Shengrui Wang

Prix et distinctions

• Honorary mention for best paper award. 7th IEEE International Conference on BioInformatics and BioEngineering (BIBE). (Distinction).
• PAKDD 2019 Best Application Paper Award. PAKDD 2019 Program Committee. (Prize / Award).
• Service Award at The 27th IEEE International Conference on Advanced Information Networking and Applications (AINA-2013) Barcelona, Spain, March 25-28, 2013. IEEE AINA Steering Committee. (Honor).

Financement

• Grant. (Awarded). Co-applicant. PYRIDINE: PolYmeR bIg Data INtErface. Canadian Foundation for Innovation. 6 617 462 $. (2021-2026)
• Grant. (Awarded). Principal Investigator. Large-scale Co-evolving Data Mining for Survival Event Prediction. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Research Program. 288 000 $. (2020-2026)
• Grant. (Awarded). Co-applicant. Network Anomaly Detection with Quantum Machine Learning. Natural Sciences and Engineering Research Council of Canada (NSERC). Alliance. 1 304 370 $. (2022-2025)
• Grant. (Under Review). Co-applicant. Developing New System for Predicting Urinary Incontinence from Sensor Data. Natural Sciences and Engineering Research Council of Canada (NSERC). Alliance Grants. 275 500 $. (2021-2024)
• Grant. (Awarded). Principal Applicant. Institutional Research Support - Graduates. Faculty of Sciences, University of Sherbrooke. Support to recruit graduate students. 180 000 $. (2018-2024)
• Grant. (Completed). Principal Applicant. Regime Learning and Prediction on Time-series Data (with approved support from Dimensional Research Canada, Essex Asset Management, Quebec Prompt ). Natural Sciences and Engineering Research Council of Canada (NSERC). Collaborative Research and Development Grant. 412 500 $. (2019-2023)
• Grant. (Awarded). Principal Applicant. Large-scale Co-evolving Data Mining for Survival Event Prediction. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Accelerator Supplements (DAS). 120 000 $. (2020-2023)
• Grant. (Completed). Co-applicant. Le raccrochage scolaire à distance : un projet innovant pour un enjeu de société. Ministère de l'Économie, de l'Innovation et de l'Énergie. Innovation sociale. 220 000 $. (2021-2023)
• Grant. (Completed). Principal Applicant. Regime Switch Analysis on Time-series Data for Financial Prediction. MITACS / Dimensional Research Canada. Accelerate. 80 000 $. (2019-2022)
• Grant. (Completed). Co-applicant. Research on Kernel Learning Methods for High-dimensional Sequence Data with Applications. National Natural Science Foundation of China (NSFC). Basic Research. 118 000 $. (2017-2020)
• Grant. (Completed). Principal Applicant. Time-dependent Survival Neural Networks for Predicting Incoming Workload and Order Turn Around Time in a Radiology Service. Natural Sciences and Engineering Research Council of Canada (NSERC). Engage. 25 000 $. (2019-2020)
• Grant. (Completed). Principal Applicant. Mining High Dimensional Event Sequences for Predictive Modelling. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Grants (DG). 215 000 $. (2015-2020)
• Grant. (Completed). Co-applicant. Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data. Canadian Institutes of Health Research (CIHR). Project Grant (201603PJT). 305 801 $. (2016-2019)
• Grant. (Completed). Principal Applicant. Sequence Clustering for Regime Change Detection. Natural Sciences and Engineering Research Council of Canada (NSERC). Engage. 25 000 $. (2017-2018)
• Grant. (Completed). Principal Applicant. Sequence Clustering for Regime Change Detection. Natural Sciences and Engineering Research Council of Canada (NSERC). Engage. 25 000 $. (2017-2018)
• Grant. (Completed). Co-applicant. Étude des G-quadruplexes d'ARN comme motifs clés du transcriptome.. Fonds Québécois de la Recherche sur la Nature et les Technologies (FQRNT). Projet de recherche en équipe. 144 000 $. (2015-2018)
• Grant. (Completed). Co-applicant. Analyse des épisodes de soins hospitaliers des patients avec une MPOC au CHUS : stratégies visant à diminuer les réhospitalisations évitables. Fondation du CHUS (Sherbrooke, QC). Programme d’aide de financement interne. 25 000 $. (2015-2016)
• Grant. (Completed). Co-applicant. Innovative analysis of large data sets : bringing researchers and data together to understand complexity. Centre de recherche sur le vieillissement de l'IUGS. Projet Structurant. 70 000 $. (2014-2016)
