Zhanyu Ma

Zhanyu MaDr. Zhanyu Ma has been an associate Professor at Beijing University of Posts and Telecommunications (BUPT), Beijing, China, since 2014. He received his M.Eng. degree in Signal and Information Processing from BUPT (Beijing University of Posts and Telecommunications), China, and his Ph.D. degree in Electrical Engineering from KTH (Royal Institute of Technology), Sweden, in 2007 and 2011, respectively. From 2012-2013, he has been a Postdoctoral research fellow in the School of Electrical Engineering, KTH, Sweden. From 2013-2014, he served as an assistant professor at Beijing University of Posts and Telecommunications. His research interests include statistical modeling and machine learning related topics with a focus on applications in speech processing, image processing, biomedical signal processing, bioinformatics, and intelligent energy network.

Email:mazhanyu AT bupt.edu.cn

Professional Experience

  • Associate Professor,  BUPT, 2014.12 till now
  • Adjunct Associate Professor, AAU, 2015.7-2020.6 (Danish: Adjungeret Lektor)
  • Visiting Scholar, UCL, 2014.07 — 2014.08
  • Assitant Professor, BUPT, 2013.08 — 2014.11
  • Postdoc Researcher, KTH, 2012.01 — 2013.06

Educational Experience

  • Ph. D., KTH, 2007.09 — 2011.12

Main Fundings

  • “Non-Gaussian Probabilistic Modeling and Analysis for Massive Urban Data”, Beijing Nova Program, Grant No. Z171100001117049, 2017.01-2019.12. (PI)
  • “Imbalance Learning of High-Dimensional Large-scale Non-Gaussian Data”, National Natural Science Foundation of China (NSFC),Grant No.61628301,2017.01-2018.12. (Co-PI)
  • “Non-Gaussian Cross-domain Analysis-based Research on Multi-view Depth Image Enhancement”, Beijing Natural Science Foundation (BNSF),Grant No. 4162044,2016.01-2018.12. (PI)
  • “Non-Gaussian Statistical Model-based Cross-domain Visual Analysis”, National Natural Science Foundation of China (NSFC),Grant No.61511130081,2015.04-2017.03. (PI)
  • “Study of Efficient Variational Approximate Inference Method for Non-Gaussian Probabilistic Models”, National Natural Science Foundation of China (NSFC),Grant No.61402047,2015.01-2017.12. (PI)
  • “Single Channel Speech Enhancement based on Variational Inference Framework with Bayesian Prior Information”, the Scientific Research Foundation for Returned Scholars, Ministry of Education of China,2015.01-2016.12. (PI)
  • “Big Data Processing in Wireless Networks”, Sweden STINT initiation grant, 2015.7-2016.6. (PI)
  • Project 66004, “New advanced probabilistic models for multimedia signal processing”, KTH research funding, 2010.9-2013.6. (PI)
  • AVTAL-12-173, “Developing a new strategy for the operation of the CHP and district heating system based on the end user heat demand” Ångpanneföreningens Forskningsstiftelse research funding, 2012.9-2013.9. (Co-PI)

Academic service

Publications

Ph. D. thesis

  • Zhanyu Ma, “Non-Gaussian Statistical Models and Their Applications”, PhD Thesis, KTH-Royal Institute of Technology, 2011.

Journal papers

(Impact factors:TPAMI: 6.077, TNNLS: 4.854, J-STSP: 2.569, KBS: 3.325, TASLP: 1.877, PR: 3.399, ENB: 2.973, SP: 2.063, NEUROCOMPUTING: 2.392)

