Zack Tidwell is a machine learning and optimization specialist who enjoys distilling problems to their most basic forms in order to develop robust, generalizable solutions. His research experience spans a diverse array of domains, including healthcare, meteorology, and gaming, as well as a variety of solution spaces, including neural networks, reinforcement learning, and spatial-temporal data mining. Notably, he developed spatial-probability trees for expert move prediction in computer Go, as well as machine-learning methods for CAPTCHA recognition. His current projects focus on distributed systems for personal computing and automated techniques for de-siloing large legacy datasets. He holds an M.S. in Computer Science from the University of Oklahoma.
Rachel Shadoan is a data visualizer and design ethnographer specializing in hybrid research methodologies. Her work spans the quantitative/qualitative research divides, making numbers friendly for story people, and stories friendly for numbers people. Her research experience includes work with Intel exploring both how people use their phones in cars and how the ability to convert to a tablet impacts laptop use. As part of the Digging into Data Challenge, she collaborated with Stanford digital humanities scholars and Oxford data archivists to develop a visual graph query language to allow researchers to form queries on complex multi-dimensional data. Currently, she is researching the way people understand and interact with the algorithms that increasingly organizer our lives. She holds an M.S. in Computer Science from the University of Oklahoma, and an M.S. in Design Ethnography from the University of Dundee.