The Department of Mathematics at Texas State University will host an 8 week summer REU in the summer of 2025, involving nine students working in teams of three under the supervision of a faculty mentor. The program will provide lodging, meals, transportation, as well as a stipend for all students. The chosen participants will come from a selective pool of applicants, and the program will directly involve them in research projects guided by faculty mentors. Beyond successful research outcomes the objectives of the program are to introduce students to aspects of mathematical research and the broader mathematical community. At the same time the students will develop mentoring relationships with faculty as they consider pursuing graduate degrees and a career in mathematics. Students will be part of an active community at Texas State; in addition to the REU there are a number of summer programs and world-renowned mathematicians who visit campus, and participants will be involved in various activities that bring students and faculty together. A diverse body of participants is strongly desired and priority will be given to applicants from groups that are underrepresented in the mathematical community. This includes minority students, women, as well as those students coming from institutions with limited resources to support independent research projects for their undergraduates.
The research projects include a diverse collection of topics based on the research interests of the faculty mentors. Specific projects include commutative algebra arising from graphs and matroids, combinatorics of hyperplane arrangements, chip firing on signed graphs and hypergraphs, combinatorial problems arising from group theory, arithmetical properties of group invariants, linear and permutational representations of groups, statistical analysis on biological data, and deep ReLU neural networks. Participants will spend some of the time learning the necessary background while pursuing original research. Students will be asked to present their findings in oral presentations, and will be guided in summarizing their work in a mathematical research paper. In the course of the program students will be trained in various skills including literature review, using software such as GAP, Macaulay2, and R to make and check examples, oral presentation skills, as well as written mathematical communication using Latex. Communication with the participants will continue after the program ends, and for instance students will be encouraged to present their work at local, regional, and national conferences in the subsequent academic year.
This program is supported by NSF and Texas State University.
Applicants must be full time undergraduate students and must not have received their bachelors degree before the start of the summer program. We will send our first round offers around Feb 28 and finish the recruitment process by March 15.
For algebra project, we are looking for students who have taken at least one semester of abstract algebra; for combinatorics project, we are looking for students who have taken linear algebra and preferably some courses related to combinatorics; for statistics project, we are looking for students who have taken linear algebra and preferably some programming experience.
(Due to NSF rules, only US citizens and permanent residents are eligible for funding assistance. International students (self-support) in the USA may apply to the program but will only be accepted in exceptional cases. If you have any questions, please contact Dr. Yang.)
Mentors
Dr. Yong Yang, the Program Director is currently a Professor at the mathematics department of Texas State University. He received his PhD in 2009 from University of Florida under Alexandre Turull. He has published over 110 research papers and one book, and his research interests are in finite groups and group representations, combinatorics, and number theory.
Dr. Thomas Keller, the Co-Program Director is a Professor of Mathematics at Texas State University and has research interests in finite group theory. He has published more than 40 peer-reviewed research articles in prominent math journals. He is interested in the orbit structure of finite linear group actions and in conjugacy classes of finite groups. He has also done research with undergraduate and graduate students.
Anton Dochtermann received his PhD from the University of Washington where his advisors were Eric Babson and Isabella Novik. After postdoctoral positions at TU Berlin, Stanford, and U Miami, he joined the faculty at Texas State in 2016. His research interests are in topological and geometric combinatorics, and combinatorial commutative algebra. Recent projects include the commutative algebra of chip-firing, generalizations of parking functions for matroids, notions of higher-dimensional chordality, and topological methods in graph theory.
Suho Oh is currently an Associate Professor at the mathematics department of Texas State University. He received his PhD from Massachusetts Institute of Technology under Alexander Postnikov and did postdoctoral studies in University of Michigan under Sergey Fomin. His research interests are in algebraic combinatorics. He mainly deals with problems regarding matroids and polytopes, with applications to geometry and representation theory.
Shuying Sun received her PhD in statistics from the University of Toronto in Canada. Dr. Sun has interests in statistical genetics and bioinformatics and has published more than 20 peer-reviewed research articles in high-impact journals. Dr. Sun’s research focuses on addressing challenging genetic and epigenetic questions using statistical and computational methods. She has been developing statistical methodologies and software packages for genomic problems using Bayesian methods, hidden Markov models, Markov Chain Monte Carlo algorithms, and linear models.
Xiaoxi Shen received his PhD in Statistics from Michigan State University in 2019. He joined the faculty at Texas State University in 2021 after working at the University of Florida for two years as a postdoctoral associate. His research interests include statistical genetics, mixed models, and statistical learning theory. Recent projects involve detecting significant disease-related genetic variants using kernel methods and neural networks, estimating genetic heritability with kernel mixed models, and predicting genetic risk via deep neural networks.
