Jonathan Hahn joined WWT in 2017. He is skilled at building analytics solutions and has experience working with clients in mining, utilities, and healthcare organizations. His machine learning experience includes research on Kalman filters and Bayesian networks under contracts with the Department of Defense. Jonathan has also developed algorithms for sensing technology in IoT devices and has built predictive models for healthcare treatments. In the area of deep learning and AI, he has researched auto-encoders, generative adversarial networks, and CNNs for image classification. In a recent project he worked with mining engineers to develop models for predictive maintenance and build digital twins to optimize operations. Jonathan has a Ph.D. in Mathematics from the University of Minnesota.