Yifeng Li

Research Interests

• Artificial Intelligence and Machine Learning
• Data Science and Data Analytics
• Optimization and Computational Intelligence
• Computational Biology and Bioinformatics

Prospective Students

• For undergraduates, if you are interested in doing course project or summer research under my supervision in the area of machine learning, data science or bioinformatics, you can contact me or meet me in my office.

• For graduate students, if you want to join my group, email me your CV and transcripts.  

Education

• BSc Computer Science, School of Information, Shandong Institute of Light Industry (SDILI), 2006
• MSc Computer Science, School of Information, Shandong Institute of Light Industry (SDILI), 2009
• PhD, Computer Science, University of Windsor, 2013
• Postdoc, Bioinformatics, University of British Columbia, 2015

Recent Publications

Dissertation:

[1] Yifeng Li, “Sparse machine learning models in bioinformatics,” PhD Dissertation, School of Computer Science, University of Windsor, 334 pages, Oct. 2013. (thesis available: http://scholar.uwindsor.ca/etd/5023/)

Published Refereed Journal Papers:

[19] Danny Salem, Yifeng Li, Pengcheng Xi, Miroslava Cuperlovic-Culf, Hilary Phenix, and Mads Kaern, "YeastNet: Deep learning enabled accurate segmentation of budding yeast cells in bright-field microscopy," Bioinformatics, under revision, 2019.

[18] Lipu Wang, Qiang Li, Ziying Liu, Anu Surendra, Youlian Pan, Yifeng Li, L. Irina Zaharia, Therese Ouellet, and Pierre R. Fobert, "Integrated transcriptome and hormone profiling highlight the role of multiple phytohormone pathways in wheat resistance against fusarium head blight," PLOS ONE, vol. 13, no. 11, e0207036, 2018.

[17] Genevieve L. Stein-O'Brien, Raman Arora, Aedin C. Culhane, Alexander V. Favorov, Lana X. Gamire, Casey S. Greene, Loyal A. Goff, Yifeng Li, Alioune Ngom, Michael F. Ochs, Yanxun Xu and Elana J. Fertig, "Enter the matrix: factorization uncovers knowledge from omics," Trends in Genetics, vol. 34, no. 10, 790-805, 2018.

[16] Yifeng Li, Youlian Pan, and Ziying Liu, "Multi-class non-negative matrix factorization for comprehensive feature pattern discovery," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 2, 615-629, 2019.

[15] Yifeng Li, Wenqiang Shi, and Wyeth W. Wasserman, "Genome-wide prediction of cis-regulatory regions using supervised deep learning methods," BMC Bioinformatics, no. 19, 202, 2018.

[14] Yifeng Li, François Fauteux, Jinfeng Zou, André Nantel and Youlian Pan, "Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine," Neurocomputing, vol. 324, 51-62, 2018.

[13] Yifeng Li, Fangxiang Wu, and Alioune Ngom, “A review on machine learning principles for multi-view biological data integration,” Briefings in Bioinformatics, vol. 19, no.2, 325-340, 2018.

[12] Chih-yu Chen, Wenqiang Shi, Bradley P. Balaton, Allison M. Matthews, Yifeng Li, David J. Arenillas, Anthony Mathelier, Masayoshi Itoh, Hideya Kawaji, Timo Lassmann, Yoshihide Hayashizaki, Piero Carninci, Alistair R.R. Forrest, Carolyn J. Brown and Wyeth W. Wasserman, "YY1 binding association with sex-biased transcription revealed through X-linked transcript levels and allelic binding analyses," Scientific Reports, vol. 6, Article ID: 37324, 2016.

[11] Yifeng Li, Chih-Yu Chen, and Wyeth W. Wasserman, “Deep feature selection: Theory and application to identify enhancers and promoters,” Journal of Computational Biology, vol. 23, no. 5, 322-336, 2016.

[10] Yifeng Li, Chih-Yu Chen, Alice M. Kaye, and Wyeth W. Wasserman, “The identification cis-regulatory elements: A review from a machine learning perspective,” BioSystems, vol. 138, 6-17, 2015.

[9] Yifeng Li, Haifen Chen, Jie Zheng, and Alioune Ngom, “The max-min high-order dynamic Bayesian network for learning gene regulatory networks with time-delayed regulations,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 4, 792-803, 2016.

