Summer Hangouts: Supranta S. Boruah

Diffusion models have emerged as powerful tools in generative modeling, achieving impressive results across different domains. In this tutorial, we’ll explore how these models can be adapted to tackle a cosmological challenge: mapping dark matter in the Universe from noisy weak lensing observations. We’ll begin with a hands-on introduction to diffusion models using a simple 1D example. Participants will simulate the forward and reverse diffusion processes, first with an analytical score function and then by training a neural network to learn it. Along the way, we’ll build up key intuitions behind denoising score matching.

The second half of the tutorial connects these concepts to a real-world application in cosmology. We’ll discuss how diffusion-based posterior sampling can be used to reconstruct dark matter mass maps from lensing data, inspired by recent work in the field.

Learn More About Supranta S. Boruah, Center for Particle Cosmology Fellow

DDDI’s 2025 Summer Hangouts program offers students the opportunity to participate in informal, hands-on tutorials led by our postdoctoral research fellows. These tutorials are open to students from all backgrounds and skill levels and cover various data science methods and topics.

Our Hangouts series this year will explore machine learning, statistics, optimization, and generative AI for scientific discovery across fields. Hangouts will be held twice a week on Tuesdays and Thursdays, from June 10th through June 25th. A pizza lunch will be provided.