- Ph.D., Department of Electrical and Computer Engineering,
University of Illinois at Urbana - Champaign, 2002
- B.S., M.S., Mathematics, Applied Mathematics and Computer Science, Peking University
Main Research Interest:
Pattern recognition,big data analytics, and machine learning
- Feature extraction and selection, classification, object recognition, and robust machine learning from high-dimensional and/or large scale dataset
- Machine learning and analytics for big data and resource management in cloud computing
- Clustering/correlation clustering for large scale data, data integration and management for complex and heterogeneous data
- Optimal configuration on graphs
- Pattern recognition and classification problems in biomedicine, engineering, and healthcare
Signal processing and image/video processing
- Efficient/optimal representation for high-dimensional data and huge amount of data for acquisition, transmission, visualization, and classification
- Image/video analysis for object recognition or tracking, including real-time tracking, and medical image analysis for diagnosis
- Multi-modal image or sensor information fusion (EM, infrared, optical, etc.).
- Information and image security and integrity
- Signal/image processing in biomedicine, engineering, healthcare
Algorithms and computing for biomedical research
- Modeling, analysis, and simulation of biomedical systems
- Causal inference using structural causal models; Pattern recognition and network models for biomedicine
- Modeling and simulation of biomedical and healthcare services
Selected Journal Papers:
- "Robust graph regularized nonnegative matrix factorization for clustering," C. Peng, Z. Kang, Y. Hu, J. Cheng, and Q. Cheng, ACM Transactions on Knowledge Discovery from Data, 11(3): 1-30, March 2017 (doi: 10.1145/3003730, article no: 33)).
- "Nonnegative matrix factorization with integrated graph and feature learning," C. Peng, Z. Kang, Y. Hu, J. Cheng, and Q. Cheng, ACM Transactions on Information Systems and Technology, 8(3): 1-29, Feb. 2017, (doi: 10.1145/2987378, article no. 42).
- "A supervised learning model for high-dimensional and large-scale data,"
C. Peng, J. Cheng, and Q. Cheng, ACM Transactions on Information Sysmtems and Technology, 8(2): 1-30. Nov. 2016, (doi: 10.1145/2972957, article no: 30).
- "Feature selection embedded subspace clustering," C. Peng, Z. Kang, M. Yang, and Q. Cheng, IEEE Signal Processing Letters, 23(7):1018-1022, 2016.(doi.10.1109/LSP.2016.2573159)
(Software package is on ResearchGate).
- "A kernel-based mixed effect regression model for earthquake ground motions," J. Tezcan, Y.D. Hazirbaba, and Q. Cheng, Advances in Engineering Software, forthcoming, 2016.(doi:10.1016/j.advengsoft.2016.06.002)
- "Robust subspace clustering via smoothed rank approximation," Z. Kang, C. Peng, and Q. Cheng, IEEE Signal Processing Letters, vol. 22, no. 11, pp. 2088-2092, 2015.
- "Modeling and prediction of ground motions as time-frequency images," J. Tezcan, J. Cheng, and Q. Cheng, IEEE Trans. Geophysics and Remote Sensing, forthcoming.
- "Assessing the carcinogenic potential of low dose exposures to chemical mixtures in the environment:
The challenge ahead," W. Goodson III, et al. (Q. Cheng with other authors worldwide), Carcinogenesis, 36 (Suppl 1): S254-S296, 2015. (doi:10.1093/carcin/bgv039).
- "Disruptive environmental chemicals and cellular mechanisms that confer resistance to cell death," Narayanan, K., Ali, M., Barclay, B., Cheng, Q., et al.
(with other authors worldwide), Carcinogenesis, 36 (Suppl 1): S89-S110, 2015. (doi:10.1093/carcin/bgv032).
- "A scalable projective scaling algorithm for l-p loss with convex penalizations," H. Zhou and Q. Cheng, IEEE Trans. Neural Networks and Learning Systems,
vol. 26, no.2, pp. 265-276, 2015. (doi: 10.1109/TNNLS.2014.2314129).
[Main paper PDF file]
- "A minimax framework for classification with applications to images and high dimensional data," Q. Cheng, H. Zhou, J. Cheng, and H. Li, IEEE Trans. Pattern Analysis Machine Intelligence,
vol. 36, no. 11, pp. 2117-2130, 2014 (doi: 10.1109/TPAMI.2014.2327978).
