Vincent Tatan works on one of the internet’s most consequential applications of machine learning: stopping phishing attacks before they reach people. At Google, he fights phishing with machine learning, applying advanced ML algorithms and MLOps practices to protect Chrome, Gmail, and Android users. That means building and operating detection systems that must perform at an enormous scale, where a model is only as good as the pipeline, monitoring, and iteration that keep it accurate against constantly evolving threats.

His work sits at the intersection of machine learning engineering and security, a domain where false positives and false negatives both carry real costs, and where production discipline matters as much as model quality. It is exactly the kind of applied ML that demands strong data infrastructure, careful evaluation, and continuous deployment.

Vincent shares his perspectives on machine learning and its applications through his writing at medium.com/@vincentkernn, helping practitioners understand how ML is used to solve problems that affect billions of people every day.