Yifeng Li's Profile

Notes:

• Well funded PhD student positions are usually available. Highly qualified and motivated candidates from Computer Science, Machine Learning, Data Science, Bioinformatics, Biological Sciences, or Biotechnology may email me their transcripts and CV.  Please excuse me for the reality that, due to overwhelming schedule, I can only reply to a small number of inquiries among many excellent ones. Everyone is welcome to apply for my supervision through the official application system.

• Well funded MSc student positions are also usually available each year. Highly qualified and motivated candidates from Computer Science, Machine Learning, Data Science, Bioinformatics, Biological Sciences, or Biotechnology may email me their transcripts and CV.  Please excuse me for the reality that, due to overwhelming schedule, I can only reply to a small number of inquiries among many excellent ones. Everyone is welcome to apply for my supervision through the official application system.

• Valuable research opportunities for undergraduate students at Brock may be available through COSC 3P99 or COSC 4F90, (as well as BIOL/BMED 4F90 or BIOL/BMED 4F91) . If you are really interested in my research areas, you may contact me. 

Yifeng Li

Associate Professor

Canada Research Chair - Tier 2 Machine Learning for Biomedical Data Science

PI of the Brock Biomedical Data Science Lab

Department of Computer Science

Department of Biological Sciences

Centre for Biotechnology

Brock University

Office Phone: (905) 688-5550 ext. 3557

Official Home Page:  https://brocku.ca/mathematics-science/computer-science/faculty-staff/yifeng-li 

Brock Biomedical Data Science Lab: https://www.bmds-lab.com 

Email: yli2ATbrockuDOTca

Work Experiences:

• Canada Research Chair - Tier 2 Machine Learning for Biomedical Data Science, July 2022 - Now

• Associate Professor, Department of Computer Science, Brock University, July 2024 - Now

• Assistant Professor, Department of Computer Science, Brock University, January, 2020 - June 2024

• Research Officer, Digital Technologies Research Centre, National Research Council Canada, August, 2015 - December, 2019

Research Interests:

1. Artificial Intelligence: In a long term, I am interested in artificial intelligence algorithms that are inspired by neuroscience and cognitive science. Particularly, computational modelling and realization of the five principles of cognition (perception-action cycle, memory, attention, intelligence and language) are my priorities. 

2. Machine Learning: I am very interested in the theoretical studies of the unification of neural networks and probabilistic graphical models, deep generative models, deep network learning, large-scale sparse matrix factorizations, sparse representations, disentangled representation learning, generalized and regularized linear models, kernel/nonlinear approaches, deep feature selection, as well as Bayesian learning and inference. I prefer to formulate and explain various machine learning models from a statistical perspective.

3. Data Science: Theories and applications of big data analytics, structured & unstructured data modelling, and heterogeneous data integration for knowledge discovery and actionable decision making. Particularly interested in multi-view matrix factorizations, multi-modal deep learning models, data visualization, network science, knowledge representation, and knowledge learning from complex data.

4. Bioinformatics: Design and apply machine learning and data mining models to bioinformatics (such as annotation of non-coding regions of the human genome, biomarker identification using next-generation/deep sequence data, systems biology, and high-dimensional biological data analysis). 

5. Chemoinformatics: Molecular representation, drug design & repurposing, RNA & protein structure prediction, and biomolecular interaction prediction.

6. Health-informatics: Cancer research, personalized disease diagnosis & treatment, biomedical image processing, and intelligent & integrative health-informatics systems.

7. Optimization: Fast numerical optimization methods for large-scale machine learning and data mining models. Multi-objective optimization problems in machine learning and design tasks.

8. Computational Intelligence: Bio-inspired computation and quantum computing for machine learning.

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:

[33] Muhetaer Mukaidaisi, Madiha Ahmed, Karl Grantham, Aws Al-Jumaily, Shoukat Dedhar, Michael Organ, Alain Tchagang, Jinqiang Hou, Syed Ejaz Ahmed, Renata Dividino, and Yifeng Li, "'Several birds with one stone': Exploring the potential of AI methods for multi-target drug design, Molecular Diversity, https://doi.org/10.1007/s11030-024-11042-0, 2024. 

