Marian Huot

The paper develops a stochastic model for affinity maturation and identifies an optimal antigen-concentration protocol that maximizes the probability of reaching a target antibody affinity.

This paper presents EscapeMap, a framework for predicting and designing SARS-CoV-2 variants that escape antibodies, and identifies antibody combinations less prone to simultaneous escape.

The paper develops a probabilistic framework to predict viral immune escape trajectories, showing that escape is constrained to specific pathways despite vast mutational space.

This paper introduces a few-shot learning framework for detecting high-fitness viral variants using Bayesian active learning and biophysics, showing it can identify variants up to fivefold faster with fewer experiments.