Dandi Chen is a postdoctoral researcher and machine learning practitioner with a decade of experience applying computer vision and health informatics to population health challenges. Based at UW–Madison, he focuses on risk factor identification and early detection of Alzheimer’s and related dementias, bridging rigorous PhD-level research with practical biomedical data science. His background includes top-performing competition work at Weill Cornell (1st place in a NeurIPS genetics challenge) and hands-on internships in industry computer vision groups, reflecting strong applied modeling and engineering skills. He has taught algorithms and AI courses, demonstrating an ability to communicate complex ideas to students and collaborators. Dandi combines deep technical training across electrical engineering, computer science, and biomedical data science with a clear drive to translate computer vision research into real clinical impact.
10 years of coding experience
6 years of employment as a software developer
Non-Degree Exchange Program, Computer Science and Engineering, Non-Degree Exchange Program, Computer Science and Engineering at National Chiao Tung University
Doctor of Philosophy, Population Health, Doctor of Philosophy, Population Health at University of Wisconsin-Madison
Bachelor of Engineering, Electrical Engineering and Computer Science, Bachelor of Engineering, Electrical Engineering and Computer Science at Southeast University
Master of Science, Computer Science and Engineering, Master of Science, Computer Science and Engineering at University of Connecticut
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