  1. Zhanyu Ma*, Jiyang Xie, Hailong Li*, Qie Sun*, Zhongwei Si, Jianhua Zhang, and Jun Guo, “The Role of Data Analysis in the Development of Intelligent Energy Networks”, IEEE Network Magazine, accepted, 2017.
  2. Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma*, Liang Wang, Tao Xiang, W. Bastiaan Kleijn, Jun Guo, “Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval”, NEUROCOMPUTING, accepted, 2017.
  3. Hong Yu, Zheng-Hua Tan, Yiming Zhang, Zhanyu Ma*, and Jun Guo, “DNN Filter Bank Cepstral Coefficients for Spoofing Detection”, IEEE ACCESS, Vol.5, pp. 4779 – 4787, March 2017. DOI
  4. Zhanyu Ma*, Jing-Hao Xue, Arne Leijon, Zheng-Hua Tan, Zhen Yang, and Jun Guo, “Decorrelation of Neutral Vector Variables: Theory and Applications”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2016. DOI
  5. Zhanyu Ma, Hong Yu*, Zheng-Hua Tan, and Jun Guo, “Text-Independent Speaker Identification Using the Histogram Transform Model”, IEEE ACCESS, Vol. 4, pp. 9733 – 9739, Jan. 2017. DOI
  6. Qie Sun, Hailong Li, Zhanyu Ma*, Chao Wang, Javier Campillo, Fredrik Wallin, and Jun Guo, “A Comprehensive Review of Smart Meters in Intelligent Energy Networks”, IEEE Internet of Things Journal (IoT-J), Volume 3, Issue 4, pp. 464-479, Aug. 2016. DOI
  7. Zhongwei Si, Hong Yu, and Zhanyu Ma*,“Learning Deep Features for DNA Methylation Data Analysis”, IEEE ACCESS, vol. 4, pp. 2732 – 2737, June 2016. DOI
  8. Chunyun Zhang, Zhongwei Si, Zhanyu Ma*, Xiaoming Xi, Yilong Yin, “Mining Sequential Update Summarization with Hierarchical Text Analysis”, Mobile Information Systems, vol. 2016, Article ID 1340973, 10 pages, 2016. DOI
  9. Zhanyu Ma*, Zheng-Hua Tan, and Jun Guo, “Feature Selection for Neutral Vector in EEG Signal Classification”, NEUROCOMPUTING, vol. 174, pp. 937-945, Jan. 2016. DOI
  10. Mikael Davis*, Zhanyu Ma*, Weiru Liu, Paul Miller, Ruth Hunter, Frank Kee, “Generating realistic labelled, weighted random graphs”, Algorithms, 8(4), 1143-1174, 2015.DOI
  11. Zhanyu Ma*, Jalil Taghia, W. Bastiaan Kleijn, Arne Leijon, and Jun Guo, “Line Spectral Frequencies Modeling by a Mixture of Von Mises-Fisher Distributions”, Signal Processing (SP), vol. 114, 219-224, Sep. 2015. DOI
  12. Chunyun Zhang, Yichang Zhang, Weiran Xu*, Zhanyu Ma, Yan Leng, and Jun Guo, “Mining Activation Force Dened Dependency Patterns for Relation Extraction”, Knowledge-Based Systems(KBS), vol. 86, pp. 278–287, Sep. 2015DOI
  13. Chunyun Zhang*, Weiran Xu, Zhanyu Ma, Sheng Gao, Qun Li, and Jun Guo, “Construction of Semantic Bootstrapping Models for Relation Extraction”, Knowledge-Based Systems(KBS), 83, 128-137, July 2015. DOI
  14. Kaili Zhao*, Honggang Zhang, Zhanyu Ma, Yi-Zhe Song, Jun Guo, “Multi-label learning with prior knowledge for facial expression analysis”, Neurocomputing, vol 157, pp. 280-289, June 2015. DOI
  15. Zhanyu Ma*, Andrew E. Teschendorff, Arne Leijon, Yuanyuan Qiao, Honggang Zhang, and Jun Guo, “Variational Bayesian Matrix Factorization for Bounded Support Data”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Volume 37, Issue 4, pp. 876 – 889, Apr. 2015. DOI
  16. Pravin K. Rana*, Jalil Taghia, Zhanyu Ma, and Markus Flierl, “Probabilistic Multiview Depth Image Enhancement Using Variational Inference”, IEEE Journal of Selected Topics in Signal Processing (J-STSP), Vol. 9, No. 3, pp. 435 – 448, April 2015. DOI
  17. Yuanyuan Qiao*, Jie Yang, Haiyang He, Yihang Cheng, and Zhanyu Ma, “User location prediction with energy efficiency model in the LTE network”, International Journal of Communicatino Systems (IJCS), Jan. 2015.
  18. Zhanyu Ma, Hailong Li, Qie Sun*, Chao Wang, Aibin Yan, and Fredrik Starfel,  “Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems”, Energy and Buildings(ENB), pp. 464–472, Volume 85, December 2014. DOI
  19. Zhanyu Ma*, Saikat Chatterjee, W. Bastiaan Kleijn, and JunGuo, “Dirichlet Mixture Modeling to Estimate an Empirical Lower Bound for LSF Quantization Signal Processing”, Signal Processing (SP), Volume 104, pp. 291-295, November 2014. DOI
  20. Zhanyu Ma*, Pravin K. Rana, Jalil Taghia, Markus Flierl, and Arne Leijon, “Bayesian Estimation of Dirichlet Mixture Model with Variational Inference”, Pattern Recognition (PR), Volume 47, Issue 9, pp. 3143-3157, September 2014. DOI
  21. Jalil Taghia*, Zhanyu Ma, and Arne Leijon, “Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Volume:36, Issue9, pp. 1701-1715, September, 2014. DOI
  22. Zhanyu Ma*, Andrew E. Teschendorff, Hong Yu, Jalil Taghia, and Jun Guo, “Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis”, International Journal of Molecular Sciences (IJMS), 15(6), 10835-10854, 2014. DOI
  23. Zhanyu Ma*, Arne Leijon, Zheng-Hua Tan, and Sheng Gao, “Predictive Distribution of the Dirichlet Mixture Model by Local Variational Inference”, Journal of Signal Processing System (JSPS), Volume 74, Issue 3, pp.359-374, 2014. DOI
  24. Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick. Gallinari, Zhanyu Ma, Jun Guo, “A Cross-Domain Recommendation Model for Cyber-Physical Systems”, IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 2, pp. 384-393, Dec. 2014. DOI
  25. Zhanyu Ma*, Arne Leijon, and W. Bastiaan Kleijn, “Vector Quantization of LSF Parameters with a Mixture of Dirichlet Distributions”, IEEE Trans. on Audio, Speech, and Language Processing (TASLP), vol.21, no.9, pp.1777-1790, Sept. 2013. DOI
  26. Zhanyu Ma and Andrew E. Teschendorff*, “A Variational Bayes Beta Mixture Model for Feature Selection in DNA Methylation Studies”, Journal of Bioinformatics and Computational Biology (JBCB), Vol. 11, No. 4 (2013) 1350005 (19 pages), 2013. DOI
  27. Zhanyu Ma*, Arne Leijon, “Bayesian Estimation of Beta Mixture Models with Variational Inference”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 33, pp. 2160 – 2173, Nov. 2011. DOI (被Nature引用)

