Information for students, faculty and staff regarding COVID-19. (Updated: 26 February 2021)

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Umeå University
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Staff photo Jun Yu

Jun Yu

Full Professor in Mathematical Statistics with focus on Statistical Learning and Inference for Spatiotemporal Data, including fundamental research and applications in AI.

Contact

E-mail
jun.yu@umu.se
Phone
+46 90 786 51 27

Works at

Affiliation
Professor at Department of Mathematics and Mathematical Statistics Role: Director of studies
Location
MIT-huset, plan 3, Matematik och matematisk statistik, F348 Umeå universitet, 901 87 Umeå
Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
Test (Madrid)
Ghorbani, Mohammad; Cronie, Ottmar; Mateu, Jorge; et al.
An extended block restricted isometry property for sparse recovery with non-Gaussian noise
Journal of Computational Mathematics, Global Science Press 2020, Vol. 38, (6) : 797-808
Leffler, Klara; Zhou, Zhiyong; Yu, Jun
Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers
Econometrics and Statistics
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.
A Method for Identification of Mechanical Response of Motor Units in Skeletal Muscle Voluntary Contractions using Ultrafast Ultrasound Imaging: Simulations and Experimental Tests
IEEE Access, IEEE 2020, Vol. 8 : 50299-50311
Rohlén, Robin; Stålberg, Erik; Stoverud, Karen-Helene; et al.
Imaging recruitment of motor units in voluntary skeletal muscle contractions using decomposition and ultrafast ultrasound imaging: A pilot study
2020 ISEK Virtual Congress Poster Abstract Booklet, International Society of Electrophysiology and Kinesiology 2020 : 141-142
Rohlén, Robin; Stålberg, Erik; Yu, Jun; et al.
A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia using the WRF model
Advances in Atmospheric Sciences, Springer 2020, Vol. 37 : 57-74
Wang, Jianfeng; Fonseca, Ricardo M.; Rutledge, Kendall; et al.
Effects of winter climate on high speed passenger trains in Botnia-Atlantica region
Wang, Jianfeng; Granlöf, Markus; Yu, Jun
Statistical inference for block sparsity of complex-valued signals
IET Signal Processing, Institution of Engineering and Technology 2020, Vol. 14, (3) : 154-161
Wang, Jianfeng; Zhou, Zhiyong; Yu, Jun
Minimization of the q-ratio sparsity with 1<q≤∞ for signal recovery
Zhou, Zhiyong; Yu, Jun
Phaseless compressive sensing using partial support information
Optimization Letters, Springer 2020, Vol. 14 : 1961-1973
Zhou, Zhiyong; Yu, Jun
Model-based computed tomography image estimation: partitioning approach
Journal of Applied Statistics, Taylor & Francis 2019, Vol. 46, (14) : 2627-2648
Bayisa, Fekadu; Yu, Jun
Adaptive algorithm for sparse signal recovery
Digital signal processing (Print), Elsevier 2019, Vol. 87 : 10-18
Bayisa, Fekadu; Zhou, Zhiyong; Cronie, Ottmar; et al.
Functional marked point processes: Unifying spatio-temporal frameworks and analysing spatially dependent functional data
Statistical Analysis for Space-Time Data: Pragramme and Abstract Book, Eurpean Courses in Advanced Statistics (ECAS) 2019 : 7-7
Cronie, Ottmar; Ghorbani, Mohammad; Yu, Jun; et al.
Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
20th Workshop on Stochastic Geometry, Stereology and Image Analysis 2–7 June, 2019, Sandbjerg Estate, Denmark: Abstract book : 13-13
Ghorbani, Mohammad; Cronie, Ottmar; Mateu, Jorge; et al.
Sharper Positron Emission Tomography: Intelligent Data Sampling to Promote Accelerated Medical Imaging
Winter Conference in Statistics 2019 - Machine Learning, March 10-14, 2019, Hemavan, Sweden
Leffler, Klara; Axelsson, Jan; Larsson, Anne; et al.
Efficient surface finish defect detection using reduced rank spline smoothers
CRoNoS & MDA 2019
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Effective degrees of freedom for surface finish defect detection and classification
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Defect detection and classfiication: statistical learning approach - Part II
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Weather Simulation Uncertainty Estimation using Bayesian Hierarchical Model
Journal of Applied Meteorology and Climatology, American Meteorological Society 2019, Vol. 58, (3) : 585-603
Wang, Jianfeng; Fonseca, Ricardo M.; Rutledge, Kendall; et al.
Bayesian sparsity estimation in compressive sensing with application to MR images
Communications in Statistics: Case Studies, Data Analysis and Applications, Taylor & Francis Group 2019, Vol. 5, (4) : 415-431
Wang, Jianfeng; Zhou, Zhiyong; Garpebring, Anders; et al.
Statistical inference for block sparsity of complex signals
Wang, Jianfeng; Zhou, Zhiyong; Yu, Jun
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values
Wang, Jianfeng; Zhou, Zhiyong; Yu, Jun
Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values
EURASIP Journal on Advances in Signal Processing, Springer 2019, Vol. 2019
Wang, Jianfeng; Zhou, Zhiyong; Yu, Jun
Statistical modelling for spatiotemporal data
The Hawassa Math&Stat Conference 2019, Hawassa, Ethiopia, February 11-15, 2019
Yu, Jun
Stable and robust ℓp-constrained compressive sensing recovery via robust width property
Journal of the Korean Mathematical Society, Korean Mathematical Society 2019, Vol. 56, (3) : 689-701
Yu, Jun; Zhou, Zhiyong
Sparse recovery based on q-ratio constrained minimal singular values
Signal Processing, Elsevier 2019, Vol. 155 : 247-258
Zhou, Zhiyong; Yu, Jun
A New Nonconvex Sparse Recovery Method for Compressive Sensing
Frontiers in Applied Mathematics and Statistics, Frontiers Media S.A. 2019, Vol. 5 : 1-11
Zhou, Zhiyong; Yu, Jun
Recovery analysis for weighted mixed ℓ2/ℓp minimization with 0 < p ≤ 1
Journal of Computational and Applied Mathematics, Elsevier 2019, Vol. 352 : 210-222
Zhou, Zhiyong; Yu, Jun
Adaptive estimation for varying coefficient modelswith nonstationary covariates
Communications in Statistics - Theory and Methods, Taylor & Francis 2019, Vol. 48, (16) : 4034-4050
Zhou, Zhiyong; Yu, Jun
On q-ratio CMSV for sparse recovery
Signal Processing, Elsevier 2019, Vol. 165 : 128-132
Zhou, Zhiyong; Yu, Jun
Statistical learning in computed tomography image estimation
Medical physics (Lancaster), John Wiley & Sons 2018, Vol. 45, (12) : 5450-5460
Bayisa, Fekadu; Liu, Xijia; Garpebring, Anders; et al.
Comparative analysis of nonlinear growth curve models for Arabidopsis thaliana rosette leaves
Acta Physiologiae Plantarum, Springer 2018, Vol. 40, (6)
Jiao, Xiang; Zhang, Huichun; Zheng, Jiaqiang; et al.
Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images
Communications in Statistics: Case Studies, Data Analysis and Applications, Taylor & Francis Group 2018, Vol. 4, (1) : 46-55
Kuljus, Kristi; Bayisa, Fekadu; Bolin, David; et al.
Intelligent data sampling promotes accelerated medical imaging: sharper positron emission tomography
The 6th Swedish Workshop on Data Science (SweDS18), Umeå, Sweden, November 20-21, 2018
Leffler, Klara; Häggström, Ida; Yu, Jun
An Extended Block Restricted Isometry Property for Sparse Recovery with Non-Gaussian Noise
COMPSTAT 2018, Iasi, Romania, August 28-31, 2018
Leffler, Klara; Zhou, Zhiyong; Yu, Jun
Defect detection and classification: statistical learning approach
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Automated surface finish defect detection using statistical learning approach
SweDS2018, Umeå University, Sweden, November 20-21, 2018
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Ultrasound-based Imaging of Motor Units in Skeletal Muscle Tissue
Medicinteknikdagarna 2018, 9-10 oktober, Umeå
Rohlén, Robin; Stålberg, Erik; Stoverud, Karen-Helene; et al.
Athletic ability in childhood and adolescence as a predictor of participation in non-elite sports in young adulthood
Sport in Society: Cultures, Media, Politics, Commerce, Routledge 2018, Vol. 21, (11) : 1686-1703
Söderström, Tor; Fahlén, Josef; Ferry, Magnus; et al.
Intramolecular 13C analysis of tree rings provides multiple plant ecophysiology signals covering decades
Scientific Reports, Vol. 8
Wieloch, Thomas; Ehlers, Ina; Yu, Jun; et al.
Tree-ring cellulose exhibits several interannual 13C signals on the intramolecular level
Geophysical Research Abstracts
Wieloch, Thomas; Ehlers, Ina; Yu, Jun; et al.
Sparse recovery based on q-ratio constrained minimal singular values
Zhou, Zhiyong; Yu, Jun
Model-based Estimation of Computed Tomography Images
Bayisa, Fekadu; Yu, Jun
Model-based Estimation of Computed Tomography Images
3rd International Researchers, Statisticians and Young Statisticians Congress: Abstract Book, Selcuk University 2017 : 84-
Bayisa, Fekadu; Yu, Jun
Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images
Kuljus, Kristi; Bayisa, Fekadu; Bolin, David; et al.
Segmentation of Motor Unit Territories in Ultrasound Image Sequences of Contracting Skeletal Muscle Tissue
Medicinteknikdagarna 2017, 10-11 oktober, Västerås
Rohlén, Robin; Stoverud, Karen-Helene; Yu, Jun; et al.
Sparsity Estimation of MR images in Compressive Sensing
IRSYSC 2017 – 3rd International Researchers, Statisticians and Young Statisticians Congress, Konya, Turkey, May 2017.
Wang, Jianfeng; Yu, Jun
Sparsity estimation in compressive sensing with application to MR images
Wang, Jianfeng; Zhou, Zhiyong; Garpebring, Anders; et al.
Estimation of block sparsity in compressive sensing
Zhou, Zhiyong; Yu, Jun
Estimation of block sparsity in compressive sensing
FOCM 2017 - Workshop C1 Computational Harmonic Analysis and Compressive Sensing: Abstract booklet
Zhou, Zhiyong; Yu, Jun
View publications in DiVA