Abnormal Blog
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.
Navigate the hype and uncover the true impact of AI on improving efficiency, scalability, and precision in defending against cyber threats.
Discover how Abnormal Security leverages large language models (LLMs) to automate and enhance email threat detection with AI-generated detection rules.
Learn how Abnormal uses natural language processing or NLP to protect organizations from phishing, account takeovers, and more.
New attacks stopped by Abnormal show how attackers are using ChatGPT and similar tools to create more realistic and convincing email attacks.
Discover the potential security risks of generative models like ChatGPT and how Abnormal keeps you protected.
Discover how the Abnormal attack detection team utilizes feature systems, advanced language models, and per-customer understanding in our approach to machine learning in cybersecurity.
We are excited to share that Abnormal has recently deployed a BERT Large Language Model (LLM), pretrained from Google on a large corpus of data, and applied it to stop advanced attacks.
In episode 9 of Abnormal Engineering Stories, Dan sits down with Mukund Narasimhan to discuss his perspective on productionizing machine learning.
At Abnormal, we pride ourselves on our excellent machine learning engineering team. Here are some patterns we use to distinguish between effective and ineffective ML engineers.
Here at Abnormal, our machine learning models help us spot trends and abnormalities in customer data in order to catch and prevent cyberattacks.