Dr. Tal Einav’s accomplishments included the development of sophisticated computational methods to understand viral behavior and predict how individuals react to vaccination or infection. This research earned Einav a prestigious Damon Runyon Quantitative Biology Fellowship and emphasized the importance of pursuing machine learning to analyze big data in immunology.
“We have these tremendous datasets that we’re just barely tapping into,” says Einav. These data allow Einav to understand the immune response in different contexts, from the young to the elderly, from healthy people to individuals who are immunocompromised. All with the goal to discover key patterns that let us understand and harness our immunity. Einav’s work has already demonstrated that blending biophysics and computer science enables researchers to predict the antibody response against new viral variants.
This work paves the way for a fundamentally new form of personalized medicine. For example, Einav imagines tailoring an individualized vaccine strain or dosage based on a patient’s specific antibody repertoire to create a stronger response that lasts for years, if not their entire life.