Kefei Liu, Ph.D.
School of Biomedical Engineering
Suzhou Institute for Advanced Research, University of Science and Technology of China A325 Mingde Building, 166 Ren’ai Road
SIP, Suzhou, Jiangsu, 215123, China Mobile: +86-18652428378
E-mail: kefeiliu@ustc.edu.cn
Google Scholar: https://scholar.google.com/citations?user=-Dh-Tp8AAAAJ
Research Interests and Expertise General Interests:
– Applied statistics, machine learning, data science, and computational health & medicine
Specific interests:
– Statistics and machine learning:
∗ Classical/frequentist statistics
∗ Bayesian statistics: Bayesian modeling, variational inference, Gibbs sampling, etc.
∗ Causal inference
∗ (Linear) regression and classification
∗ Dimension reduction (canonical correlation analysis, PCA, ICA, etc.)
∗ Regularization techniques, e.g., L1, L2, elastic net, graph Laplacian regularization
– Optimization algorithms:
∗ Majorize-minimization (MM) algorithm
∗ Accelerated proximal gradient descent
∗ Alternating direction method of multipliers (ADMM)
Education
Ph.D. in Electronic Engineering, City University of Hong Kong, Hong Kong, China, 09/2010 – 08/2013
Dissertation: “Source enumeration and prewhitening techniques for high-resolution and robust multidimensional array processing”
Advisors: Profs. Hing-Cheung So and Jo˜ao Paulo C. L. da Costa GPA: 4.3/4.3 (all A+)
M.Sc. in Mathematics, Beihang University, China, 09/2006 – 01/2009
Thesis: “Quadratic prime codes: Performance analysis and applications in frequency-hopping spread-spectrum communication systems,” awarded Excellent Thesis for Master’s Degree Advisor: Prof. Dongkai Yang
GPA: 3.50/4.0, Rank: 1st/40
B.Sc. in Applied Mathematics, Wuhan University, China, 09/2002 – 06/2006 GPA: 3.24/4.0, Rank: 5th/45
Postgraduate Training
Visiting Researcher, Harbin Institute of Technology Shenzhen Graduate School, China, 09/2013– 12/2013
Visiting PhD Student, Department of Electrical Engineering, University of Bras´ılia, Brazil, and Institute for Circuit Theory and Signal Processing, Technical University of Munich, Germany, 02/2012 – 08/2012
Mentor: Prof. Dr.-Ing. Jo˜ao Paulo C. L. da Costa
Visiting Graduate Student, School of Electronic and Information Engineering, BUAA, 10/2006– 8/2008 Activities: Participated in project “Development of a Novel Digital Nuclear Spectrum Analyzer,” and prepared my research proposal and master’s thesis under the guidance of Prof. Dongkai Yang.
Work Experience
Associate Professor, School of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China, 05/2022 – present
Research projects: multimodal fusion, knowledge graph embedding and reasoning
Advisor: S. Kevin Zhou
Research Associate, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 07/2016 – 12/2021
Research projects: Developed and applied new regularized canonical correlation analysis (CCA) models and algorithms: elastic net regularized CCA, Bayesian CCA with spike and slab priors, tensor CCA and deep generalized CCA, for multi-view analysis of imaging, omics, biomarker and clinical data in neuropsychiatric disorders (e.g., Alzheimer’s and Parkinson’s Diseases). These models have been designed to incorporate various biomedical domain knowledge to guide CCA to make its results more interpretable and accurate.
Advisors: Profs. Li Shen and Qi Long
Postdoctoral Fellow, Department of Computational Medicine and Bioinformatics, University of Michigan, 01/2014 – 06/2016
Research projects: Developed a unified robust statistical method (based on an extension of the sparsity (L0 or L1 norm) regularized linear regression model) for joint between-sample normalization and identification of differentially expressed genes in RNA-seq data.
Advisors: Profs. Jieping Ye and Hui Jiang
Software Engineer, Wuhan Binhu Electronics Co., Ltd., China, 02/2009 – 08/2010 Responsibil- ities: DSP+ARM software development (ADSP TS101 programming in C/Assembly).
