Daniel Kats is a Principal Researcher, as well as an avid espresso drinker and pub quiz aficionado. His primary research is on the application of machine learning to creative tasks normally handled by humans, as well as human-machine collaboration. Daniel received a Masters in Computer Science from the University of Toronto in 2016, where he was advised by Eyal de Lara.
Daniel has authored a number of patents in diverse computer science areas including privacy, machine learning, server hardening, alert management, risk evaluation, identity, and malware detection. He has also published in the areas of virtualization systems, machine learning, and data visualization.