• Grant. (Completed). Co-investigator. Research on Clustering of High-Dimensonal Mixed Type Data. National Natural Science Foundation of China (NSFC). Basic Research. 89 000 $. (2012-2015)
• Grant. (Completed). Co-investigator. Research on Methods for Mining Event Sequences towards Software Behaviour Identification. National Natural Science Foundation of China (NSFC). Basic Research. 93 600 $. (2012-2015)
• Grant. (Completed). Principal Applicant. Combining Transactional Data Mining and Sequence Mining Techniques for Efficient Real-Time Threat Assessment. Natural Sciences and Engineering Research Council of Canada (NSERC). Engage Grants (EG). 25 000 $. (2014-2015)
• Grant. (Completed). Co-investigator. Centre de recherche sur les environnements intelligents. Universite de Sherbrooke. Institutitonal Centers of Excellences. 108 000 $. (2012-2015)
• Grant. (Completed). Principal Applicant. Mining High-dimensional Data, Sequences and Data Streams. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Grant. 215 000 $. (2010-2015)
• Grant. (Completed). Co-applicant. Novel approaches to aging physiology: Using statistics and data mining to understand dysregulation of biomarker networks in human populations. Canadian Institutes of Health Research (CIHR). Operating Grant: Advancing Theoretical and Methodological Innovations. 193 482 $. (2012-2015)
• Grant. (Completed). Co-investigator. A web server integrated into a supercomputer infrastructure required by a new generation of bioinformatics tools and used to centralize the access to our applications and analyses. Natural Sciences and Engineering Research Council of Canada (NSERC). Research Tools and Instruments - Category 1. 29 965 $. (2013-2014)
• Grant. (Completed). Principal Applicant. Mining High-dimensional Data, Sequences and Data Streams. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Accelerator Supplements (DAS). 120 000 $. (2010-2013)
• Grant. (Completed). Co-investigator. MOIVRE : MOdélisation en Imagerie, Vision et REseaux de neurones. University of Sherbrooke. Centres d'Excellence. 106 800 $. (2009-2012)
• Contract. (Completed). Principal Investigator. A Software System for Helping the Search of Suicidal Persons. City of Sherbrooke. Programme de soutien à l'Innovation. 10 000 $. (2011-2011)
• Grant. (Completed). Principal Applicant. Clustering Algorithms for Mining High Dimensional Data and Structural Data. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Grant. 105 000 $. (2005-2010)
• Grant. (Completed). Co-applicant. Integrated Vehicle Navigation and Communication Systems Development. Networks of Centres of Excellence (NCE). AUTO 21. 280 000 $. (2007-2009)
• Grant. (Completed). Co-investigator. Système de détections des signes précurseurs de faillites chez les consommateurs. Natural Sciences and Engineering Research Council of Canada (NSERC). Collaborative Research and Development. 344 254 $. (2006-2009)

Publications

Articles de revue

• Meriem Zerkouk, Miloud Mihoubi, Belkacem Chikhaoui and Shengrui Wang. (2024). A machine learning based model for student’s dropout prediction in online training. Education and Information Technologies 1-20. DOI. (Published).
• Wei Zhang, Ping He, Shengrui Wang, Lizhi An, Fan Yang. (2023). A Dynamic Convolutional Generative Adversarial Network for Video Anomaly Detection. Arabian Journal for Science and Engineering 48 (2), 2075-2085. (Published).
• Jean Marie Tshimula, D'Jeff K Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, Belkacem Chikhaoui. (2023). Characterizing Financial Market Coverage using Artificial Intelligence. arXiv preprint arXiv:2302.03694 (Published).
• Y Yang, P He, S Wang, Y Tian, W Zhang. (2023). DB-TASNet for disease diagnosis and lesion segmentation in medical images. Journal of Visual Communication and Image Representation 95 103896. DOI. (Published).
• Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang. (2023). Modeling regime shifts in multiple time series. ACM Transactions on Knowledge Discovery from Data 17 (8), 1-31. DOI. (Published).
• Lifei Chen, Haiyan Wu, Wenxuan Kang, Shengrui Wang. (2023). Symbolic sequence representation with Markovian state optimization. Pattern Recognition 131 116637 (14 pages). (Published).
