Yue M. Lu was born in Shanghai. After finishing undergraduate studies at Shanghai Jiao Tong University, he attended the University of Illinois at Urbana-Champaign, where he received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering, both in 2007.
From September 2007 and October 2010, he was a postdoctoral researcher at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He then joined Harvard University, where he is currently Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at the Harvard John A. Paulson School of Engineering and Applied Sciences. He is also fortunate to have held visiting appointments at Duke University in 2016 and at the École Normale Supérieure (ENS) in 2019.
He received the Most Innovative Paper Award of IEEE International Conference on Image Processing (ICIP) in 2006 for his paper (with Minh N. Do) on the construction of directional multiresolution image representations, the Best Student Paper Award of IEEE ICIP in 2007, and the Best Student Presentation Award at the 31st SIAM SEAS Conference in 2007. Student papers supervised and coauthored by him won the Best Student Paper Award (with Ivan Dokmanic and Martin Vetterli) of IEEE International Conference on Acoustics, Speech and Signal Processing in 2011, the Best Student Paper Award (with Ameya Agaskar and Chuang Wang) of IEEE Global Conference on Signal and Information Processing (GlobalSIP) in 2014, and the Best Student Paper (First Prize) of the IEEE CAMSAP Workshop (with Oussama Dhifallah) in 2017. He is a recipient of the 2015 ECE Illinois Young Alumni Achievement Award.
He is a Member of the IEEE Signal Processing Theory and Methods Technical Committee, a Member of the IEEE SP Society Big Data Special Interest Group (SIG), a Member of the IEEE Machine Learning for Signal Processing Technical Committee, and an Associate Editor of the IEEE Transactions on Signal Processing. In the past, he has also served as an Associate Editor of the IEEE Transactions on Image Processing, and as a Member of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee.
Exploring and Exploiting High-dimensional Phenomena in Statistical Learning and Inference
The massive datasets being compiled by our society present new challenges and opportunities to the field of statistical learning and inference. The increasing dimensionality of modern datasets lead to unique geometric and probabilistic phenomena, including scaling limits, phase transitions, and universality. A deeper understanding and clever exploitation of such fascinating (and sometimes counter-intuitive) high-dimensional phenomena can translate to both theoretical breakthroughs and novel algorithms.