About Me

I am a fourth-year Ph.D. student in the Department of Statistics at University of Washington advised by Zaid Harchaoui and Alex Luedtke. My research interests involve generative modeling, optimal transport, gradient flows, and deep learning in general. I am interested in application of optimal transport and gradient flows in statistical inference and explainable artificial intelligence. At UW, I am fortunate to be working with Soumik Pal on optimal transport. I also work in collaboration with the Abrahms’s Lab at the eScience Institute on revealing hidden lives of cryptic carnivores with AI.

I did my undergraduate in Mathematics from Indian Institute of Technology Kanpur with a minor in English literature. At IITK, I worked on developing methods for output analysis and convergence diagnostics of Markov chain Monte Carlo and importance sampling with Dootika Vats.

Contact me at medhaaga [at] uw [dot] edu.

News

Feb 2025 Gave a talk at the IFML Mathematucs of Deep Learning Workshop in Austin, TX. [Slides]
Feb 2025 Gave a talk at the UW Data Science Seminar titled Revealing the Hidden Lives of Cryptic Carnivores with Machine Learning and AI. Check out the YouTube video! [Slides]
Jan 2025 Gave a talk titled Pre-trained Transformer Learning as Heat Flows at the JMM 2025 AMS Special Session on MRC Mathematics of Adversarial, Interpretable, and Explainable AI. [Slides]
Dec 2024 The paper on Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty in collaboration with Abrahms's Lab is now on arXiv!
Nov 2024 Received the best student poster award at ASA SLDS 2024.
Nov 2024 Presented a poster titled Pretrained Transformers are Heat Flows at ASA SLDS 2024. [Poster]
Oct 2024 Presented a poster titled Iterated Schrödinger Bridge Approximation to Wasserstein Gradient Flows at SIAM MSD 2024. [Poster]
Jun 2024 The paper on Iterated Schrödinger bridge approximation to Wasserstein Gradient Flows is now on arXiv!
Summer 2024 Concluded a summer internship at Amazon Science!