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Education and Career

11/18 - now: Postdoctoral Research Associate, DAMTP, University of Cambridge

08/15 - 07/18: PhD Student, The Chinese University of Hong Kong

 

Research

I am a Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. My research interests are machine learning, inverse problems, image processing and numerical linear algebra

My google scholar profile.

 

Selected Publications

R. Ke and C.B. Schönlieb. Unsupervised image restoration using partially linear denoisers. IEEE Trans. Pattern Anal. Mach. Intell., 2021. DOI: 10.1109/TPAMI.2021.3070382
 
R. Ke, A. Bugeau, N. Papadakis, M. Kirkland, P. Schuetz, and C.B. Schönlieb. Multitask deep learning for image segmentation using recursive approximation tasks. IEEE Trans. Image Process., 30:3555–3567, 2021.
 
R. Ke, R. Wagner, R. Ramlau, and R. Chan. Reconstruction of the high resolution phase in a closed loop adaptive optics system. SIAM J. Imaging Sci., 13(2):775–806, 2020.
 
R. Ke, M. Ng, and T. Wei. Efficient preconditioning for time fractional diffusion inverse source problems. SIAM J. Matrix Anal. Appl., 41(4):1857–1888, 2020.
 
J. Pan, R. Ke, M. Ng, and H. Sun. Preconditioning techniques for diagonal-times-Toeplitz matrices in fractional diffusion equations. SIAM J. Sci. Comput., 36(6): A2698–A2719, 2014.

 

 

 

Publications

Unsupervised Image Restoration Using Partially Linear Denoisers
R Ke, C-B Schonlieb
– IEEE Trans Pattern Anal Mach Intell
(2021)
PP,
1
Learning to Segment Microscopy Images with Lazy Labels
R Ke, A Bugeau, N Papadakis, P Schuetz, CB Schönlieb
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2021)
12535 LNCS,
411
Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks.
R Ke, A Bugeau, N Papadakis, M Kirkland, P Schuetz, C-B Schonlieb
– IEEE Transactions on Image Processing
(2021)
30,
3555
EFFICIENT PRECONDITIONING for TIME FRACTIONAL DIFFUSION INVERSE SOURCE PROBLEMS
R Ke, MK Ng, T Wei
– SIAM Journal on Matrix Analysis and Applications
(2020)
41,
1857
IUNets: Learnable invertible up-and downsampling for large-scale inverse problems
C Etmann, R Ke, CB Schonlieb
– PROCEEDINGS OF THE 2020 IEEE 30TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
(2020)
2020-September,
1
Reconstruction of the High Resolution Phase in a Closed Loop Adaptive Optics
R Ke, R Wagner, R Ramlau, R Chan
– SIAM Journal on Imaging Sciences
(2020)
13,
775

Research Group

Cambridge Image Analysis

Room

F2.08

Telephone

01223 337867