Jessica Hoffmann is a 5th-year PhD student at the University of Texas at Austin, working with Prof. Constantine Caramanis. Her areas of interest include epidemics, applied probability, graph algorithms, combinatorics, and robustness. She received her master's degree in Applied Mathematics from ENS Paris, and most recently was awarded second place in the George E. Nicholson Student Paper Competition. During her graduate studies, she revived and led for two years the Graduate Women in Computing association at UT Austin, which is still active to this day.

AG 1, AG 2, AG 3, INET, AG 4, AG 5, SWS, RG1, MMCI   Info

AG Audience

English

60 Minutes

Saarbrücken

Epidemic processes can model anything that spreads. As such, they are a useful tool for studying not only human diseases, but also network attacks, chains of activation in the brain, the propagation of real or fake news, the spread of viral tweets, and other processes. In this talk, we investigate epidemics spreading on a graph in the presence of various forms of uncertainty. We present in particular a result about controlling the spread of an epidemic when there is uncertainty about who exactly is infected. We show first that neither algorithms nor results are robust to uncertainty. In other words, uncertainty fundamentally changes how we must approach epidemics on graphs. We also present two related results about learning the graph underlying an epidemic process when there is uncertainty about when people were infected or what infected them.

Gretchen Gravelle

068193039102

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Yes

Saarbrücken

E1 5

029

SWS Space 2 (6312)

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