• Kunpeng Xu, Lifei Chen, Shengrui Wang. (2022). A Multi-view Kernel Clustering framework for Categorical sequences. Expert Systems with Applications 197 (Published).
• JM Tshimula, B Chikhaoui, S Wang. (2021). COVID-19: Detecting Depression Signals during Stay-At-Home Period. Health Informatics Journal 28 (2), (Published).
• Chen Rongbo, Haojun Sun, Chen Lifei, Jianfei Zhang, Shengrui Wang. (2021). Dynamic order Markov model for categorical sequence clustering. Journal of Big Data 8 (1), 1-25. (Published).
• JM Tshimula, B Chikhaoui, S Wang. (2021). Investigating Moral Foundations from Web Trending Topics. arXiv preprint arXiv:2102.11928 (Published).
• EG Tajeuna*, M Bouguessa, S Wang. (2021). Mining Customers' Changeable Electricity Consumption for Effective Load Forecasting. ACM Transactions on Intelligent Systems and Technology (TIST) 12 (4), 1-26. (Published).
• P. Chatigny, JM Patenaude, S Wang. (2021). Spatiotemporal adaptive neural network for long-term forecasting of financial time series. International Journal of Approximate Reasoning 132 70-85. (Published).
• Jean Marie Tshimula, Belkacem Chikhaoui and Shengrui Wang. (2020). A new approach for affinity relationship discovery in online forums. Social Network Analysis and Mining 10 1-15. DOI. (Published).
• Esaie Kuitche Kamela, Marie Degen, Shengrui Wang, AIda Ouangraoua. (2020). Choosing representative proteins based on splicing structure similarity improves the accuracy of gene tree reconstruction. bioRxiv (Published).
• Jianfei Zhang*, Lifei Chen, Yanfang Ye, Gongde Guo, Rongbo Chen, Alain Vanasse, Shengrui Wang. (2020). Survival neural networks for time-to-event prediction in longitudinal study. Knowledge and Information Systems (KAIS) 62 3727–3751. (Published).
• J. Zhang*, S, Wang, J. Courteau, A. Bach*, L. Chen, A. Vanasse. (2019). Feature-weighted Survival Learning Machine for COPD Failure Prediction. Artificial Intelligence in Medicine 96 68-79. (Published).
• A S M Touhidul Hasan*, Qingshan Jiang, Shengrui Wang. (2018). A new approach to privacy preserving multiple independent data publishing. Applied Sciences 8 783-805. (Published).
• L Yao, QZ Sheng, X Wang, S Wang, X Li, S Wang. (2018). Collaborative text categorization via exploiting sparse coefficients. World Wide Web 21 (2), 373-394. (Published).
• Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang. (2018). Modeling and Predicting Community Structures Changes in Time Evolving Social Networks. IEEE Transactions on Knowledge and Data Engineering (TKDE) 31 (6), 1166-1180. (Published).
• Christian M. Rochefort, David L. Buckeridge, Andréanne Tanguay, Alain Biron, Frédérick D’Aragon, Shengrui Wang, Benoit Gallix, Louis Valiquette, Li-Anne Audet, Todd C. Lee, Dev Jayaraman, Bruno Petrucci and Patricia Lefebvre. (2017). Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data: a validation study protocol. BMC Health Services Research 17:147 1-9. (Published).
• Joël Lafond-Lapalme*, Marc-Olivier Duceppe, Shengrui Wang, Peter Moffett, Benjamin Mimee. (2017). A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm. Bioinformatics (Oxford) 33 (9), 1293–1300. DOI. (Published).
• Belkacem Chikhaoui*, Mauricio Chiazzaro*, Shengrui Wang. (2017). Detecting Communities of Authority and Analyzing their Influence in Dynamic Social Networks. ACM Transactions on Intelligent Systems and Technology 8 (6), 82:1-82:28. (Published).
• Haojun, Sun; Rongbo, Chen*; Yong, Qin; Shengrui, Wang. (2017). Holo-Entropy Based Categorical Data Hierarchical Clustering. Informatica 28 (2), 303-328. (Published).
• Jianfei Zhang, Shengrui Wang, Aurélien Bach, Lifei Chen, Josiane Courteau, Alain Vanasse. (2017). Learning Relevance of COPD-specific Risk Factors in Failure Prediction. Artificial Intelligence in Medicine (AIM) (Submitted).