[32] Nicholas Aksamit, Alain Tchagang, Yifeng Li, and Beatrice Ombuki-Berman,  "Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery," BMC Bioinformatics, vol. 25, 255, 2024.

[31] Nicholas Aksamit, Jinqiang Hou, Yifeng Li, and Beatrice Ombuki-Berman, "Integrating Transformers and Many-objective optimization for drug design," BMC Bioinformatics, vol. 25, 208, 2024.

[30] Cameron Andress, Kalli Kappel, Marcus Elbert Villena, Miroslava Cuperlovic-Culf, Hongbin Yan, and Yifeng Li, “DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design,” PLoS Computational Biology, vol. 19, no. 7, e1010774, 2023.

[29] Aws Al-Jumaily, Muhetaer Mukaidaisi, Andrew Vu, Alain Tchagang, and Yifeng Li, "Examining multi-objective deep reinforcement learning frameworks for molecular design," Biosystems, vol. 232, 104989, 2023.

[28] Hayley-Ann Bennett, Yifeng Li, Hongbin Yan, “Conformational flexibility and structural dynamics of aptamer - A mini review,” Aptamers, vol. 7m 1-6, 2023.

[27] Hayley-Ann Bennett, Yifeng Li, and Hongbin Yan, "Thermal treatment affects aptamers' structural profiles," Bioorganic & Medicinal Chemistry Letters, vol. 82, no. 15, 129150, 2023.

[26] Wenjie Liu, Austin M. Hopkins, Peizhi Yan, Shan Du, Leonard G. Luyt, Yifeng Li, and Jinqiang Hou, "Can machine learning 'transform' peptides/peptidomimetics into small molecules? A case study with ghrelin receptorligands," Molecular Diversity, vol. 27, 2239-2255, 2023.

[25] Youlian Pan, Yifeng Li, Ziying Liu, Jitao Zou, and Qiang Li, "Computational genomics insights into cold acclimation in wheat," Frontiers in Genetics, vol 13, 1015673, 2022. 

[24] Muhetaer Mukaidaisi, Andrew Vu, Karl Grantham, Alain Tchagang, and Yifeng Li, "Multi-objective drug design based on graph-fragment molecular representation and deep evolutionary learning," Frontier in Pharmacology, vol 13, Article 920747, 2022.

[23] Sheriff Abouchekeir, Andrew Vu, Muhetaer Mukaidaisi, Karl Grantham, Alain Tchagang, and Yifeng Li, "Adversarial deep evolutionary learning for drug design," BioSystems, vol. 222, 104790, 2022.

[22] Karl Grantham, Muhetaer Mukaidaisi, Hsu Kiang Ooi, Mohammad Sajjad Ghaemi, Alain Tchagang, and Yifeng Li, "Deep evolutionary learning for molecular design,"  IEEE Computational Intelligence Magazine, vol. 17, no. 2, pp. 14-28, May 2022.

[21] Wenpeng Lu, Pengyu Zhao, Yifeng Li, Shoujin Wang, Heyan Huang, Shumin Shi, Hao Wu, “Chinese sentence semantic matching based on multi-level relevance extraction and aggregation for intelligent human-robot interaction,” Applied Soft Computing, vol. 131, 109795, 2022.

[20] Qiang Li, Wenyun Shen, Ioannis Mavraganis, Liping Wang, Peng Gao, Jie Gao, Dustin Cram, Yifeng Li, Ziying Liu, Brian Fowler, Youlian Pan, Jitao Zou. Elucidating the biochemical basis of trans-16:1 fatty acid change in leaves during cold acclimation in wheat. Plant-Environment Interactions, vol. 2, no. 3, 101-111, 2021. 

[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," Applied Sciences, vol. 11, no. 6, 2692, 2021.