Conference papers

  1. H. Yu, Z.-H. Tan, Z. Ma*, and J. Guo, “Adversarial Network Bottleneck Features for Noise Robust Speaker Verification”, INTERSPEECH, 2017.
  2. X. Ma, J. Zhang, Y. Zhang, Z. Ma, and Y. Zhang, “A PCA-based Modeling Method for Wireless MIMO Channel”, INFOCOM, 2017.
  3. J. Xie, H. Li*, Z. Ma*, Q. Sun*, F. Wallin, Z. Si, and J. Guo, “Analysis of Key Factors in Heat Demand Prediction with Neural Networks”, in Proceedings of International Conference on Applied Energy (ICAE), Oct. 8-11, 2016.
  4. P. Xu, Q. Yin, Y. Qi,  Y.-Z. Song, Z. Ma*, L. Wang, J. Guo, “Instance-level Coupled Subspace Learning for Fine-grained Sketch-based Image Retrieval”, in Proceedings of European Conference on Computer Vision (ECCV), Oral, Oct. 8-16, 2016.
  5. P. Xu, K. Li, Z. Ma*, Y.-Z. Song, L. Wang, J. Guo, “Cross-modal Subspace Learning for Sketch-based Image Retrieval: A Comparative Study”, in Proceedings of IEEE International Conference on Network Infrastructure and Digital Content, Sept. 23-25, 2016. (Best Paper Award)
  6. H. Zhou, N. Zhang, D. Huang, Z. Ma*, W. Hu, and J. Guo, “Activation Force-based Air Pollution Tracing”, in Proceedings of IEEE International Conference on Network Infrastructure and Digital Content, Sept. 23-25, 2016.
  7. Z. Wang*, Y. Qi, J. Liu, and Z. Ma, “User Intention Understanding From Scratch”, IEEE International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 6-8 July, 2016.
  8. Y. Chen, Y. Li, F. Qi, Z. Ma, H. Zhang, “Cycled Merging Registration of Point Clouds for 3D Human Body Modeling”, IEEE International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 6-8 July, 2016.
  9. H. Yu, A. Sarkar, D. A. L. Thomsen, Z.-H. Tan , Z. Ma , and J. Guo, “Effect of Multi-condition Training and Speech Enhancement Methods on Spoofing Detection”, IEEE International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 6-8 July, 2016.
  10. Y. Sun, W. Wang, H. Zhang, Z. Ma, “Detecting Formula based on Stroke Width Transform for online Chinese Examination Question Retrieval”, IEEE Digital Media Industry and Academic Forum, 4-6 July, 2016.
  11. L. Qi, Y. Qiao, F. B. Abdesslem, Z. Ma, J. Yang, “Oscillation Resolution for Massive Cell Phone Traffic Data”, ACM MobiSys Workshop on Mobile Data, 30 June, 2016.
  12. V. Nian, Q. Sun, Z. Ma, and H. Li. “A comparative cost assessment of energy production from central heating plant or power plant waste heat recovery “, CUE2016-Applied Energy Symposium and Forum, June 13-15, 2016.
  13. D. Huang, N. Zhang, H. Yu, H. Zhou, Z. Ma*, W. Hu, and J. Guo, “Activation Force-based Air Pollution Observation Station Clustering”, QSHINE, 19-20 August, Taipei, 2015.
  14. C. Zhang, Z. Ma*, J. Zhang, W. Xu, and J. Guo, “A Multi-level System for Sequential Update Summarization”, QSHINE, 19-20 August, Taipei, 2015.
  15. R. L. Kristensen*, Z.-H. Tan, Z. Ma, and J. Guo, “Binary Pattern Flavored Feature Extractors for Facial Expression Recognition: An Overview,” CIS-MIPRO 2015, 25-29 May 2015, Opatija, Croatia.
  16. H. Yu*, Z. Ma, M. Li, and J. Guo, “Histogram Transform Model Uding MFCC Features for Text-independent Speaker Identification”, in Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers (Asilomar SS&C), November 2014.
  17. F. Qi*, H. Zhang, Z. Ma, and N. Liu, “Supervoxel for Human and Clothes in RGB-D Image”, in Proceedings of Global Wireless Summit (GWS), 2014.
