Unraveling the Future: Predicting the Influenza Vaccine Response through Machine Learning

In the light of the recent pandemic, we are more aware than ever of the importance of effective vaccination. Immunology is undergoing a paradigm shift where the objective has expanded from creating a stopgap vaccine against a currently-circulating virus to developing a universal vaccine that confers lifelong protection. At the same time, we are coming to appreciate that individuals respond differently to a vaccine, so that the one-size-fits-all approach may not protect the entire population. Join Dr. Tal Einav from La Jolla Institute for Immunology as he describes how his group uses machine learning to dig into the wealth of antibody-virus data to develop more personalized vaccine recommendations.

Tal Einav, PhD
Dr. Einav is an Assistant Professor at La Jolla Institute for Immunology. He completed his physics PhD at Caltech, spent a year working as a software developer at Wolfram Research, and did a postdoctoral fellowship in computational immunology at the Fred Hutch Cancer Center in Seattle. This diverse set of skills inspire unique ways of thinking about what the future of immunology could look like.

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