PhD Alumnus Qing Qu Receives NSF Career Award
EE PhD alumnus (2018) Qing Qu received an NSF Career Award in support of early-career faculty who have the potential to serve as academic role models in both research and education and can advance the mission of their respective department or organization. Dr. Qu received his PhD under the guidance of Prof. John Wright.
He received this award for his project titled "CAREER: From Shallow to Deep Representation Learning: Global Nonconvex Optimization Theories and Efficient Algorithms". In this project, he and his students will aim to develop a principled and unified framework for learning succinct representation in high-dimensional space via nonconvex optimization methods. This project will not only enrich the mathematical theory of signal processing, optimization, and machine learning, but also have broad impacts on many other practical areas in engineering and science where (deep) representation learning has already made significant advances. More information on his project can be found on the NSF website.
Currently, Dr. Qu is an assistant professor at EECS Department, University of Michigan - Ann Arbor. Prior to that, Dr. Qu received his PhD from Columbia University in 2018, where his PhD work has been recognized by a couple of awards, such as SPARS’15 best student paper award and the Microsoft PhD fellowship in 2016. He was a Moore-Sloan fellow at NYU Center for Data Science from 2018 to 2020. Now at the University of Michigan, together with his students, Dr. Qu is developing computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry. These data-driven methods have the potential to transform many applications in imaging sciences, scientific discovery, healthcare, and beyond. In the meanwhile, Dr. Qu is also developing new machine learning courses, particularly for EE students at both undergraduate and graduate levels, such as EECS 453 Principles of Machine Learning and EECS 559 Optimization Methods for SIPML at the University of Michigan.
“It has been such a rewarding and wonderful journey when I was doing my PhD at Columbia University. I am greatly indebted to my advisor Prof. John Wright, who set such a great examplar for me as a researcher and mentor, spending tremendous effort on my research and long-term career development. It has been a long-lasting and life-changing influence on me, and I will do the same to my students at Michigan”, he said.