[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:

[2] Ziying Liu, Youlian Pan, Yifeng Li, Therese Ouellet, Nora A. Foroud, “RNA-seq data processing in plant-pathogen interaction system: A case study,” In Foroud, N.A., Neilson, J.A.D. (eds), Plant Pathogen Interactions, Springer Nature book series: Methods in Molecular Biology, vol. 2659, pp. 119-135, 2023.

[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:

[42] Tszyi Kwok, Brendan Park, Katharine Yagi, Anne Yagi, Chunsheng Yang, Min Liao, Yifeng Li, "Keypoint-guided ophidian transformation for string-shaped data augmentation," International Conferences on Computational Biology and Bioinformatics (CIBCB), Aug. 2025, accepted.  

[41] Yage Zhang, Xiaodan Zhu, Yifeng Li, "Aligning language models using multi-objective deep reinforcement learning,"  Canadian AI Conference, May 2025. 

[40] Andrew Vu, Brendan Park, Yifeng Li, Ping Liang, "Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing,"  Great Lakes Bioinformatics Conference (GLBIO), May 2025. 

[39] Tyler McDonald, Anthony Colosimo, Yifeng Li, Ali Emami, "Can we afford the perfect prompt? Balancing cost and accuracy with the economical prompting index,"  International Conference on Computational Linguistics (COLING), Jan. 2025, pp. 7075-7086. 

[38] Brendan Park, Madeline Janecek, Naser Ezzati-Jivan, Yifeng Li, Ali Emami, “Picturing Ambiguity: A visual twist on the Winograd Schema Challenge,” Annual Meeting of the Association for Computational Linguistics (ACL), Aug. 2024. (oral presentation)

[37] Aws Al Jumaily, Muhetaer Mukaidaisi, Andrew Vu, Alain Tchagang, and Yifeng Li, "Exploring multi-objective deep reinforcement learning methods for drug design,"  International Conferences on Computational Biology and Bioinformatics (CIBCB), Aug. 2022.

[36] Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, and Hsu Kiang Ooi. "Generative enriched sequential learning (ESL) approach for molecular design via augmented domain knowledge," Canadian AI Conference, 2022.

[35] Chunsheng Yang, Yifeng Li, Yubin Yang, Zheng Liu, Min Liao, “Transfer learning-enabled modelling framework for digital twin,” IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD), May 2022.

[34] Sheriff Abouchekeir and Yifeng Li, "Adversarial deep evolutionary learning for drug design," International Conferences on Computational Biology and Bioinformatics (CIBCB), Oct. 2021, pp. 1-8.

[33] Rui Yu, Wenpeng Lu, Yifeng Li, Jiguo Yu, Guoqiang Zhang and Xu Zhang, "Sentence semantic matching with hierarchical CNN based on dimension-augmented representation," International Joint Conference on Neural Networks (IJCNN), July 2021.

[32] Pengyu Zhao, Wenpeng Lu, Yifeng Li, Jiguo Yu, Ping Jian and Xu Zhang , "Chinese semantic matching with multi-granularity alignment and feature fusion," International Joint Conference on Neural Networks (IJCNN), July 2021.

[31] Xu Zhang, Yifeng Li, Wenpeng Lu, Ping Jian, and Guoqiang Zhang, "Intra-Correlation Encoding for Chinese Sentence Intention Matching," International Conference on Computational Linguistics (COLING), Dec. 2020, pp. 5193-5204.

[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), July 2020, pp. 1-8.

[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 published in IJCNN 2020.)

[28] Yifeng Li and Xiaodan Zhu "Capsule generative models," International Conference on Artificial Neural Networks (ICANN), Munich, Germany, Sep. 2019, pp. 281-295.

[27] Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, and 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. (long version published in Canadian AI 2020.)

[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.

