Qing Qu, who earned his PhD in electrical engineering from Columbia in 2018 under the guidance of Prof. John Wright and is now an assistant professor of electrical and computer engineering at the University of Michigan, received the College of Engineering’s 1938E Award. The honor recognizes “an outstanding teacher in both elementary and secondary courses… whose scholarly integrity pervades his service to the University of Michigan and the profession of engineering.” At Columbia, Qu’s doctoral work was recognized with the SPARS’15 Best Student Paper Award and a Microsoft PhD Fellowship in 2016.
“It was a deeply rewarding and formative journey to pursue my Ph.D. at Columbia University. I am profoundly grateful to my advisor, Prof. John Wright, who set an extraordinary example for me as both a researcher and a mentor. He invested tremendous effort not only in my research, but also in my long-term career development. The PhD training experience at Columbia has been lasting and life-changing, and it has paved the foundation for success and leadership as a tenure-track faculty at the University of Michigan,” he said.
Bringing his research enthusiasm into the curriculum, Qu designed and implemented ECE 453 Principles of Machine Learning, transforming it from a seminar into a core undergraduate course. He also instructs the graduate-level EECS 559 Optimization in Signal Processing and Machine Learning. Qu receives outstanding feedback for these courses, with students praising his accessibility and his ability to inspire them to pursue advanced topics in machine learning.
Beyond his university teaching, Qu launched the “AI Magic” camp, a course on generative AI for high school students offered through U-M ECE’s Electrify program. He has also developed and delivered numerous tutorials and short courses on the mathematical foundations of deep learning and generative AI at top-tier conferences, including ICASSP, CVPR, ICCV, and ICML. As a leader in the field, he co-founded the Conference on Parsimony and Learning (CPAL) and has co-organized multiple high-impact workshops and symposia (such as DeepMath and MIDAS Symposiums) to disseminate the latest research findings and foster community growth on mathematical foundations of generative AI.
Dr. Qu is widely recognized as a patient and encouraging mentor who prioritizes his students' well-being and success. Leading a vibrant group of 10 PhD students alongside several postdocs and undergraduates, he has cultivated a highly collaborative research environment. This supportive culture has translated into significant success: his trainees frequently secure spotlight and oral presentations at top-tier venues like ICML, NeurIPS, and ICLR. Additionally, his students have received numerous prestigious awards, including three Rackham Predoctoral Fellowships, three CPAL Rising Star Awards, one MMLS’24 Best Student Poster Award, and one Best Paper Award at the NeurIPS’23 Diffusion Model Workshop. His mentorship has also launched successful careers, with four former postdocs securing faculty positions in universities and national laboratories.