- "Highly conserved RNA pseudoknots at the Gag-pol junction of HIV-1 suggest a novel mechanism of -1 ribosomal frameshifting,"
X. Huang, G. Wang, Y. Yang, Q. Cheng, and Z. Du, RNA, vol.20, no. 5, pp. 1-7, 2014. (doi: 10.1261/rna.042457.113).
- "Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine," Q. Cheng, J. Tezcan, J. Cheng, Pattern Recognition Letters, vol. 40, pp. 88-95, 2014.
- "A genome-wide analysis of RNA pseudoknots that stimulate efficient -1 ribosomal frameshifting or readthrough in
animal viruses," X. Huang, Q. Cheng, and Z. Du, BioMed Research International, vol. 2013, Article ID 984028, pp. 1-15, 2013
- "Novel Regulatory small RNAs in Streptococcus pyogenes,"
R.A. Tesorero, N. Yu, J.O. Wright, J.P. Svencionis, Q. Cheng, J. Kim, and K. Cho, PLoS One, 8(6): e64021, 2013. (doi:10.1371/journal.pone.0064021).
- "The Fisher-Markov selector: Fast selecting maximally separable feature subset for multi-class classification with applications to high dimensional data," Q. Cheng, H. Zhou, and J. Cheng,
IEEE Trans. Pattern Analysis Machine Intelligence, vol. 33, no.6, pp. 1217-1233, 2011.
[PDF file] [Software package]
- "Nonparametric characterization of vertical ground motion effects,"
J. Tezcan and Q. Cheng, Earthquake Engineering and Structural Dynamics, vol. 41, pp. 515-530, 2012.
- "Nonparametric estimation of earthquake response spectra," J. Tezcan and Q. Cheng, Bulletin of Earthquake Engineering, vol. 10, no. 4, pp. 1205-1219, 2012.
- "Real-time vector quantization and clustering based on ordinary differential equations," J. Cheng, M. Sayeh, M. Zargham, and Q. Cheng, IEEE Trans. Neural Networks, vol. 22, no. 12, pp. 2143-2148, 2011.
- "Abnormal behaviors and micro-structural changes in white matter of
juvenile mice repeatedly exposed to amphetamine," H.-J. Yang, L. Wang, Q. Cheng, and H. Xu, Special Issue of Oligodendrocytes in Schizophrenia, Schizophrenia Research and Treatment, vol. 2011, pp. 1-11, 2011. (doi:10.1155/2011/542896).
- "Specialty care use in US patients with chronic disease," J. Bellinger,
R. Hassan, P. Rivers, Q. Cheng, E. Williams, and S. Glover, Int. J. Environmental Research Public Health, vol. 7, pp.975-990, 2010. (doi:10.3390/ijerph7030975).
- "A sparse learning machine for high-dimensional data with applications to microarray gene analysis," Qiang Cheng, IEEE/ACM Trans. on Computational Biology and Bioinformatics, vol. 7, no. 4, pp. 636-646, 2010.
- "Generalized embedding of multiplicative watermarks," Qiang Cheng, IEEE Trans. Circuit and Systems for Video Technology, vol. 19, no. 7, pp. 978-988, 2009.
- "Sparsity optimization method for multivariate feature screening for gene expression analysis," Q. Cheng and J. Cheng, Journal of Computational Biology, 16(9), pp. 1241-1252, 2009.
- "Toward actively defending from denial of service attacks in UMTS-WLAN," H. Qu, Q. Cheng, E. Yaprek, and L.-Y. Wang, Ubiquitous Computing and Communication Journal, vol.3, no.3, pp. 1-11, July 2008.
- "An efficient compression method for multiplanar reformulated biomedical images," Q. Cheng and M. Zargham, Int. J. of Functional Informatics and Personalized Medicine, Special Issue for IEEE 7th BIBE, vol.1, pp 68-79, Feb. 2008.
- "A novel distributed sensor positioning system using the dual of target tracking," L. Zhang, Q. Cheng, L. Wang, and S. Zeadali, IEEE Trans. Computers, vol. 57, no. 2, pp. 246-260, 2008.
- "Unconfined e-healthcare system using UMTS-WLAN," H. Qu, Q. Cheng, and E. Yaprek, Int. Journal of Modeling and Simulation, vol. 26, no. 3, pp. 261-270, 2006.
- "Performance analysis and error exponents of asymmetric watermarking systems," Q. Cheng, Y. Wang, and T.S. Huang, Signal Processing, vol. 84, no. 8, pp. 1429-1445, 2004.