Honors and Awards
Best Paper Award, BIBM 2018
Research Tuition Scholarship, City University of Hong Kong, 2013
Postgraduate Studentship, City University of Hong Kong, 2010-2013 (Annual Award) Best Paper Award, ICUMT 2012
Graduate National Defense Employment Scholarship, Beihang University, 01/2009 Excellent Thesis for Master’s Degree, School of Science, Beihang University, 12/2008
People's Scholarship & Excellent Undergraduate of Wuhan University, 2004-2006 (Annual Award) National Scholarship & Excellent Undergraduate of Wuhan University, 2004
Second, Second and First Prize, Wuhan University Mathematics Contest, 2004-2006 (Annual Award) Second Prize, Hubei Provincial English Translation Contest, 2005
First Prize, “Electrical Engineering Cup” National Undergraduate Mathematical Contest in Mod- eling, 2004
Computing Proficiency
MATLAB, Python, R, C/C++
Teaching Experience
(Teaching Assistant for the following courses) Signal Processing, Fall 2012
Security Technology, Fall 2011 Mathematical Analysis, Spring & Fall 2007
Professional Activities
Conference Program Committee Member or Reviewer:
– NeurIPS 2020–2021, ICML 2020–2021, ICLR 2021-2022, AAAI 2020–2022, SDM 2022, IJCAI
2017–2021, KDD 2020-2021, MICCAI 2018–2021, BIBM 2019–2021, ACM BCB 2020–2021 and IEEE ICASSPs
Journal reviewer:
– IEEE Transactions on Signal Processing
– Signal Processing
– Digital Signal Processing
– IEEE Signal Processing Letters
– IET Signal Processing
– IEEE Transactions on Pattern Analysis and Machine Intelligence
– IEEE Transactions on Medical Imaging
– IEEE Transactions on Biomedical Engineering
– IEEE Transactions on Vehicular Technology
– IEEE Journal of Biomedical and Health Informatics
– IEEE Access
– Pattern Recognition
– Scientific Reports
– Computer Vision and Image Understanding
– Knowledge and Information Systems
– Symmetry
– Mathematics
Session Chair: KDD 2021, BIBM 2021
Peer-Reviewed Publications Journal Papers:
1. Mansu Kim, Eun Jeong Min, Kefei Liu, Jingwen Yan, Andrew J. Saykin, Jason H. Moore, Qi Long, and Li Shen, “Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics,” Medical Image Analysis, vol. 76, Feb. 2022, article no. 102297.
2. Mansu Kim, Jingxuan Bao, Kefei Liu, Bo-yong Park, Hyunjin Park, Jae Young Baik, and Li Shen, “A structural enriched functional network: An application to predict brain cognitive performance,” Medical Image Analysis, vol. 71, Jul. 2021, article no. 102026.
3. Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin, Lei Guo, and Li Shen, “Multi-task sparse canonical correlation analysis with application to multi-modal brain imaging genetics,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 1, pp. 227–239, Jan.—Feb. 2021.
4. Lei Du, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin, and Li Shen, “Associating multi-modal brain imaging phenotypes and genetic risk factors via a dirty multi-task learning method,” IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3416–3428, Nov. 2020.
5. Lei Du, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew
J. Saykin, and Li Shen, “Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification,” Bioinformatics, vol. 36, no. Sup- plement 1, pp. i371–i379, Jul. 2020.
6. Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin, Lei Guo, and Li Shen, ”Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach,” Medical Image Analysis, vol. 61, Apr. 2020, article no. 101656.
7. Kefei Liu, Li Shen, and Hui Jiang, “Joint between-sample normalization and differential expression detection through ℓ0-regularized linear regression,” BMC Bioinformatics, vol. 20, suppl. 16, Dec. 2019, article no. 593.
8. Lei Du, Kefei Liu, Lei Zhu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, and Li Shen, ”Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: A longitudinal study of the ADNI cohort,” Bioinformatics, vol. 35, no. 14, pp. i474–i483, Jul. 2019.
9. Kefei Liu, Jieping Ye, Yang Yang, Li Shen, and Hui Jiang, “A unified model for joint normal- ization and differential gene expression detection in RNA-seq data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 2, pp. 442–454, Mar.–Apr. 2019.
10. Lei Du, Kefei Liu, Tuo Zhang, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, and Li Shen, “A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics,” Bioinformatics, vol. 34, no. 2, pp. 278–285, Jan. 2018.
11. Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew
J. Saykin, and Li Shen, “Pattern discovery in brain imaging genetics via SCCA modeling with a generic non-convex penalty,” Scientific Reports, vol. 7, Oct. 2017, article no. 14052.
