Imperial College London

DrShenglongZhou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate in Communications and Signal Processing
 
 
 
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Contact

 

shenglong.zhou CV

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

Employment History


Research Associate, 2021-present
Department of EEEImperial College London, UK

Teaching Fellow, 2020-2021
Research Fellow, 2017-2020
School of Mathematics, University of Southampton, UK

Education Background


PhD in Operational Research, 2014-2017
School of MathematicsUniversity of Southampton, UK

MSc in Operational Research, 2011-2014
BSc in Information and Computing Sciences, 2007-2011
Department of MathematicsBeijing Jiaotong University, China

Research Interests


His research interests include the theory and methods of optimization in the fields of sparse optimization, low-rank matrix optimization, 0/1 loss optimization, bilevel optimization, and machine learning-related optimization.

Key Links

Homepage, ResearchGate, Github, GoogleScolar

Selected Publications

Journal Articles

Zhou S, Pan L, Xiu N, et al., 2021, Quadratic convergence of smoothing Newton's method for 0/1 loss optimization, SIAM Journal on Optimization, Vol:31, ISSN:1052-6234, Pages:3184-3211

Zhou S, 2021, Sparse SVM for sufficient data reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN:0162-8828, Pages:1-11

Zhou S, Shang M, Pan L, et al., 2021, Newton hard-thresholding pursuit for sparse linear complementarity problem via a new merit function, SIAM Journal on Scientific Computing, Vol:43, ISSN:1064-8275, Pages:A772-A799

Zhou S, Xiu N, Qi HD, 2021, Global and quadratic convergence of newton hard-thresholding pursuit, Journal of Machine Learning Research, Vol:22, ISSN:1532-4435

Zhou S, Xiu N, Qi H-D, 2020, Robust Euclidean embedding via EDM optimization, Mathematical Programming Computation, Vol:12, ISSN:1867-2949, Pages:337-387

Zhou S, Xiu N, Qi H-D, 2018, A Fast Matrix Majorization-Projection Method for Penalized Stress Minimization With Box Constraints, IEEE Transactions on Signal Processing, Vol:66, ISSN:1053-587X, Pages:4331-4346

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