  18. Z. Ma*, R. Martin, J. Guo, and H. Zhang, “Nonlinear estimation of missing ΔLSF parameters by a mixture of Dirichlet distributions”, in Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
  19. S. Di*, Z. Yang, H. Zhang, Z. Ma, and J. Guo, “A Robust Lane Detection Algorithm for Lane Departure Warning Systems”, in Proceedings of International Conference on Communications, Connectivity, Convergence, Content and Co-operation, 2013.
  20. Z. Ma*, Q. Sun, H. Li, C. Wang, A. Yan, and F. Starfelt, “Dynamic Prediction of the Heat Demand for Buildings in District Heating Systems”, in Proceedings of International Conference on Applied Energy (ICAE), 2013.
  21. P. K. Rana, Z. Ma, J. Taghia, and M. Flierl, “Multiview Depth Map Enhancement by Variational Bayes Inference Estimation of Dirichlet Mixture Models”, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1528–1532, May 2013.
  22. J.Taghia, Z. Ma, and A. Leijon, “On Von-Mises Fisher Mixture Model in Text-Independent Speaker Identification”, in Proceedings of INTERSPEECH, 2013.
  23. Z. Ma* “Bayesian Estimation of the Dirichlet Distribution with Expectation Propagation”, in Proceedings of European Signal Processing Conference (EUSIPCO), pp. 689-693, 2012.
  24. Z. Ma* Z. –H. Tan, and S. Prasad, “EEG Signal Classification with Super-Dirichlet Mixture Model”, in Proceedings of IEEE Statistical Signal Processing Workshop (SSP), pp. 440-443, 2012.
  25. Z. Ma* and A. Leijon, “A Model-based Collaborative Filtering Method for Bounded Support Data”, in proceedings of IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), 2012.
  26. Z. Ma* and A. Leijon, “Approximating the Predictive Distribution of the Beta Distribution with the Local Variational Method”, in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1-6, 2011.
  27. Z. Ma* and A. Leijon, “Super-Dirichlet Mixture Models using Differential Line Spectral Frequencies for Text-Independent Speaker Identification”, in Proceedings of INTERSPEECH, pp. 2349-2352, 2011.
  28. Z. Ma* and A. Leijon, “Expectation Propagation for Estimating the Parameters of the Beta Distribution”, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2082-2085, 2010.
  29. Z. Ma* and A. Leijon, “Modeling Speech Line Spectral Frequencies with Dirichlet Mixture Models”, in Proceedings of INTERSPEECH, pp. 2370-2373, 2010.
  30. Z. Ma* and A. Leijon, “PDF-optimized LSF Vector Quantization Based on Beta Mixture Models”, in Proceedings of INTERSPEECH, pp. 2374-2377, 2010.
  31. Z. Ma* and A. Leijon, “Human Skin Color Detection in RGB Space with Bayesian Estimation of Beta Mixture Models”, in Proceedings of European Signal Processing Conference (EUSIPCO), pp. 1204-1208, 2010.
  32. Z. Ma* and A. Leijon, “Coding Bounded Support Data with Beta Distribution”, in Proceedings of IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), pp. 246-250, 2010.
  33. Z. Ma* and A. Leijon, “Beta Mixture Models and the Application to Image Classification”, in Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 2045-2048, 2009.
  34. Z. Ma* and A. Leijon, “Human Audio-Visual Consonant Recognition Analyzed with Three Bimodal Integration Models”, in Proceedings of INTERSPEECH, pp. 812-815, 2009.
  35. Z. Ma* and A. Leijon, “A Probabilistic Principal Component Analysis Based Hidden Markov Model for Audio-Visual Speech Recognition”, in Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers (Asilomar SS&C), pp. 2170-2173, 2008.

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