I develop geometric generative models for protein structure, sequence, and dynamics, with a focus on <i>de novo</i> antibody design. The challenge is to generate human-like antibodies that specifically bind to a target while meeting a range of ancillary constraints—many related to antibody flexibility—in order to create viable therapeutics. I am therefore particularly focused on developing machine learning models to accurately predict conformational ensembles and enable robust antigen-conditional design. An interesting recurring challenge in the antibody space is the comparative scarcity of data compared to general-protein systems.