Abnormal Blog/Author/Dr. Dan Shiebler
Abnormal Blog/Author/Dr. Dan Shiebler

Dr. Dan Shiebler
Head of Machine Learning
Dr. Dan Shiebler is the Head of Machine Learning at Abnormal, responsible for leading a team of 40+ detection and ML engineers in building the data processing and ML layers in Abnormal’s platform. Prior to Abnormal, Dan worked at Twitter, first as a staff machine learning engineer in Cortex, and later as the manager of the web ads machine learning team. Before Twitter, Dan worked as a senior data scientist at Truemotion, where he developed smartphone sensor algorithms to price car insurance. He has a Ph.D. in machine learning from the University of Oxford.

Engineering
Misclassification Adaptation in Cyberattack DetectionFeb 7, 2025

Artificial Intelligence
AI vs. AI: How Abnormal Fights DeepSeek AI-Powered Phishing AttacksJan 29, 2025

Artificial Intelligence
Cutting Through the Hype: How AI Truly Enhances CybersecuritySep 20, 2024

Engineering
Innovating Email Protection: Writing Detection Rules with LLMsJul 26, 2024

Engineering
How Abnormal Security Leverages NLP to Thwart CyberattacksJul 16, 2024

Artificial Intelligence
Generative AI Enables Threat Actors to Create More (and More Sophisticated) Email AttacksJun 14, 2023

Artificial Intelligence
ChatGPT Phishing Attacks: You’re Still Protected With AbnormalApr 12, 2023

Product
An Abnormal Approach to Machine Learning: Feature Systems and Language ModelsJan 8, 2023

Product
How Abnormal Enhanced Its Detection Platform with BERT Large Language Models (LLMs)Oct 12, 2022

Engineering
Abnormal Engineering Stories, Episode #9: Productionizing Machine LearningJun 1, 2022

Engineering
8 Key Differences Between Ineffective and Effective Machine Learning EngineersMay 2, 2022

Engineering
Model Understanding with Feature ImportanceMar 16, 2022