Dan Shiebler

Dan Shiebler has a diverse and extensive work experience in the field of machine learning and data science. Dan is currently the Head of Machine Learning at Abnormal Security, leading a team of approximately 50 engineers in catching cyberattacks using artificial intelligence. Prior to this, Dan worked at Twitter, where they served as an ML Engineering Manager and later as a Staff Machine Learning Researcher. At Twitter, they played a key role in developing and managing the web ads machine learning organization, resulting in significant improvements in revenue. Dan also contributed to NLP, Recommender Systems, and Graph Learning research, building and deploying Twitter scale embedding systems. Before their time at Twitter, Dan was a Deep Learning Researcher at Brown University, where they designed and trained biologically inspired convolutional neural networks for various tasks. Dan has also worked as a Senior Data Scientist and Team Lead at TrueMotion, where they developed machine learning algorithms for distracted driving detection and prevention. Additionally, Dan has experience as a Data Scientist at the John D. Rockefeller, Jr. Library. Dan's work experience demonstrates their expertise and contributions to the field of machine learning and data science across various industries and organizations.

Dan Shiebler holds a Ph.D. in Artificial Intelligence Theory from the University of Oxford. Prior to that, they completed their Sc.B and A.B degrees with Honors in Neuroscience and Computer Science, respectively, at Brown University, maintaining a flawless 4.0/4.0 GPA. Dan acquired their secondary education at Millburn High School.