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

jun.yu@umu.se

+46 90 786 51 27

Works at

Professor at Department of Mathematics and Mathematical Statistics Role: Director of studies

MIT-huset, plan 3, Matematik och matematisk statistik, F348 Umeå universitet, 901 87 Umeå

Statistical Learning of Phase Measuring Deflectometry for Defect Detection and Classification on Specular Cab Body Surfaces

Advances in Signal Processing: Reviews: Book Series, Volume 2, International Frequency Sensor Association Publishing 2021 : 309-342

Pya Arnqvist, Natalya; Lindahl, Eric; Yu, Jun

Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers

Econometrics and Statistics, Elsevier 2021, Vol. 18 : 89-105

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.

Effects of winter climate on delays of high speed passenger trains in Botnia-Atlantica region

Journal of Rail Transport Planning & Management, Elsevier 2021, Vol. 18

Wang, Jianfeng; Granlöf, Markus; Yu, Jun

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

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 ℓ_p-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/ℓ_p 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.

View publications in DiVA

Research groups

Head of research

Statistical Learning and Inference for Spatio-Temporal Data

Research projects

1 September 2018 until 28 February 2022

NoICE - Nordic Icing Centre of Expertise

1 July 2018 until 30 June 2020

Large scale analysis of tree growth in space and time under changing climate conditions

1 January 2016 until 30 June 2019

Final Inspection and Quality Analysis

1 January 2016 until 31 December 2024

Pioneering ultrasound imaging methods for neuromuscular diagnostics

1 December 2015 until 31 May 2018

WindCoE - Nordic Wind Energy Center

1 January 2014 until 31 December 2018

Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment

1 August 2008 until 30 June 2011

Development of biostochastic methods for analysis of spatio-temporal signals – with applications to muscle physiology and cancer tumor biology