[8] Yifeng Li, B. John Oommen, Alioune Ngom, and Luis Rueda, “Pattern classification using a new border identification paradigm: The nearest border technique,” Neurocomputing, vol. 157, 105-117, 2015.

[7] Yifeng Li and Alioune Ngom, “Versatile sparse matrix factorization: Theory and applications,” Neurocomputing, vol. 145, 23-29, 2014.

[6] Yifeng Li and Alioune Ngom, “Sparse representation approaches for the classification of high-dimensional biological data,” BMC Systems Biology, vol.7(S-4), pp. S6, 2013.

[5] Yifeng Li and Alioune Ngom, “The non-negative matrix factorization toolbox for biological data mining,” BMC Source Code for Biology and Medicine, vol. 8, pp. 10, 2013.

[4] Yifeng Li and Alioune Ngom, “Non-negative least squares methods for the classification of high dimensional biological data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no.2, pp. 447-456, 2013.

[3] Yifeng Li and Alioune Ngom, “Classification approach based on non-negative least squares,” Neurocomputing, vol. 118, pp. 41-57, 2013.

[2] Yifeng Li and Yihui Liu, “Feature selection based on simulated annealing algorithm for high-resolution protein mass spectrometry data,” China Journal of Bioinformatics, vol. 7, no.2, pp. 85-90, Jun. 2009. (Chinese)

[1] Yifeng Li and Yihui Liu, “Feature selection for protein mass spectrometry data based on genetic algorithm,” Computer Engineering, vol. 35, no.19, pp. 192-194, Oct. 2009. (Chinese)

Book Chapters:

[1] Yifeng Li and Alioune Ngom, “Mining gene-sample-time microarray data,” Chapter 13 in Luis Rueda ed. Microarray Image and Data Analysis: Theory and Practice, CRC Press/Taylor & Francis, pp. 339-368, 2014.

Published Refereed Conference Proceedings:

[30] Yifeng Li, Xiaodan Zhu, Richard Naud, and Pengcheng Xi, " Capsule deep generative model that forms parse trees ," International Joint Conference on Neural Networks (IJCNN), accepted, July, 2020.

[29] Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, and Yifeng Li (corresponding author), "Anomaly detection based on unsupervised disentangled representation learning in combination with manifold learning,” NeurIPS 2019 LIRE Workshop, Vancouver, December 2019. (long version accepted with IJCNN 2020.)

[28] Yifeng Li and Xiaodan Zhu "Capsule generative models," International Conference on Artificial Neural Networks (ICANN), Munich, Germany, Sep. 2019.

[27] Xiaoyan, LiIluju Kiringa, Tet Yeap, Xiaodan Zhu, Yifeng Li (corresponding author), "Exploring deep anomaly detection methods based on capsule net," ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning, Long Beach, June 2019.

[26] Yifeng Li and Xiaodan Zhu, "Capsule restricted Boltzmann machine," NIPS 2018 Workshop on Bayesian Deep Learning, Montreal, Canada, Dec. 2018.

[25] Yufei Feng, Xiaodan Zhu, Yifeng Li, Yuping Ruan, Michael Greenspan, "Learning capsule networks with images and text," NIPS 2018 Workshop on Visually Grounded Interaction and Language, Montreal, Canada, Dec. 2018.

[24] Yifeng Li and Xiaodan Zhu, "Exploring Helmholtz machine and deep belief net in the exponential family perspective," ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, July 2018.

[23] Yifeng Li and Xiaodan Zhu, "Exponential family restricted Boltzmann machines and annealed importance sampling ," 2018 International Joint Conference on Neural Networks (IJCNN/WCCI), Rio, Brazil, July 2018, pp. 39-48.

[22] Yifeng Li, “Advances in multi-view matrix factorizations,” 2016 International Joint Conference on Neural Networks (IJCNN/WCCI), Vancouver, Canada, July 2016, pp. 3793-3800.

[21] Yifeng Li and Alioune Ngom, “Data integration in machine learning,” 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington DC, Nov., 2015, pp. 1665-1671.

[20] Yifeng Li, Chih-Yu Chen, and Wyeth W. Wasserman, “Deep feature selection: Theory and application to identify enhancers and promoters,” 2015 Annual International Conference on Research in Computational Molecular Biology (RECOMB), Warsaw, Poland, April, 2015, vol. LNCS 9029, pp. 205-217. (acceptance rate: 21.2%)

[19] Yifeng Li, Richard Caron, and Alioune Ngom, “A decomposition method for large-scale sparse coding in representation learning,” IEEE World Congress on Computational Intelligence (IJCNN/WCCI), Beijing, China, 2014, pp. 3732-2738.