• Jianfei Zhang*, Shengrui Wang, Lifei Chen, Patrick Gallinari. (2017). Multiple Bayesian discriminant functions for high-dimensional massive data classification. Data Mining and Knowledge Discovery 31 465-501. DOI. (Published).
• Jean-Pierre Glouzon*, Jean-Pierre Perreault, Shengrui Wang. (2017). Structurexplor: A platform for the exploration of structural features of RNA secondary structures. Bioinformatics (Oxford) 33 (19), 3117-3120. (Published).
• Jean-Pierre Séhi Glouzon*, Jean-Pierre Perreault, Shengrui Wang. (2017). The super-n-motifs model: A novel alignment-freeapproach for representing and comparing RNA secondary structures. Bioinformatics (Oxford) 33 (8), 1169-1178. DOI. (Published).
• Lifei Chen*, Shengrui Wang, Kaijun Wang, Jianping Zhu. (2016). Soft subspace clustering of categorical data with probabilistic distance. Pattern Recognition 51 322-332. (Published).
• Qing Li*, Shengrui Wang, Emmanuel Milot, Patrick Bergeron, Luigi Ferrucci, Linda P. Fried and Alan A. Cohen. (2015). Homeostatic dysregulation proceeds in parallel in multiple physiological systems. Aging Cell 14 (6), 1103-1112. (Published).
• T. Xiong*, S. Wang, Q. Jiang, J. Z. Huang. (2014). A Novel Variable-order Markov Model for Clustering Categorical Sequences. IEEE Transactions on Knowledge and Data Engineering 26 (10), 2339-2353. (Published).
• J-P. Sehi Glouzon*, T. Xiong, F. Bolduc, J-P Perreault and S. Wang. (2014). DHCS: A Web Server for Clustering Biological Sequences. Bioinformatics (Submitted).
• J-P S. Glouzon*, F. Bolduc, S. Wang, R J Najmanovich, J-P Perreault. (2014). Deep-Sequencing of the Peach Latent Mosaic Viroid Reveals New Aspects of Population Heterogeneity. Plos One 9 (1), 1-16. (Published).
• B, Chikhaoui*, S, Wang, T, Xiong*, H, Pigot. (2014). Pattern-Based Causal Relationships Discovery from Event Sequences for Modeling Behavioral User Profile in Ubiquitous Environments. Information Sciences 285 204-222. (Published).
• V. D'Orangeville*, A. Mayers, E. Monga and S. Wang. (2013). Efficient Cluster Labeling for Support Vector Clustering. IEEE Transactions on Knowledge and Data Engineering 25 (11), 2494 – 2506. (Published).
• V. D'Orangeville, A. Mayers, E. Monga and S. Wang. (2013). Fast and approximate support vector clustering with active learning. Stat. Analysis and Data Mining (Submitted).
• A. N. Markovits*, C. J. Beauparlant, D. Toupin*, S. Wang, A. Droit, N. Gevry. (2013). NGS++: a library for rapid prototyping of epigenomics software tools. Bioinformatics (Oxford) 29 (15), 1893-1894. (Published).
• S. Wu* and S. Wang. (2013). Parameter-free Outlier Detection for Large-scale Categorical Data. IEEE Transactions on Knowledge and Data Engineering 25 (3), 589-602. (Published).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2013). Personal bankruptcy prediction by mining credit card data. Expert Systems with Applications 40 (2), 665-676. (Published).
• B. Chikhaoui*, S. Wang and H. Pigot. (2012). ADR-SPLDA: Activity Discovery and Recognition by Combining Sequential Patterns and Latent Dirichlet Allocation. Pervasive and Mobile Computing 8 (6), 845-862. (Published).
• D. Wei, Q. Jiang, Y. Wei and S. Wang. (2012). A Novel Hierarchical Clustering Algorithm for Gene Sequences. BMC Bioinformatics 13 (1), 174-189. (Published).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2012). DHCC: Divisive Hierarchical Clustering of Categorical Data. Data Mining and Knowledge Discovery 24 (1), 103-135. (Published).
• L. Chen*, Q. Jiang and S. Wang. (2012). Model-Based Method for Projective Clustering. IEEE Transactions on Knowledge and Data Engineering 24 (7), 1291-1305. (Published).
