Chris Gates's primary research is on the application of machine learning and data mining to malware classification, risk estimation, active learning, automation, and other problems in security and scalable machine learning. Chris received his Ph.D. in Computer Science on the topic of quantifying and communicating risk using machine learning. He graduated from Purdue University in 2014 advised by Ninghui Li and was also part of the Center for Education and Research in Information Assurance and Security (CERIAS).
Since joining the company, Chris has authored several papers and patents, worked on pure research as well as with product teams. The projects that Chris has worked on have touched hundreds of millions of users to keep them, and their information, safer.