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Dave is an assistant professor in the Information Systems department, and he's easily excited by a variety of research and teaching topics.
These currently include finding new ways to capture, process, and analyze digital behavior, and then exploit that behavioral data to help solve interesting problems. More specifically, Dave uses data collected from ubiquitous input devices (e.g., computer mouse, keyboard, mobile devices) to try to infer cognitive processes affecting usability, privacy issues, and online fraud behavior. Application of these technologies can be used to assess and improve online experiences for large populations of users.
Much of the above requires complex data processing, feature extraction, and machine learning modeling, and Dave spent about 5 years working in the private sector building and deploying real-time machine learning systems to deliver useful data products to customers. Those products focused primarily on fraud prevention and user experience improvement.
As a result of those private-sector adventures, Dave is especially passionate about teaching these skills and techniques to students in the classroom. His general teaching objectives are to (1) mentor our amazing students into thoughtful, detail-oriented, utility-focused data ninjas, and (2) ensure that our students graduate with an appreciation for "the big picture" and the grit required to create tangible business value from data science assets.
- Dutch; Flemish