Sophia Tang

Hi! I am a second-year undergraduate student at the University of Pennsylvania pursuing a dual degree in computer science and business in the Jerome Fisher Program in Management & Technology.

I conduct research at the Chatterjee Lab at Duke University developing generative deep learning models for biological discovery and at the Mitchell Lab at the University of Pennsylvania designing lipid nanoparticle (LNP)-based delivery vehicles to transport mRNA across the blood-brain barrier. Working across two labs has enabled me to gain experience in both the computational and wet-lab side of bioengineering innovation, which you can see on my publications page.

One of my favourite aspects of research is deeply understanding the theory behind the mechanisms governing the algorithms or biological processes that I study. To share this interest, I began writing in-depth technical guides on my substack, Alchemy Bio. My most read article, “A Complete Guide to Spherical Equivariant Graph Transformers” is a 2.5-hour long read breaking down the mathematics and physics underlying the structural deep learning architectures for molecules and proteins.

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