- "Robust optimum detection of transform-domain multiplicative watermarks," Q. Cheng and T.S. Huang, IEEE Trans. on Signal Processing, Special Issue for Data Hiding in Digital Media and Secure Content Delivery, vol. 51, no. 4, pp. 906-924, 2003.
- "An additive approach to transform-domain information hiding and optimum detection structure," Q. Cheng and T.S. Huang, IEEE Trans. on Multimedia, vol. 3, pp. 273-284, 2001.
- "Spread Spectrum Signaling for Speech Watermarking," Qiang Cheng and Jeffrey S. Sorensen, US Patent 6892175, IBM T. J. Watson Research Center, Yorktown Heights, Issued on May 10, 2005.
- A number of patents filed (2 have been issued in Feb. 2012: US Patent 8121419, US Patent 8121420; one issued in May 2012: US Patent 8170354; one issued in Aug. 2012: US Patent 8238678) by Siemens Medical, and Siemens Aktiengesellschaft (DE), Siemens in U.S., China, and Germany, when I was a Senior Research Scientist and Senior Researcher at Siemens Medical and Siemens Corporate Research, Siemens, at Princeton, NJ.
- A provisional patent (H. Zhou and Q. Cheng) on large-scale fast computation of ridge regression has been granted in 2013, and a formal patent has been filed by Southern Illinois University in 2014.
- A provisional patent (Z. Kang and Q. Cheng) on top-N recommender system has been filed in 2015.
Selected Conference Papers or Book Chapters:
- "Subspace clustering via variance regularized ridge regression," C. Peng, Z. Kang, and Q. Cheng, IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2017), accepted. 2017.
- "Clustering with adaptive manifold structure learning," Z. Kang, C. Peng, and Q. Cheng, 2017 IEEE 33rd International Conference on Data Engineering (ICDE 2017), San Diego, CA, April 19-22, 2017.
- "Twin learning for similarity and clustering: A unified kernel approach," Z. Kang, C. Peng, and Q. Cheng, The 31st AAAI Conf. on Artificial Intelligence (AAAI-17), San Francisco, CA, Feb. 4-9, 2017.
- "A fast factorization-based approach to robust principal component analysis," C. Peng, Z. Kang, and Q. Cheng, IEEE Int. Conf. Data Mining (IEEE ICDM 2016), Barcelona, Spain, Dec. 13-15, 2016.
- "Rap: Scalable RPCA for low-rank matrix recovery," C. Peng, Z. Kang, M. Yang, and Q. Cheng, The 25th ACM Int. Conf. on Information and Knowledge Management (ACM CIKM 2016), Indianapolis, IN, Oct. 24-28, 2016.
- "Top-N recommendation on graphs," Z. Kang, C.Peng, M. Yang, and Q. Cheng, The 25th ACM Int. Conf. on Information and Knowledge Management (ACM CIKM 2016), Indianapolis, IN, Oct. 24-28, 2016.
- "Top-N recommendation with novel rank approximation," Z. Kang, C. Peng, and Q. Cheng, 2016 SIAM Int. Conf. on Data Mining (SDM 2016), Miami, FL, May 5-7, 2016.
- "Top-N recommender system via matrix completion," Z. Kang, C.Peng, and Q. Cheng, the 30th AAAI Conf. on Artificial Intelligence (AAAI-16), Phoenix, AZ, Feb. 12-17, 2016.
- "Robust PCA via nonconvex rank approximation," Z. Kang, C. Peng, and Q. Cheng, IEEE Int. Conf. Data Mining (IEEE ICDM 2015), Atlantic City, NJ, Nov. 14-17, 2015.
- "Robust subspace clustering via tighter rank approximation," Z. Kang, C. Peng, and Q. Cheng, The 24th ACM Int. Conf. on Information and Knowledge Management (ACM CIKM 2015), 2015. Melbourne, Australia, Oct. 2015.
- "Subspace clustering using log-determinant rank approximation," C. Peng, Z. Kang, H. Li, and Q. Cheng, Proc. The 21st ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining (ACM KDD 2015), pp. 925-934, 2015. (Sydney, Australia, Aug. 10-13).
- "Least-squares-kernel-machine regression for earthquake ground motion prediction," J. Tezcan, Y.D. Hazirbaba, and Q. Cheng, The 12th Int. Conf. Computational Structures Technology, Naples, Italy, Sept. 2014.
