Dan Shiebler


Head of Machine Learning


Abnormal Security


Dan works as the Head of Machine Learning at Abnormal Security, where he builds cybercrime detection systems to keep people and businesses safe. Cybercrime is always changing as attackers innovate, and the detection organization at Abnormal Security must stay ahead of these innovations. As the Head of Machine Learning, Dan works to optimize the detection organization’s ability to anticipate these innovations, iterate and improve.

Before joining Abnormal Dan worked at Twitter: first as an ML researcher working on recommendation systems, and then as the engineering manager for the web ads machine learning team. Before Twitter Dan built smartphone sensor algorithms at TrueMotion and Computer Vision systems at the Serre Lab. Dan’s PhD at the University of Oxford focused on the applications of Category Theory to Machine Learning.

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