12. Xiaohui Yao, Jingwen Yan, Kefei Liu, Sungeun Kim, Kwangsik Nho, Shannon L. Risacher, Casey
S. Greene, Jason H. Moore, Andrew J. Saykin, and Li Shen, “Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules,” Bioinfor- matics, vol. 33, no. 20, pp. 3250–3257, Oct. 2017.
13. Kefei Liu, Hui Cao, Hing Cheung So, and Andreas Jakobsson, “Multi-dimensional sinusoidal order estimation using angles between subspaces,” Digital Signal Processing, vol. 64, pp. 17–27, May 2017.
14. Lei Huang, Yuhang Xiao, Kefei Liu, Hing Cheung So, and Jian-Kang Zhang, “Bayesian informa- tion criterion for source enumeration in large-scale adaptive antenna array,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3018–3032, May 2016.
15. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, Lei Huang, and Jieping Ye, “Detection of number of components in CANDECOMP/PARAFAC models via minimum description length,” Digital Signal Processing, vol. 51, pp. 110–123, Apr. 2016.
16. Kefei Liu, Lei Huang, Hing Cheung So, and Jieping Ye, “Multidimensional folding for sinusoidal order selection,” Digital Signal Processing, vol. 48, pp. 349–360, Jan. 2016.
17. Lei Huang, Jun Fang, Kefei Liu, Hing Cheung So, and Hongbin Li, “An eigenvalue-moment-ratio approach to blind spectrum sensing for cognitive radio under sample-starving environment,” IEEE Transactions on Vehicular Technology, vol. 64, no. 8, pp. 3465–3480, Aug. 2015
18. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, and Andr´e L. F. de Almeida, “Semi-blind receivers for joint symbol and channel estimation in space-time-frequency MIMO-OFDM systems,” IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5444–5457, Nov. 2013.
19. Jo˜ao Paulo C. L. da Costa, Kefei Liu, Hing Cheung So, Stefanie Schwarz, Martin Haardt, and Florian Roemer, “Multidimensional prewhitening for enhanced signal reconstruction and parameter estimation in colored noise with Kronecker correlation structure,” Signal Processing, vol. 93, no. 11, pp. 3209–3226, Nov. 2013. [First two authors contributed equally.]
20. Kefei Liu, Hing Cheung So, Jo˜ao Paulo C. L. da Costa, Florian Roemer, and Lei Huang, “Efficient source enumeration for accurate direction-of-arrival estimation in threshold region,” Digital Signal Processing, vol. 23, no. 5, pp. 1668–1677, Sept. 2013.
21. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, and Lei Huang, “Subspace techniques for multi-dimensional model order selection in colored noise,” Signal Processing, vol. 93, no. 7, pp. 1976–1987, Jul. 2013.
22. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, Florian Roemer, Martin Haardt, and Luiz
F. de A. Gadˆelha, “3-D Unitary ESPRIT: Accurate attitude estimation for unmanned aerial vehicles with a hexagon-shaped ESPAR array,” Digital Signal Processing, vol. 23, no. 3, pp. 701–711, May 2013.
23. Kefei Liu, Dongkai Yang, and Jiang Wu, “Simulink implementation of frequency-hopping commu- nication system,” Journal of System Simulation, vol. 21, no. 24, pp. 7969–7973, Dec. 2009.
24. Sheng Hong, Dongkai Yang, Kefei Liu, Xiaoxiang He, and Manos M. Tentzeris, “Robust passive beamformer using bridge function sequences as weights,” IEICE Electronics Express, vol. 6, no. 16, pp. 1192–1198, Aug. 2009.
25. Dongkai Yang, Kefei Liu, and Qishan Zhang, “Linear multi-user detection based on the bridge function for MC-CDMA systems,” (in Chinese), Journal of Telemetry, Tracking, and Command, vol. 30, no. 2, pp. 53–59, Mar. 2009.
26. Kefei Liu, Shangwei Zhao, Meizhu Liu, “Solution of a class of minimal surface problem with obstacle,” Journal of Mathematics Research, vol. 1, no. 1, pp. 39–45, Mar. 2009.
