Faculty Profile for Dr. Jeremiah Birrell

profile photo for Dr. Jeremiah Birrell
Dr. Jeremiah Birrell
Assistant Professor — Mathematics
MCS 457
phone: (512) 245-8018

Biography Section

Biography and Education

I obtained a PhD in Applied Mathematics from the University of Arizona and a Bachelor of Science in Physics from Brigham Young University. Before coming to Texas State University I was a postdoc in the Mathematics Department and the Computer Science Department at the University of Massachusetts Amherst.

Teaching Interests

As an applied mathematician, my preferred teaching style is to intermingle a solid foundation in theory with practical algorithmic considerations, including non-trivial applications, which generally involve coding. I find that this approach amplifies students' abilities in both theoretical and practical aspects of the subject matter and makes the material more accessible and relevant to a diverse group of students with many different backgrounds and interests. My preferred courses to teach in this style are linear algebra, numerical analysis, nonlinear dynamics, and machine learning.

Research Interests

I am an applied mathematician, currently working in statistical learning theory. Current and past research includes: 1) Privacy-preserving machine learning. 2) Adversarial robustness of deep learning models. 3) Design and analysis of novel algorithms for generative models. 4) Robustness bounds for quantities of interest in stochastic systems. 5) Singular limits of stochastic differential equations. 6) Numerical methods in kinetic theory.

Selected Scholarly/Creative Work

  • Birrell, J., Formanek, M., Steinmetz, A., Yang, C. T., & Rafelski, J. (2024). Fermi-Dirac Integrals in Degenerate Regimes: Novel Asymptotic Expansion. International Journal of Theoretical Physics, 63(7). https://doi.org/10.1007/s10773-024-05695-8
  • Rafelski, J., Birrell, J., Steinmetz, A., & Yang, C. T. (2023). A Short Survey of Matter-Antimatter Evolution in the Primordial Universe. Universe, 9(7). https://doi.org/10.3390/universe9070309
  • Birrell, J., Pantazis, Y., Dupuis, P., Rey-Bellet, L., & Katsoulakis, M. A. (2023). Function-space regularized Rényi divergences. In The Eleventh International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=89GT-S49mGd
  • Birrell, J., Katsoulakis, M. A., & Pantazis, Y. (2022). Optimizing Variational Representations of Divergences and Accelerating Their Statistical Estimation. IEEE Transactions on Information Theory, 68(7), 4553–4572. https://doi.org/10.1109/tit.2022.3160659
  • Birrell, J., Katsoulakis, M. A., Rey-Bellet, L., & Zhu, W. (2022). Structure-preserving GANs. In Proceedings of Machine Learning Research (Vol. 162, pp. 1982–2020). Retrieved from https://proceedings.mlr.press/v162/birrell22a.html

Selected Service Activities

Member
Colloquium Committee
September 23, 2024-Present