Summary
Yuxin Chen is an Assistant Professor at the University of Chicago with 12 years of experience at the intersection of machine learning, decision making, and adaptive optimization. Her work focuses on interactive machine learning and practical theories for active learning deployed in real-world systems, bridging rigorous theory from her PhD at ETH Zurich with applied research from postdoctoral work at Caltech. She has a strong background in submodular optimization and data-driven methods from prior roles at ETH Zurich, Microsoft Research, and Xerox Research, which informs her approach to scalable, provable algorithms. Based in Chicago, she combines academic leadership with hands-on experimentation, often emphasizing deployment challenges that are overlooked in purely theoretical work. Not obvious from titles alone, her trajectory spans both programming-language influenced verification research and information-retrieval privacy work, giving her a rare cross-disciplinary perspective on trustworthy, interactive AI.
12 years of coding experience
7 years of employment as a software developer
M.S., Computer Science, M.S., Computer Science at University of Kansas
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Eidgenössische Technische Hochschule Zürich
B.E., Electrical Engineering, Information Science, B.E., Electrical Engineering, Information Science at University of Science and Technology of China
English, Chinese