Submitted or In-Revision Peer-reviewed Journal Papers:
1. Kefei Liu, Rui Feng, Long Qi, and Li Shen, “Enhanced canonical correlation analysis model via elastic net regularization,” IEEE Transactions on Pattern Analysis and Machine Intelligence. (Sub- mitted)
2. Kefei Liu, Jingwen Yan, Andrew J. Saykin, and Li Shen, “Cross-domain knowledge guided multi- modal imaging genetic analysis via a novel two-way regularized SCCA model,” Pattern Recognition. (Submitted)
Refereed Conference Papers:
1. Yixue Feng, Mansu Kim, Kefei Liu, Andrew J. Saykin, Jason H. Moore, Qi Long, and Li Shen, “Identifying multimodal imaging-driven subtypes in mild cognitive impairment using deep multiview learning,” Alzheimer’s Association International Conference (AAIC), Jul. 2021.
2. Yingxuan Eng, Xiaohui Yao, Kefei Liu, Shannon L. Risacher, Andrew J. Saykin, Qi Long, Yize Zhao, and Li Shen, “Polygenic mediation analysis of Alzheimer’s disease implicated intermediate amyloid imaging phenotypes,” in AMIA Annual Symposium Proceedings 2020, Nov. 2020, pp. 422—431.
3. Yixue Feng, Mansu Kim, Xiaohui Yao, Kefei Liu, Qi Long, and Li Shen, “Deep multiview learn- ing to identify population structure with multimodal imaging,” in Proc. IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA, Oct. 2020, pp. 308–314.
4. Mansu Kim, Jingxaun Bao, Kefei Liu, Bo-yong Park, Hyunjin Park, and Li Shen, “Structural connectivity enriched functional brain network using simplex regression with GraphNet,” in Proc. 11th International Workshop on Machine Learning in Medical Imaging (MLMI), Lecture Notes in Computer Science vol. 12436, Lima, Peru, Oct. 2020, pp. 292–302.
5. Bo Peng, Zhiyun Ren, Xiaohui Yao, Kefei Liu, Andrew J. Saykin, Li Shen, and Xia Ning, “Pri- oritizing amyloid imaging biomarkers in Alzheimer’s disease via learning to rank,” in Proc. 4th International Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science vol. 11846, Shenzhen, China, Oct. 2019, pp. 139–148.
6. Lei Du, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew
J. Saykin, and Li Shen, “A dirty multi-task learning method for multi-modal brain imaging genet- ics,” in Proc. 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Shenzhen, China, Oct. 2019, pp. 447–455.
7. Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, and Li Shen, “Diagnosis status guided brain imaging genetics via integrated regression and sparse canonical cor-
relation analysis,” in Proc. IEEE 16th International Symposium on Biomedical Imaging (ISBI), Venice, Italy, Apr. 2019, pp. 356–359.
8. Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, and Li Shen, “Fast multi-task SCCA learning with feature selection for multi-modal brain imaging genetics,” in Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, Dec. 2018, pp. 356–361. [acceptance rate: 19.6%]. BEST PAPER AWARD
9. Kefei Liu, Li Shen, and Hui Jiang, “A unified model for robust differential expression analysis of RNA-seq data,” in Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, Dec. 2018, pp. 437–442. [acceptance rate: 19.6%]
10. Jingwen Yan, Kefei Liu, Huang Li, Enrico Amico, Shannon L. Risacher, Yu-chien Wu, Shiaofen Fang, Olaf Sporns, Andrew J. Saykin, Joaqu´ın Gon˜i, and Li Shen, “Joint exploration and mining of memory-relevant brain anatomic and connectomic patterns via a three-way association model,” in Proc. IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, Apr. 2018, pp. 6–9.
11. Yuming Huang, Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, Li Shen, and Alzheimer’s Disease Neuroimaging Initiative, “A fast SCCA algorithm for big data analysis in brain imaging genetics,” in Proc. 3rd International Workshop on Imaging Genetics, MICGen 2017, Lecture Notes in Computer Science vol. 10551, Quebec City, Canada, Sept. 2017, pp. 210–219.
12. Kefei Liu, Xiaohui Yao, Jingwen Yan, Danai Chasioti, Shannon L. Risacher, Kwangsik Nho, An- drew J. Saykin, Li Shen, and Alzheimer’s Disease Neuroimaging Initiative, “Transcriptome-guided imaging genetic analysis via a novel sparse CCA algorithm,” in Proc. 3rd International Workshop on Imaging Genetics, MICGen 2017, Lecture Notes in Computer Science vol. 10551, Quebec City, Canada, Sept. 2017, pp. 220–229.
13. Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Li Shen, and Heng Huang, “Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model,” in Proc. 25th International Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science vol. 10265, Boone, NC, USA, Jun. 2017, pp. 198–209. [acceptance rate: 33%]
14. Lei Du, Tuo Zhang, Kefei Liu, Jingwen Yan, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin et al, “Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach,” in Proc. 25th International Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science vol. 10265, Boone, NC, USA, Jun. 2017, pp. 543–555. [acceptance rate: 33%]
15. Lei Du, Tuo Zhang, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, and Li Shen, “Sparse canonical correlation analysis via truncated ℓ1-norm with application to brain imaging genetics,” in Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, Dec. 2016, pp. 707–711.
16. Kefei Liu, Florian Roemer, Jo˜ao Paulo C. L. da Costa, Jie Xiong, Yi-Sheng Yan, Wen-Qin Wang, and Giovanni Del Galdo, “Tensor-based sparsity order estimation for big data applications,” in Proc. 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, Aug.–Sept. 2017, pp. 648–652. invited paper
17. Jie Xiong, Kefei Liu, Jo˜ao Paulo C. L. da Costa, and Wen-Qin Wang, “Bayesian information criterion for multidimensional sinusoidal order selection,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, USA, Mar. 2017, pp. 3106–3110. [First two authors contributed equally.]
18. Thiago Felippe K. Cordeiro, Jo˜ao Paulo C. L. da Costa, Kefei Liu, and Geovany A. Borges, “Kalman-based attitude estimation for an UAV via an antenna array,” 8th International Conference on Signal Processing and Communication Systems, Gold Coast, Australia, Dec. 2014.
19. Ricardo Kehrle Miranda, Jo˜ao Paulo C. L. da Costa, Marco A. M. Marinho, Edison Pignaton de Fre- itas, Rafael de Freitas Ramos, Kefei Liu, Hing Cheung, Leonardo Baltar, and Rafael T. de Sousa, “Evaluation of space-time-frequency (STF)-coded MIMO-OFDM systems in realistic channel mod- els,” in Proc. 28th International Conference on Advanced Information Networking and Applications Workshops, Victoria, Canada, May 2014, pp. 310–315.
20. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, and Rafael T. de Sousa, “Iterative prewhitening for multidimensional harmonic retrieval: New variants and comparative study,” in Proc. 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Saint Martin, France, Dec. 2013, pp. 216–219. invited paper
21. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, Florian Roemer, and Lei Huang, “On the use of order selection rules for accurate parameter estimation in threshold region,” 21st European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, Sept. 2013.
22. Kefei Liu, Hing Cheung So, Jo˜ao Paulo C. L. da Costa, and Lei Huang, “Core consistency diagnostic aided by reconstruction error for accurate enumeration of the number of components in PARAFAC models,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013, pp. 6635–6639.
23. Marco A. M. Marinho, Ronaldo S. Ferreira Ju´nior, Jo˜ao Paulo C. L. da Costa, Edison Pignaton de Freitas, Felix Antreich, Kefei Liu, Hing Cheung So, Rafael T. de Sousa, and Ricardo Zelenovsky, “Antenna array based positioning scheme for unmanned aerial vehicles,” 17th International ITG Workshop on Smart Antennas, Stuttgart, Germany, Mar. 2013.
24. Ronaldo S. Ferreira Ju´nior, Marco A. M. Marinho, Kefei Liu, Jo˜ao Paulo C. L. da Costa, Arthur V. Amaral, and Hing Cheung So, “Improved landing radio altimeter for unmanned aerial vehicles based on an antenna array,” in Proc. IV International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT), Saint Petersburg, Russia, Oct. 2012, pp. 105–113. BEST PAPER AWARD
25. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Andr´e L. F. de Almeida, and Hing Cheung So, “A closed form solution to semi-blind joint symbol and channel estimation in MIMO-OFDM systems,” in Proc. IEEE International Conference on Signal Processing, Communications and Computing, Hong Kong, China, Aug. 2012, pp. 191–196.
26. Kefei Liu, Jo˜ao Paulo C. L. da Costa, Hing Cheung So, Luiz F. de A. Gadˆelha, and Geovany A. Borges, “Improved attitude determination for unmanned aerial vehicles with a cross-shaped antenna array,” in Proc. 14th IASTED International Conference on Signal and Image Processing, Honolulu, Hawaii, USA, Aug. 2012, pp. 60–67
27. Kefei Liu, Hing Cheung So, and Lei Huang, “A multi-dimensional model order selection criterion with improved identifiability,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, Mar. 2012, pp. 2441–2444.