[18] Yifeng Li, B. John Oommen, Alioune Ngom, and Luis Rueda, “A new paradigm for pattern classification: Nearest border techniques,” 26th Australasian Joint Conference on Artificial Intelligence, New Zealand, Dec. 2013, vol. LNCS 8272, pp. 441-446.

[17] Yifeng Li and Alioune Ngom, “Versatile sparse matrix factorization and its applications in high-dimensional biological data analysis,” IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), Nice, June, 2013, LNBI 7986, pp. 91-101.

[16] Iman Rezaeian, Yifeng Li, Martin Crozier, Eran Andrechek, Alioune Ngom, Luis Rueda, and Lisa Porter, “Identifying informative genes for prediction of breast cancer subtypes,” IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), Nice, June, 2013, LNBI 7986, pp. 138-148.

[15] Yifeng Li, “Sparse representation for machine learning,” 26th Canadian Conference on Artificial Intelligence (AI 2013), Regina, May, 2013, LNAI 7884, pp. 352-357.

[14] Yifeng Li and Alioune Ngom, “The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB/SSCI), Singapore, Apr. 2013, pp. 83-90.

[13] Yifeng Li and Alioune Ngom, “Fast kernel sparse representation approaches for classification,” IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, Dec. 2012, pp. 966-971. (acceptance rate 19.97%)

[12] Yifeng Li and Alioune Ngom, “Fast sparse representation approaches for the classification of high-dimensional biological data,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Philadelphia, PA, Oct. 2012, pp. 306-311. (acceptance rate 19.93%)

[11] Yifeng Li and Alioune Ngom, “Supervised dictionary learning via non-negative matrix factorization for classi fication,” International Conference on Machine Learning and Applications (ICMLA), Boca Raton, Florida, Dec. 2012, pp. 439-443.

[10] Yifeng Li and Alioune Ngom, “Diagnose the premalignant pancreatic cancer using high dimensional linear machine,” LNBI/LNCS: 2012 IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), LNBI 7632, pp. 198-209, 2012.

[9] Yifeng Li, Alioune Ngom, and Luis Rueda, “A framework of gene subset selection using multiobjective evolutionary algorithm,” LNBI/LNCS: 2012 IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), LNBI 7632, pp. 38-48, 2012.

[8] Yifeng Li and Alioune Ngom, “A new kernel non-negative matrix factorization and its application in microarray data analysis,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, CA, May 2012, pp. 371-378.

[7] Yifeng Li and Alioune Ngom, “Classification of clinical gene-sample-time microarray expression data via tensor decomposition methods,” LNBI/LNCS: Selected Papers of 2010 International Meeting on Computational Intelligence Methods for Bioinfomatics and Biostatistics (CIBB), vol. 6685, pp. 275-286, 2011.

[6] Yifeng Li and Alioune Ngom, “Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, Dec. 2010, pp.438-443.

[5] Yifeng Li, Numanul Subhani, Alioune Ngom, and Luis Rueda, “Alignment-based versus variation-based transformation methods for clustering microarray time-series data,” ACM International Conference On Bioinformatics and Computational Biology (BCB), Niagara Falls, NY, Aug. 2010, pp.53-61.

[4] Numanul Subhani, Yifeng Li, Alioune Ngom, and Luis Rueda, “Alignment versus variation vector methods for clustering microarray time-series data,” IEEE Congress on Evolutionary Computation (CEC/WCCI), Barcelona, Spain, Jul. 2010, pp. 818-825.

[3] Yifeng Li, Alioune Ngom, and Luis Rueda, “Missing value imputation methods for gene-sample-time microarray data analysis,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Montreal, Canada, May 2010, pp.183-189.

[2] Yifeng Li, Yihui Liu, and Li Bai, “Genetic algorithm based feature selection for mass spectrometry data,” IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, Oct. 2008, pp.85-90.

[1] Yifeng Li and Yihui Liu, “A Wrapper feature selection method based on simulated annealing algorithm for prostate protein mass spectrometry data,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Sun Valley, Idaho, Sep. 2008, pp.195-200.

Recent M.Sc. Graduate Student Thesis

• TBA

Current M.Sc. Graduate Student Thesis

• I am looking for excellent potential graduate students.