Sophia Tang

Hi! I am a third-year undergraduate student at the University of Pennsylvania conducting research in generative ML for scientific discovery. Aside from research, I study computer science and statistics in the Jerome Fisher Program in Management & Technology.

Currently, I’m part of the Chatterjee Lab, developing theoretical ML frameworks for biological design, and the Mitchell Lab, designing lipid nanoparticle (LNP)-based delivery vehicles to transport mRNA across the blood-brain barrier.

My research ranges from developing theoretical Schrödinger bridge frameworks for generative modelling of branching and interacting particle systems to multi-objective RL and guidance techniques for discrete diffusion - but I’m always exploring new theoretical ideas and thinking about interesting problems to apply them to!

I also write technical articles on my Substack, Alchemy Bio. My most read article, “A Complete Guide to Spherical Equivariant Graph Transformers” (now released on arXiv) is a self-contained guide (99 pages, 46 figures) breaking down the theory behind spherical equivariant graph neural networks (EGNNs) for geometric deep learning.

If you’re also passionate about any of these topics, I would love to connect via LinkedIn or X.