Hannah Aizenman is a doctoral student  in Computer Science. Her research is in using machine learning to make sense of and visualize large, mostly climate, datasets. She has contributed to projects on evaluating how well forecasts predict things like temperature and rain, profiling risk and sustainability of coastal deltas, visualizing climate data online, running global climate models on super computers through a web interface, and giving students rapid feedback through an automated code runner that ties into blackboard and other CMSes. She was an adjunct at CCNY for the past 5 years, mostly teaching engineers how to program. For the past 3 years, she has taught and mentored high school students for the CREST HIRES REU program. She’s spoken at a couple of Python conferences, is on the planning committee of the AMS Python Symposium, and is an organizer for the New York Linux Users Group (NYLUG).