Garrett Mulcahy

I am a fourth year graduate student in the Mathematics Department at the University of Washington, Seattle, where I have the good fortune of being advised by Prof. Soumik Pal.

I’m currently interested 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. Iterated Schrödinger Bridge Approximation to Wassertsein Gradient Flows. 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.