Garrett Mulcahy
I am a fourth year PhD candidate in the Mathematics Department at the University of Washington, Seattle, where I have the good fortune of being advised by Prof. Soumik Pal.
I plan to graduate in June 2026. I am on the job market for postdoctoral/research positions starting Summer/Fall 2026.
I’m currently interested in optimal transport and its connections to machine learning and statistics. My recent projects have involved
- approximating entropic regularized optimal transport plans (Schrödinger bridges)
- approximating gradient flows in the Wasserstein space using Schrödinger bridges
- statistical sensitivity analysis via optimal transport
More broadly, I am interested in all things probability, statistics, and machine learning. I have previously worked on applied projects involving reinforcement learning, language models, and Bayesian statistics.
I did my undergraduate studies in Mathematics and Statistics at Purdue University, where I was generously mentored by Prof. Thomas Sinclair.
Publications, Conference Papers, and Technical Reports
- Agarwal, M., Harchaoui, Z., Mulcahy, G., Pal, S. Langevin Diffusion Approximation to Same Marginal Schrödinger Bridge. Arxiv
- M. Landajuela, C. Shing Lee, J. Yang, R. Glatt, C. Santiago, T. N. Mundhenk, I. Aravena, G. Mulcahy, B. K. Petersen, A unified framework for deep symbolic regression. Thirty-sixth Conference on Neural Information Processing Systems, 2022, NeurIPS 2022.
- Mulcahy, G. & Sinclair T., Malnormal matrices, Proc. Amer. Math. Soc. 150 (2022), no. 7, 2969-2982
- Mulcahy, G., Brooks, D.M., Ehrhart, B, Using Bayesian methodology to estimate liquefied natural gas leak frequencies, Sandia National Laboratories, Albuquerque, New Mexico, 2021, SAND2021-4905.
- Mulcahy, G., Atwood, B., & Kuznetsov, A. (2020). Basal ganglia role in learning rewarded actions and executing previously learned choices: Healthy and diseased states. PLOS ONE 15(2): e0228081. https://doi.org/10.1371/journal.pone.0228081
Contact Information
My email is gmulcahy @ uw . edu.