Infectious disease is a persistent and substantial threat to human health, with consequences that include widespread mortality, suffering, and economic disruption. I will present several algorithmic advances that, when coupled with biotechnologies for data collection and perturbation, are aimed at understanding infectious disease and using this knowledge to fight it. First, I will describe geometric algorithms that enable a panoramic understanding of the systems biology of the human immune system and of infectious pathogens at single-cell resolution. Next, I will show how Bayesian machine learning can explore complex biological spaces to search for new therapies that fight infectious disease. Finally, I will leverage neural language models to predict how pathogens mutate to evade human immunity, potentially enabling more broadly effective vaccines and therapies. Taken together, these developments outline an interdisciplinary, algorithmic approach to infectious disease research, with broader implications for computation and biology more generally.
Thesis Supervisor: Bonnie Berger
To attend this defense, please contact the doctoral candidate at brianhie at mit dot edu