Dan Shiebler

Dan Shiebler is the Head of Machine Learning at Abnormal Security, where he leads a team of over 40 detection engineers developing AI systems to combat cybercrime. His team deploys probabilistic models over real-time aggregates, neural network and tree-based classifiers over tabular data, and large language models over raw data. Under his leadership, Abnormal Security has built the world's most advanced messaging cyberattack detection system, protecting many of the world's largest companies.

Prior to joining Abnormal Security, Dan managed the Web Ads Machine Learning team at Twitter. Before that, he served as a Staff Machine Learning Engineer at Twitter Cortex and was a Senior Data Scientist at TrueMotion. Throughout his career, Dan has focused on leveraging machine learning and AI to drive innovation and solve complex problems in technology and cybersecurity.

Dan has also spent time in academia, earning his PhD from the University of Oxford. His research focused on applications of Category Theory to Machine Learning, advised by Jeremy Gibbons and Cezar Ionescu. Earlier, he worked as a Computer Vision Researcher at the Serre Lab.