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Speaker Profile

Yue M. Lu

Harvard University


​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 an Associate Professor of Electrical Engineering and directing the Signals, Information, and Networks Group (SING) at the Harvard John A. Paulson School of Engineering and Applied Sciences.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 and the Best Student Paper Award (with Ameya Agaskar and Chuang Wang) of IEEE Global Conference on Signal and Information Processing (GlobalSIP) in 2014. He is a recipient of the 2015 ECE Illinois Young Alumni Achievement Award. He has been an Associate Editor of the IEEE Transactions on Image Processing since December 2014, an Elected Member of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee since January 2015, and an Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee since January 2016.

All sessions by Yue M. Lu

  • MondayNovember 6
2:30 PM

From Statics to Dynamics and from Convexity to Nonconvexity: the Scaling Limit of Iterative Algorithms for High-Dimensional Inference