ABSTRACT:

Networks can be conducive to the spread of undesirable phenomena from false information to bankruptcy of financial institutions and contagious disease. How can we leverage algorithms to stop or slow down contagious processes? I will survey some of the (vast) literature and discuss algorithmic problems related to social distancing and vaccination. For networks sampled from certain random graph distributions I will present treewidth-based algorithms for these problems. Finally, I will discuss some directions for future research.

BIO:

Daniel Reichman is an assistant professor in the department of computer science at Worcester Polytechnic Institute. Daniel received his PhD at the Weizmann institute and was a postdoc at Cornell University, UC Berkeley and Princeton University. His research interests include theoretical machine learning, artificial intelligence and the study of algorithmic approaches that go beyond worst case analysis.

Hosted by Professor Todd Millstein