI
fairness: he discusses an audit of Google Vision AI that failed to identify women and people of
color as scientists and showed an additional intersectional bias affecting women of color. Third,
through the international AI Peer project, he compares large language model outputs against
human expert judgment in academic peer review. He closes with two externally funded AI
literacy initiatives: FLARE A Click here for more information
more about event
Ehsan Mohammadi
University of South Carolina
Abstract: Generative AI and algorithmic systems now shape decisions in health, education, and
scholarship, often faster than anyone can measure what they actually do. In this talk, Dr. Ehsan
Mohammadi, Associate Professor in the School of Information Science at the University of
South Carolina and Director of the Hi Da Research Lab, presents several connected lines of
research on that gap.
He begins with adoption. A survey of academics across 20 countries found heavy use of
generative AI in research and teaching alongside persistent doubts among those same users
about accuracy, transparency, intellectual property, privacy, and bias. He continues with AI
fairness: he discusses an audit of Google Vision AI that failed to identify women and people of
color as scientists and showed an additional intersectional bias affecting women of color. Third,
through the international AI Peer project, he compares large language model outputs against
human expert judgment in academic peer review. He closes with two externally funded AI
literacy initiatives: FLARE A Click here for more information
- Location:
- DERR 329; 329
- Cost:
- Free
- Contact:
- Ellen Couvillion
ebr21@txstate.edu - Campus Sponsor:
- Department of Mathematics
Mathematics major, Cyril Salib will give a talk titled How Great Minds and Endeavors Changed Our World For the Better. All students and faculty are welcome to attend!
Click here for more information
more about event
- Location:
- DERR 333; 333
- Cost:
- Free
- Contact:
- Xiaoxi Shen
rcd67@txstate.edu
Title: Variable Importance Identification Through Lazy Training for Binary Classification
Abstract: Deep neural networks have been widely used in many applications (e.g., computer vision and natural language processing); however, understanding their explainability remains a challenging task. Recently, substantial research has been devoted to improving the explainability of deep neural networks, with most of this work focusing on the regression framework. In this paper, we instead focus on the binary classification framework and adopt a variable-importance framework combined with the idea of lazy training to propose an efficient algorithm for identifying important features. From a theoretical perspective, our method relies on only a minimal set of assumptions and achieves well-controlled error rates. The validity of the proposed method and algorithm is examined through extensive simulation studies and real-data applications.
Abstract: Deep neural networks have been widely used in many applications (e.g., computer vision and natural language processing); however, understanding their explainability remains a challenging task. Recently, substantial research has been devoted to improving the explainability of deep neural networks, with most of this work focusing on the regression framework. In this paper, we instead focus on the binary classification framework and adopt a variable-importance framework combined with the idea of lazy training to propose an efficient algorithm for identifying important features. From a theoretical perspective, our method relies on only a minimal set of assumptions and achieves well-controlled error rates. The validity of the proposed method and algorithm is examined through extensive simulation studies and real-data applications.
- Location:
- Online only
- Cost:
- Free
- Contact:
- Bea Ellis
bea.ellis@txstate.edu - Campus Sponsor:
- Department of Mathematics
Reagin Taylor McNeill and Sara Elakesh (Michigan State University)
Don’t They Want Us To Succeed?: A Critical Race Counterstory to Deficit Discourses about Students’ Effort in Undergraduate Calculus
Abstract: Nationally, undergraduate calculus is a course with high DFW rates, contributing to persistent racialized disparities in STEM. In response, researchers and practitioners have often drawn on deficit discourses that locate these educational failures within students—particularly Black and Latin* students—framing them as lacking effort or ability. This interactive session presents an analysis of how Black and Latin* calculus students’ experiences talk back to these deficit discourses. We place particular emphasis on our methods to make visible the interpretive decisions that shape qualitative research, challenging notions of detached “objectivity” while engaging the complexities of intersectionality and coalitional solidarity among participants of color with diverse racial and ethnic identities. Click here for more information
more about event
Don’t They Want Us To Succeed?: A Critical Race Counterstory to Deficit Discourses about Students’ Effort in Undergraduate Calculus
Abstract: Nationally, undergraduate calculus is a course with high DFW rates, contributing to persistent racialized disparities in STEM. In response, researchers and practitioners have often drawn on deficit discourses that locate these educational failures within students—particularly Black and Latin* students—framing them as lacking effort or ability. This interactive session presents an analysis of how Black and Latin* calculus students’ experiences talk back to these deficit discourses. We place particular emphasis on our methods to make visible the interpretive decisions that shape qualitative research, challenging notions of detached “objectivity” while engaging the complexities of intersectionality and coalitional solidarity among participants of color with diverse racial and ethnic identities. Click here for more information
- 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 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 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 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!