Education:

1. Post-Doctoral Research Fellow, Centre for Molecular Medicine and Therapeutics (CMMT), University of British Columbia (UBC), 2013.10-2015.07 (Main Research Topic: Deep Machine Learning Methods for the Identification of cis-Regulatory Elements in Human Genome, Advisor: Dr. Wyeth Wasserman)

2. Ph.D. in computer science, School of Computer Science, University of Windsor (UWindsor), ON, Canada, 2009.09-2013.10. (Dissertation Title: Sparse Machine Learning Models in Bioinformatics, available online: http://scholar.uwindsor.ca/etd/5023, Advisor: Dr. Alioune Ngom and Dr. Luis Rueda)

3. Master’s degree in computer science, School of Information, Shandong Institute of Light Industry (SDILI; now renamed as Qilu University of Technology), China, 2006-2009. (Thesis Title: Computational Intelligences for Mass Spectrometry Data Analysis, Advisor: Dr. Yihui Liu)

4. Bachelor’s degree in computer science, School of Information, SDILI, China, 2002-2006.

Tools and Software:

1. Multi-View Matrix Factorization Models for Integrative Data Analysis at https://github.com/yifeng-li/mvmf,

2. Deep Learning for Identifying cis-Regulatory Elements and Other Applications at https://github.com/yifeng-li/DECRES,

3. Non-Negative Matrix Factorization toolbox at https://sites.google.com/site/nmftool,

4. Sparse Representation toolbox at https://sites.google.com/site/sparsereptool,

5. Regularized Linear Models and Kernels toolbox: https://sites.google.com/site/rlmktool,

6. Probabilistic Graphical Models toolbox at https://sites.google.com/site/pgmtool,

7. Spectral Clustering toolbox at https://sites.google.com/site/speclust.

Awards, Grants, and Honours:

41. CFI - John R. Evans Leaders Fund (JELF), National, 2022-2027.

40. Canada Research Chair Award, National, 2022-2027.

39. NSERC Discovery Grant, National, 2021-2026.

38. AI for Drug Design, Artificial Intelligence for Design Challenge program, National, 2020-2023.

37. NRC Rising Star Award, Institutional/National, NRC, 2018. 

36. NRC New Beginning Ideation Grant, Institutional/National, 2019-2020.

35. NRC-DT Award for Innovative & Interdisciplinary Research, Institutional, 2018.

34. Certificate of Excellence, Institutional, NRC, 2016.

33. NRC Ideation Game Changing Funds, Institutional/National, 2016.

32. NSERC Postdoctoral Fellowship, National, 2015.

31. Canada Governor General’s Gold Medal, National, 2013-2014.

30. RECOMB 2015 Travel Fellowship, 2015.

29. Alberta Innovates Centre for Machine Learning and Canadian Artificial Intelligence Association Travel Award for Canadian AI, 2013.

28. IEEE BIBM Student Travel Award, 2012.

27. IEEE CIBCB Student Travel Grants, 2012.

26. Ontario Graduate Scholarship, UWindsor, 2012-2013.

25. Ontario Graduate Scholarship, UWindsor, 2011-2012.

24. Graduate Student Achievement Awards, UWindsor, April 2012.

23. Graduate Student Society Scholarship, UWindsor, 2010-2011.

22. International Student Society Bursary Awards, UWindsor, May 2011.

21. IEEE Walter Karplus Summer Research Grant, 2010.

20. Graduate Student Achievement Awards, UWindsor, April 2010.

19. Doctoral Tuition Scholarships, UWindsor, 2009-2013.

18. Graduate Assistantship, UWindsor, 2009-2013.

17. Research Assistantship, UWindsor, 2009-2013.

16. Outstanding Master Graduate Student Thesis Award of SDILI, Apr. 2010.

15. Outstanding Graduate Student Sci.&Tech. Innovation Achievement Award, SDILI, 2009.

14. Shandong Province Outstanding Graduate Award, China, 2009.

13. First Place Graduate Student Scholarship of SDILI, 2007-2008.

12. First Place Innovation Award, Forum on Graduate Students Innovation, SDILI, 2008.

11. IEEE CIBCB Student Travel Grants, 2008.

10. First Place Graduate Student Scholarship of SDILI, 2006-2007.

9. Outstanding Graduate Student Cadre Award of SDILI, 2006-2007.

8. Shandong Province Outstanding Graduate Award, 2006.

7. Second Place Scholarship of SDILI, First Term, 2005-2006.

6. Second Place Scholarship of SDILI, Second Term, 2004-2005.

5. First Place Scholarship of SDILI, First Term, 2004-2005.

4. Merit Student Award of SDILI, 2003-2004.

3. First Place Scholarship of SDILI, Second Term, 2003-2004.

2. First Place Scholarship of SDILI, First Term, 2003-2004.

1. Second Place Scholarship of SDILI, Second Term, 2002-2003.

Leaderships and Activities:

