Summary
Kenneth (Kezhi) Li is currently a senior research associate at Dept. of Electrical and Electronic Engineering, Imperial College London. He obtained the PhD degree in the EEE department at Imperial College London in 2013, and B.Eng. degree in Electronic Engineering from and University of Science and Technology of China (USTC), Hefei, China, in 2008, respectively.
He is a key member in the Centre of Bio-inspired Technology (CBT). His research interests are quite broad in several inter-discipline areas, such as using artificial intelligence in diabetes research, seeking for the relation between organism's genotype and phenotype, statistical signal processing (compressive sensing) and their applications in imaging system and quantum tomography. Before joining CBT, he used to be a research scientist at London Institute of Medical Science, a research associate at University of Cambridge, a research fellow at Royal Institute of Technology (KTH) in Stockholm and a research assistant at Microsoft Research Asia (MSRA) and USTC.
Research Interests
Signal Processing and Data Science, especially Compressed Sensing, Machine/Deep Learning in Diabetes and behaviour genomics, Image/Video Processing, Convex/Nonconvex Optimization, Sparse/Low-Rank Machine Learning, Quantum State Tomography, Inverse Problems, System Identification.
Selected Publications
Journal Articles
Li K, Zheng K, Yang J, et al., 2017, Hybrid reconstruction of quantum density matrix: when low-rank meets sparsity, Quantum Information Processing, Vol:16, ISSN:1570-0755
Yang J, Cong S, Liu X, et al., 2017, Effective quantum state reconstruction using compressed sensing in NMR quantum computing, Physical Review A, Vol:96, ISSN:1050-2947
Li K, Zhang J, Cong S, 2017, Fast reconstruction of high-qubit-number quantum states via low-rate measurements, Physical Review A, Vol:96, ISSN:2469-9926
Zheng K, Li K, Cong S, 2016, A reconstruction algorithm for compressive quantum tomography using various measurement sets, Scientific Reports, Vol:6, ISSN:2045-2322
Li K, Sundin M, Rojas CR, et al., 2015, Alternating strategies with internal ADMM for low-rank matrix reconstruction, Signal Processing, Vol:121, ISSN:0165-1684, Pages:153-159
Li K, Gan L, Ling C, 2013, Convolutional Compressed Sensing Using Deterministic Sequences, IEEE Transactions on Signal Processing, Vol:61, ISSN:1053-587X