- "O(N) implicit subspace embedding for unsupervised multi-scale image segmentation," H. Zhou and Q. Cheng, Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, June 2011. [Demo/software available at Software Tools on this page]
- "Sufficient conditions for generating group level sparsity in a robust minimax framework," H. Zhou and Q. Cheng, the 24th Annual Conf. on Neural Information Processing Systems (NIPS 2010), Vancouver, Canada, Dec. 2010.
- "Maximum direction to geometric mean spectral response ratios using relevance vector machines," Y.D. Hazirbaba, J. Tezcan, and Q. Cheng, The 15th World Conference on Earthquake Engineering, Lisbon, Portugal, Sept. 2012.
- "Wavelet-based estimation of site response," J. Tezcan, V. Puri, and Q. Cheng, Proc. The 14th World Conference on Earthquake Engineering, Beijing, China, Oct. 2008.
- "Prediction of protein function using graph container and message passing," H. Zhou, Q. Cheng, and M. Zargham, Int. Conf. Bioinformatics and Computational Biology (BIOCOMP'08), Las Vegas, NV, July 2008.
- "Wireless-based medical information processing: Integrated system analysis and simulation," Q. Hu, L. Wang, Q. Cheng, et al., Int. Conf. Telehealth, Alberta, Canada, July 2006.
- "Landscape (3D): A robust sensor localization scheme for sensor networks over 3D terrains," L. Zhang, X. Zhou and Q. Cheng, IEEE Conf. Local Computer Networks (LCN), Tampa, FL, Nov. 2006.
- "Unconfined mobile Bluetooth telemedicine for empowered healthcare," Q. Cheng, H. Qu, Y. Wang, and J. Tan, in E-Health Paradigm Shift: Perspectives, Domains and Cases, Wiley: Jossey-Bass, 2005.
- "SNR analysis for phased-array MRI," Y. Wang, Q. Cheng, and J. Cheng, Proc. International Conference on Acoustic, Speech, and Signal Processing (ICASSP'05), Philadelphia, 2005.
- "Enhancing Bluetooth security with covert channel signaling," H. Qu and Q. Cheng, IEEE and IFIP Int. Conf. on Wireless Communications Networks, June 2004.
- "Combined audio and video watermarking using mel-frequency cepstra," Q. Cheng, T. S. Huang, and H.Pan, Proc. International Conference on Multimedia and Expo (ICME'01), Tokyo, Japan, Aug., 2001.
- "Spread spectrum signaling for speech watermarking," Qiang Cheng and Jeffrey Sorensen, Proc. International Conference on Acoustic, Speech, and Signal Processing (ICASSP'01), Salt Lake City, UT, May 2001.
- "An image watermarking technique using pyramid transform," Q. Cheng and T.S. Huang, Proc. ACM International Conference on Multimedia (ACM Multimedia'01), pp. 319-328, Ottawa, Canada, Sept., 2001.
- "Identify region of interest for video watermark embedment with principle component analysis on multiple cues," R. Wang, Q. Cheng, and T.S. Huang, Proc. ACM International Conference on Multimedia (ACM Multimedia'00), L.A., California, Oct. 2000.
- "Blind digital watermarking for images and videos and performance analysis," Qiang Cheng and Thomas S. Huang, Proc. International Conference on Multimedia and Expo (ICME'00), New York, Aug. 2000.
Recent/Current Research Projects Supported by NSF:
Software Tools, Demos and Results from Our Research for Downloading:
- A Computational Tool for Finding Small, NonCoding RNAs from Genomic Sequences:
We developed a computational tool for predicting small, non-coding RNAs (sRNA) from genomic sequences.
Applying our tool to Streptococcus pyogene, we have found 5 putative sRNAs sequences, among which 4 are new, and 1 was experimentally found in the literature
(now all sRNAs found by our program for Streptococcus have been experimentally verified).
These 5 putative sRNAs are the most likely ones. By lowering the likelihood, we can find more candidates - over 80 for Streps.
The tool is generic, applicable to other species. If interested, please contact me.
- A Real-Time Fusion Method and Tool for Fusing Medical Surgical Images (based on Bayesian risk minimization and pixon maps)
Fast in complexity for practical medical image fusion.
Comparable in quality to multiresolution methods based fusion.
Applicable to non-medical images.
- A Software Tool for Computing Pre-Pulse Inhibition (PPI) for Neuroscience
- Software tool and demo for Subspace Clustering using Log-determinant rank Approximation (SCLA)
Companion software tool and demo for our paper:
"Subspace clustering using log-determinant rank approximation," C. Peng, Z. Kang, H. Li, and Q. Cheng, Proceedings of the 21 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Ming (ACM KDD 2015), pp. 925-934, 2015. (Aug. 10-13, Sydney, Australia).
