Alex J. Li


Hi, I’m Alex! I’m a first year graduate student in the UC Berkeley - UCSF Joint Bioengineering PhD Program. I graduated from MIT in 2022 with a SB in Chemistry with a Focus in Applied Machine Learning. During my undergrad, I worked on

  • organic synthesis: synthesizing extended rigid \(\pi\)-conjugated helical structures for optomagnetic materials.
  • automated wet-lab experiment analysis: using Bayesian inference and differentiable programming to simulate and analyze wet lab experiments with automatic uncertainty quantification
  • machine learning for protein design: combining tertiary motifs (TERMs) with energy-based modeling to improve graph-based protein design models.

I’m generally interested in the ways we can apply computation and scientific intuitions to assist and accelerate biochemical scientific discovery. On the computational side, I’m interested in geometric/graph deep learning, probabilistic inference, and program synthesis. On the biochemical side, I’m interested in protein design and engineering, in particular on the topics of enzymatic catalysis, protein-protein interactions, and ligand-protein interactions.


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selected publications

  1. Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs
    Alex J. Li, Mindren Lu, Israel Desta, Vikram Sundar, Gevorg Grigoryan, and Amy E. Keating
    bioRxiv 2022
  2. TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
    Alex J. Li, Vikram Sundar, Gevorg Grigoryan, and Amy E. Keating
    Machine Learning for Structural Biology Workshop, NeurIPS 2021