Inferring Selection Effects in SARS CoV 2 with Bayesian Viral Allele Selection

Inferring Selection Effects in SARS CoV 2 with Bayesian Viral Allele Selection

Presented By: Martin Jankowiak, PhD

Speaker Biography: Martin Jankowiak is a machine learning fellow at the Broad Institute of MIT and Harvard. His research focuses on a range of topics across probabilistic machine learning, Bayesian statistics, and applications to computational biology. Martin is also a co-creator and core developer of the probabilistic programming frameworks Pyro and NumPyro. He was previously a senior research scientist at Uber AI Labs and has a PhD in theoretical physics from Stanford.

Webinar: Inferring Selection Effects in SARS-CoV-2 with Bayesian Viral Allele Selection

Webinar Abstract: The global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance data make rigorous analysis challenging.

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