- Software tool and demo for the Fisher-Markov feature selector: Fast selecting maximally separable feature subset for multiclass classification
A companion software tool and demo for our paper:
"The Fisher-Markov selector: Fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data," Q. Cheng, H. Zhou, and J. Cheng, IEEE Trans. Pattern Analysis and Machine Intelligence, 33(6): 1217-1233, 2011.
More detailed user manual will be provided upon request.
- Demostration for linear time implicit subspace embedding for multiscale image segmentation
Demonstration and software for our paper:
"O(N) implicite embedding for unsupervised multi-scale image segmentation," CVPR, 2011, Colorado Springs.
- Companion software for message passing algorithm (MPA)
Software and demo for:
"A scalable projective scaling algorithm for l-p loss with convex penalizations," H. Zhou and Q. Cheng, IEEE Trans. Neural Networks and Learning Systems,
vol. 26, no.2, pp. 265-276, 2015. (doi: 10.1109/TNNLS.2014.2314129). The algorithm is fully scalable.
- Companion software for robust PCA via non-convex rank approximation
Companion software and demonstration for our paper:
"Robust PCA via non-convex rank approximation," Z. Kang, C. Peng, and Q. Cheng, Proc. ICDM 2015, Nov. 2015, Atlantic City, NY.
Hongbo Zhou (BS, MS: Beihang University; PhD Graduated.
Dissertation: A Unified Robust Minimax Framework For Regularized Learning Problems.)
Sharon Huang (BS, MS: TsingHua University; Dissertation topic: Bioinformatics and drug/protein design)
Chong Peng (BS: Qingdao University; Dissertation topic: Machine Learning)
Dengsong Zhu (BS, MS: Nankai University; Dissertation topic: Biomedical Imaging)
Zhao Kang (MS: Sichuan University; Dissertation topic: Pattern Recognition and Data Science)
Ming Yang (BS: Jilin University; Dissertation topic: Machine Learning)
Ning Yu (now system adminstrator, South Carolina State University; Thesis: Computational tool for finding small RNAs)
Xiongyu Peng (Thesis: An educational gaming approach for children's behavioral rehabilitation)
Pablo Robles Granda (now at Purdue University; Thesis: Image retrieval with decision trees)
John Beck (now software engineer at a music company; Thesis: Interactive visual representation of categorical datasets)
Zhichu Huang (now software engineer at US Army; Thesis: Human machine interface for biomedicine)
Jayanthan Raveendiran (now software engineer at State Farm; Thesis: 3D reconstruction for RAS)
Wei Rang (Thesis topic: Image processing)
Undergraduate research assistants:
Pat Keller (now developer at Patterson Dental),
Lijun Wang (entered Graduate Program at SIU Medical School),
Zhichu Huang (entered Graduate Program at CS SIUC).
Int. J. of Healthcare Information Systems and Informatics,
- GSTF Journal of Computing (Springer),
- Applied Scientific Reports,
- Int. J. of Statistics in Medical Research,
- J. of Medical Statistics and Informatics.
International Journal of Biomedical Imaging, Special Issue on Automatic Lesion Detection: Advances (manuscript due date - Nov.7, 2014. with other guest editors M.A. Balafar, A.B. Tosun, L. Wang, and J. Wang) Call for Papers.
Workshops/Conferences Organized or Helping Organize:
Co-Chair, Int. Workshop on Healthcare and Informatics Services (HIS 2010), Miami, Florida
Co-Chair, Int. Workshop on Web Services in Healthcare and Application 2011, Washington, D.C.
Publicity Co-Chair, The 2nd ACM Int. Health Informatics Symposium (IHI 2011), Miami, Florida
Technical Program Committee Co-Chair, The 5th Int. Congress on Image and Signal Processing (CISP 2012), joitly with the 5th Int. Conf. on
BioMedical Engineering and Informatics (BMEI 2012), Oct. 16-18 2012, Chongqing, China.
Computer Science Courses Taught/Teaching:
CS 455-Algorithm Design and Analysis; CS 586-Pattern Recognition and Image Analysis;
CS 220-Data Structures and Abstractions Using Java; CS 330-Introduction to Algorithm Deisgn and Analysis
Matlab Tutorial for Basic Probability, Statistics, and Reliability: Teaching Software