chat
expand_more

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.

Blog Thumbnail

Engineering

Misclassification Adaptation in Cyberattack Detection

Feb 7, 2025

Blog Thumbnail

Artificial Intelligence

AI vs. AI: How Abnormal Fights DeepSeek AI-Powered Phishing Attacks

Jan 29, 2025

Blog Thumbnail

Artificial Intelligence

Cutting Through the Hype: How AI Truly Enhances Cybersecurity

Sep 20, 2024

Blog Thumbnail

Engineering

Innovating Email Protection: Writing Detection Rules with LLMs

Jul 26, 2024

Blog Thumbnail

Engineering

How Abnormal Security Leverages NLP to Thwart Cyberattacks

Jul 16, 2024

Blog Thumbnail

Artificial Intelligence

Generative AI Enables Threat Actors to Create More (and More Sophisticated) Email Attacks

Jun 14, 2023

Blog Thumbnail

Artificial Intelligence

ChatGPT Phishing Attacks: You’re Still Protected With Abnormal

Apr 12, 2023

Blog Thumbnail

Product

An Abnormal Approach to Machine Learning: Feature Systems and Language Models

Jan 8, 2023

Blog Thumbnail

Product

How Abnormal Enhanced Its Detection Platform with BERT Large Language Models (LLMs)

Oct 12, 2022

Blog Thumbnail

Engineering

Abnormal Engineering Stories, Episode #9: Productionizing Machine Learning

Jun 1, 2022

Blog Thumbnail

Engineering

8 Key Differences Between Ineffective and Effective Machine Learning Engineers

May 2, 2022

Blog Thumbnail

Engineering

Model Understanding with Feature Importance

Mar 16, 2022