Noyce Conference Room
  US Mountain Time
Tina Eliassi-Rad

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Abstract: As the use of machine learning (ML) algorithms in network science increases, so do the problems related to explainability, transparency, fairness, privacy, and robustness, to name a few. In this talk, I will give a brief overview of the field and present recent work from my lab on the (in)stability and explainability of node embeddings, attacks on ML algorithms for graphs, and equality in complex networks.


Tina Eliassi-RadTina Eliassi-RadProfessor, Computer Science, Northeastern University; Science Steering Committee Member + External Professor at SFI
SFI Host: 
Cris Moore

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