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Dr. Alexander White, Associate Chair of the Department of Mathematics and Professor of Statistics at Texas State University, passed away on January 30, 2025. A dedicated educator, mentor, and collaborator, Dr. White’s academic journey began with a PhD in Statistics from Michigan State University. He also earned both his MS in Statistics and BS in Chemistry from the University of Texas at El Paso. Dr. White joined American University in 2000 before joining Texas State University in Fall 2005 as an Assistant Professor of Statistics. As a proud bobcat, he dedicated nearly two decades of service to the institution. He served as a Doctoral Program Advisor in Mathematics Education for 12 years, mentoring over 30 PhD students, 10 master’s students, 2 undergraduate honors theses, and many junior faculty. His university-wide service includes his roles as Chair of the Nontenure Line Faculty Committee (2015-2017), Assessment Coordinator for the Departmental General Education Assessment (2019-present), Chair of the Faculty Senate (2017-2019), and active member of the Developmental Education Advisory Committee, General Education Council, and QEP Leadership Team. Most recently, Dr. White’s leadership was instrumental in the approval of the newest Mathematics doctoral program, a lifelong goal of his. For his outstanding dedication to the university, Dr. White has received 7 Teaching, Research, and Service Awards.
However, his impact extended far beyond the university, where he became a trusted collaborator and mentor in the larger mathematics and statistics education community. He worked closely with Texas Mathworks, developing curricula, facilitating professional development for educators, and mentoring over 25 high school students on summer research projects. Dr. White contributed his expertise in test development, sample selection, and evaluation to several global organizations. His scholarly work in mathematics and statistics education included research on innovative statistical analysis for mathematics education settings, the role of technology in teaching geometry, the use of visualization in the classroom, and the integration of real-world applications into statistical education. He was also a co-author of a widely used middle school Algebra I textbook. Dr. White’s legacy lives on in the countless students, colleagues, and educators whose lives he touched. His commitment to academic excellence and unwavering dedication to his students have left a lasting mark on Texas State University and the broader academic community.
Alex is survived by his wife of 32 years, Alejandra, and two daughters, Isabel and Sofia.
In lieu of flowers or food, the family requests donations be made to a scholarship that has been established in his name, allowing us to give back in recognition of his immense contributions. Alexander White Statistics Fellowship. When making a gift, please manually search for his program in the ‘search for other Texas State University programs' section.
A Celebration of Life for Dr. Alexander White was held on Sunday, February 16 at 3:00 p.m. in the Performing Arts Center Recital Hall.
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.
Jennifer Czocher, Samuel Obara, Ray Treinen, & Yong Yang
Speaker: Professor Julianne Chung, Emory University, Department of Mathematics
Uncertainty quantification for linear inverse problems remains a challenging task, especially for problems with a very large number of unknown parameters (e.g., dynamic inverse problems), for problems where computation of the square root and inverse of the prior covariance matrix are not feasible, and for hierarchical problems where hyperparameters are not known a priori. In this talk, I will describe some recent works on exploiting Krylov subspace methods in the context of large-scale uncertainty quantification. For problems where generalized Golub-Kahan based methods have been used to compute an estimate of the solution, we describe an efficient method that uses the preconditioned Lanczos algorithm to efficiently generate samples from the posterior distribution. Then, we focus on hyperparameter estimation and describe an efficient approach based on stochastic average approximation combined with a preconditioned Lanczos method. Numerical examples from dynamic photoacoustic tomography and atmospheric inverse modeling demonstrate the effectiveness of the described approaches.
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txstate.zoom.us…
Peter Mueller - University of Texas at Austin
We consider several examples of statistical inference for two or more related populations. In one example we characterize two patient populations that are relevant in the construction of a clinical study design and propose a method to adjust for detected differences. Another example is about comparative immune profiling under two biologic conditions of interest when we identify shared versus condition-specific homogeneous cell subpopulations. In a third example we model spatially aligned cell subpopulations using spatial transcriptomics data. Bayesian inference in all three applications requires prior probability models for related distributions. We build on extensive literature on such models based on Dirichlet process priors. Related models are commonly known as dependent Dirichlet processes (DDP), with many variations and extensions beyond the Dirichlet process model. One special feature in all three the motivating applications is the focus on understanding the nature of the dependence across the related populations. In one application we aim to adjust for differences in population heterogeneity, in another we aim to identify and understand homogeneous subpopulations that are characteristic for one or the other condition.
For those who cannot attend in person:
https://txstate.zoom.us/j/84237982936?pwd=4IDhjLs66LazkmpoPg8RA7jrIbIwAR.1
Meeting ID 842 3798 2936 Passcode SS_DERR325
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