28. Xiaojing Ye, Kefei Liu, and Meizhu Liu, “Efficient minimization for dictionary based sparse rep- resentation and signal recovery,” in Proc. 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, Barcelona, Spain, Oct. 2011, article no. 105. invited paper
29. Meizhu Liu, Kefei Liu, and Xiaojing Ye, “Find the intrinsic space for multiclass classification,” in Proc. 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, Barcelona, Spain, Oct. 2011, article no. 106. invited paper
30. Sheng Hong, Kefei Liu, Ying Li, Hang Zou, Yunping Qi, and Kan Hong, “A new direct-sequence UWB transceiver based on bridge function sequence,” in Proc. 2nd International Conference on Computational Intelligence and Natural Computing, Wuhan, China, Sept. 2010, pp. 209–212.
31. Kefei Liu, Meizhu Liu, Zhihua Huang, Linbing Zhao, and Youyu Shen, “Error estimate of quadra- ture sampling system via discrete Hilbert transform,” in Proc. 2nd International Conference on Signal Processing Systems, Dalian, China, Jul. 2010, pp. V1-10–V1-13.
32. Dongkai Yang, Xin Yu, Kefei Liu, and Qishan Zhang, “Performance analysis of PAPR in MC- CDMA system,” in Proc. 1st International Conference on Information Science and Engineering, Nanjing, China, Dec. 2009, pp. 2676–2679.
33. Dongkai Yang, Kefei Liu, Qingge Liu, and Qishan Zhang, “Performance analysis of quadratic prime codes and construction of new frequency-hopping sequences,” 5th IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, China, Sept. 2009.
34. Sheng Hong, Dongkai Yang, Kefei Liu, Shuliang Gao, Huagang Xiong, and Qishan Zhang, “Noise analysis for a new digital beamformer on-receive-only,” 5th IEEE International Conference on Wire- less Communications, Networking and Mobile Computing, Beijing, China, Sept. 2009.
35. Sheng Hong, Huagang Xiong, Kefei Liu, Qing Chang, Qishan Zhang, Yongqiang Liu, Xiaoxiang He, and Manos M. Tentzeris, “Application of bridge function sequences in passive beamformer,” in Proc. International Conference on Networks Security, Wireless Communications and Trusted Computing, Wuhan, China, Apr. 2009, pp. 637–640.
36. Dongkai Yang, Kefei Liu, Qishan Zhang, and Yi Chen, “Performance analysis of frequency diversity for multi-carrier CDMA system based on bridge function,” 4th IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China, Oct. 2008.
37. Sheng Hong, Chunyu Hanzhong, Kefei Liu, and Manos M. Tentzeris, “Combination of adaptive modulation and power management for the performance enhancement of MIMO-OFDM systems,” IEEE Antennas and Propagation Society International Symposium, San Diego, CA, USA, Jul. 2008.
Book Chapters:
1. Walter C. Freitas Jr., Andr´e L. F. de Almeida, Jo˜ao Paulo C. L. da Costa, Kefei Liu, and Hing Che- ung So, “Multiantenna multicarrier transceiver architectures,” in Resource Allocation and MIMO for 4G and Beyond, Francisco Rodrigo Porto Cavalcanti, Ed., New York, NY, USA: Springer, 2014, pp. 359–395.
Invited Talks
“Core consistency diagnostic aided by reconstruction error for accurate enumeration of the number of components in PARAFAC models,” ICASSP 2013, Vancouver, Canada, 05/2013 “Multidimensional prewhitening for accurate signal reconstruction in Kronecker colored noise envi- ronments,” CityU, Hong Kong, China, 11/2012
“Application of parallel factor analysis in MIMO-OFDM systems,” University of Bras´ılia, Brazil, 06/2012
“Error estimate of quadrature sampling system via discrete Hilbert transform,” ICSPS 2010, Dalian, China, 07/2010.
Suzhou Institute for Advanced Research, University of Science and Technology of China,No.99 Ruo'shui Road( Ruo'shuiCampus), No.188 Ren'ai Road(West Campus), No.166 Ren'ai Road(East Campus), Suzhou Dushu Lake Science and Education Innovation District, Suzhou Industrial Park(SIP), Suzhou, Jiangsu, 215123, P.R.China
Email: suzhou@ustc.edu.cn
TEL：86-512-87161188
Fax：86-512-87161100