18. Organizer of the Brock FMS Data Science Seminar Series, since May 2021.

17. Co-founder of the Ottawa-AI Alliance and co-organizer of the Ottawa-AI Workshop, Oct. 19, 2018 (https://sites.google.com/view/ottawaaialliance).

16. Organizer of the NRC-DT Computing Science Colloquium Series, since May 2018.

15. Member of Canadian Artificial Intelligence Association (CAIAC) since 2013.

14. Member of Institute of Electrical and Electronics Engineers (IEEE) since 2008.

13. Co-founder of Ottawa-AI Alliance, and Co-organizer of Ottawa-AI Workshop, 2018  

12. Assessor of Canada Foundation for Innovation (CFI).

11. Assessor of Build in Canada Innovation Program (BCIP) and Western Innovation (WINN) Initiative.

10. Evaluator of the Creative Destruction Lab, Toronto.

9. Local Arrangements Chair of the 2016 IEEE World Congress on Computational Intelligence (WCCI), Vancouver.

8. Co-chair of the special session of Computational Intelligence in Genetic Regulatory Network under the umbrella of IEEE CIBCB 2013.

7. Co-chair of the special session of Computational Intelligence for Microarray Data Analysis under the umbrella of IEEE CIBCB 2012 and 2011.

6. Advertising committee chair of the BTC 2011 conference in University of Windsor.

5. Co-organizer of the 5th University of Windsor Computer Science Conference.

4. Judge of the 2014 Bioinformatics Retreat @ UBC Roboson Square.

3. Committee member of The International Association for Pattern Recognition Technical Committee 20: Pattern Recognition for Bioinformatics (IAPR TC-20: PRIB: http://iaprtc20.mosuma.org/?q=node/2).

2. Reviewer/PC member of NIPS 2016/17/18, ICML 2017/18/19, ICLR 2016/17/18/19, AISTATS 2016/17/18/19, WCCI 2016/18, BIBM 2015, ICCABS BMC Special Issues 2014, IEEE CEC 2009/10/11/18/19, SSCI 2014, PRIB 2014/13, IEEE CIBCB 2012/16/17, and Graduate Student Symposium under Canadian AI 2013, etc.

1. Reviewer of Briefings in Bioinformatics, Bioinformatics, BMC Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Bioinformatics and Computational Biology, IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, Neural Computing and Applications, ACM Transactions on Knowledge Discovery from Data, Expert Systems with Applications, IEEE Transactions on Multimedia,IEEE Transactions on Systems, Man, and Cybernetics: Systems, PLOS ONE, PLOS Computational Biology, Scientific Reports, Science China: Life Science Journal, etc.

Current MSc Students:

• Marcus Elbert Villena

• Daniil Pozdniakov

• Brendan Park

• Andrew Vu

• Karl Grantham

• Yage Zhang

• Nicholas Aksamit (co-supervised with Dr. Beatrice Ombuki-Berman)

• Madiha Ahmed (co-supervised with Dr. Ejaz Ahmed)

• Hayley Bennett (co-supervised with Dr. Tony Yan)

Past MSc Students:

• Muhetaer Mukaidaisi

• Peng Jin

• Cameron Andress

Current Undergraduate Research Students:

• Aws Al Jumaily

• Brendan Lu

• Aurora Zuo

• Liam Coorssen

Past Undergraduate Research Students:

• Anthony Colosimo

• Tyler McDonald

• Kylee Schram

• Hang Li

• Victoria Danh

• Karl Grantham

• Sheriff Abouchekeir

• Bradyn Willet-Smithers

• Joel Sauve

• Rajko Colakovic