• A. Ntwari, A. Kelil*, R. Drouin, E. Monga, S. Wang, R. Brzezinski, M. Bronsard and J. Yan. (2011). DNAc: A Clustering Method for Identifying Kinship Relations Between DNA Profiles Using a Novel Similarity Measure. Journal of Forensic Sciences (JFS), (IF: 1.524), 2010 56 (s1), S17–S22. (Published).
• H. Sun* and S. Wang. (2011). Measuring the component overlapping in the Gaussian mixture model. Data Mining and Knowledge Discovery 23 (3), 479-502. (Published).
• Y. Lu*, S. Wang, S. Li and C. Zhou. (2011). Particle swarm optimizer for optimal variable weighting in clustering high-dimensional data. Machine Learning 82 (1), 43-70. (Published).
• H. Ni, B. Abdulrazak, D. Zhang, S. Wu*, Z. Yu, X. Zhou, S. Wang. (2011). Towards Non-Intrusive Sleep Pattern Recognition in Elder Assistive Env. J. of Ambient Intel. and Humanized Comp 3 (2), 167-175. (Published).
• M. Bouguessa*, S. Wang, and Benoit Dumoulin. (2010). Discovering knowledge-sharing communities in question-answering forums. ACM Transactions on Knowledge Discovery from Data, Special Issue on Knowledge Discovery for Web Intelligence 5 (1), 1-49. (Published).
• A. Kelil*, S. Wang, Q. Jiang, and R. Brzezinski. (2009). A general measure of similarity for categorical sequences. Knowledge and Information Systems 24 (2), 197-220. (Published).
• A. Kelil*, A. Nordell-Markovits*, Y. Parakh Ousman* and S. Wang. (2009). CLASS: A general approach for classifying categorical sequences. Can. J. Elec.&Comp. Eng 34 (4), 158 - 166. (Published).
• Mohamed Bouguessa* and Shengrui Wang. (2009). Mining Projected Clusters in High-Dimensional Spaces. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 21 (4), 507-522. (Published).
• L. Chen*, Q. Jiang and S. Wang. (2008). A hierarchical method for determining the number of clusters. Journal of Software 19 (1), 62-72. (Published).
• A. Kelil*, S. Wang, R. Brzezinski. (2008). CLUSS2: An alignment-independent algo. for clustering protein families with multiple biological functions. Int J. of Comp. Biology & Drug Design 1 (2), 122-140. (Published).

Articles de conférence

• K Xu, L Chen, S Wang. (2024). DRNet: A Decision-Making Method for Autonomous Lane ChangingwithDeep Reinforcement Learning. Proceedings of Canadian AI 2024, Canada, (Accepted).
• Abdallah Aaraba, Jean-Marc Patenaude, Shengrui Wang. (2024). FR3LS:a Forecasting model with Robust and Reduced Redundancy Latent Series. Proceedings of PAKDD, (Published).
• Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, Shengrui Wang. (2024). Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting. Proceedings of PAKDD, (Accepted).
• Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, Shengrui Wang. (2024). RHINE: A Regime-Switching Model with Nonlinear Representation for Discovering and Forecasting Regimes in Financial Markets. Proceedings of SDM, (Accepted).
• Imen Montassar, Belkacem Chikhaoui, Shengrui Wang. (2023). Agitated Behaviors Detection in Children with ASD Using Wearable Data. Proceedings of International Conference on Smart Homes and Health Telematics, 92-103. DOI. (Published).
• Théodore Simon, Jianfei Zhang, Shengrui Wang. (2023). Analysis and Comparison of Machine Learning Models for Glucose Forecasting. Proceedings of AINA, 113-123. (Published).
• Jean Marie Tshimula, D'Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang and Belkacem Chikhaoui. (2023). Characterizing Financial Market Coverage usingArtificial Intelligence. Proceedings of 18th International Conference on Machine Learning and Data Mining, (Accepted).
• Olfa Gassara, Belkacem Chikhaoui, Rostom Mabrouk, Shengrui Wang. (2023). Deriving Physiological Information from PET Images Using Machine Learning. Proceedings of International Conference on Smart Homes and Health Telematics, 26-37. DOI. (Published).
• P. A. Owusu, E. Tajeuna, J. -M. Patenaude, A. Brun and S. Wang. (2023). Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data. Proceedings of IEEE ICDM, 1247-1252. DOI. (Published).
• Mingxuan Sun, Rongbo Chen, Jianfei Zhang, Shengrui Wang. (2023). Unsupervised Learning via Graph Convolutional Network for Stock Trend Prediction. Proceedings of AINA, 358-369. (Published).
• Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang. (2022). A Longitudinal Study of Customer Electricity Load Profiles. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 289-294. (Published).
• Rongbo Chen, Mingxuan Sun, Kunpeng Xu, Jean-Marc Patenaude, Shengrui Wang. (2022). Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting. Proceedings of DEXA 2022, 3-16. (Published).
• Kunpeng Xu, Lifei Chen, Shengrui Wang. (2022). Data-driven Kernel Subspace Clustering with Local Manifold Preservation. 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 876-884. (Published).
• Jean Marie Tshimula, Sharmistha Gray, Belkacem Chikhaoui, Shengrui Wang. (2022). Emotion Detection in Law Enforcement Interviews. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 468-475. (Published).
• Jean Marie Tshimula, Belkacem Chikhaoui, Shengrui Wang. (2022). Investigating Moral Foundations from Web Trending Topics. Advances in Network-Based Information Systems: The 25th International Conference on Network-Based In, (Published).
• Heng Shi, Belkacem Chikhaoui, Shengrui Wang. (2022). Tree-Based Models for Pain Detection from Biomedical Signals. Proceedings of ICOST 2022, 183-195. (Published).
• Jean Marie Tshimula, Belkacem Chikhaoui and Shengrui Wang. (2020). A Pre-training Approach for Stance Classification in Online Forums. ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. (Published).
• B Chikhaoui, JM Tshimula, S Wang. (2020). Community Mining and Cross-Community Discovery in Online Social Networks. International Conference on Network-Based Information Systems. 176-187. (Published).
• R. Chen*, H. Sun, J. Zhang*, K. Xu* and S. Wang,. (2020). Exploring Sparse Patterns from Non-frequent Patterns for Categorical Sequence Clustering. The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). (Submitted).
• P. Chatigny*, J-M Patenaude, S. Wang. (2020). Financial ecosystem modeling with spatiotemporal neural network. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). (Submitted).
• J-M Tshimula*, B, Chikhaoui and S. Wang. (2020). On Predicting Behavior Deterioration in Online Discussion Communities. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). (Submitted).
• Jean Marie Tshimula, Belkacem Chikhaoui and Shengrui Wang. (2020). On Predicting Behavioral Deterioration in Online Discussion Forums. ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. (Published).
• J-M Tshimula*, B, Chikhaoui and S. Wang. (2019). HAR-search: A Method to Discover Hidden AffinityRelationships in Online Communities. The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019). (Published).
• E. G. Tajeuna*, M. Bouguessa, and S. Wang,. (2019). Survival Analysis for Time Series Forecasting. 36th IEEE International Conference on Data Engineering (2020). (Submitted).
• Jianfei Zhang* Shengrui Wang, Lifei Chen, Gongde Guo, Rongbo Chen*, Alain Vanasse. (2019). Time-Dependent Survival Neural Network for Remaining Useful Life Prediction. Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 441-452. (Published).
• E. G. Tajeuna*, M. Bouguessa, and S. Wang. (2018). A Network-Based Approach to Enhance Electricity Load Forecasting. Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 266-275. (Published).
• K. Xu*, L. Chen, S. Wang, B. Wang. (2018). A Self-representation Model for Robust Clustering of Categorical Sequences. Proceedings of APWeb-WAIM 2018, 13-23. (Published).
• Philippe Chatigny*, Rongbo Chen*, Jean-Marc Patenaude, Shengrui Wang. (2018). A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets. Proceedings of ICDM 2018, 887-892. (Published).
• Bach Aurélien, Zhang Jianfei, Wang Shengrui. (2018). Survival Classification with Two-Tied Labeling of Censored Data. Proc. of 2018 IEEE 32nd Inter. Conf. on Advanced Information Networking and Applications (AINA), 617-622. (Published).
• Z He*, EG Tajeuna*, S Wang, M Bouguessa. (2017). A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks. Proceeding of IEEE DSAA, 89-98. (Published).
• Jianfei Zhang*, Lifei Chen, Aurélien Bach*, Josiane Courteau, Alain Vanasse, Shengrui Wang. (2017). Sequential Representation of Clinical Data for Full-fitting Survival Prediction. 31st International Conference on Advanced Information Networking and Applications Workshops. (Published).
• Jianfei Zhang*, Shengrui Wang, Josiane Courteau, Lifei Chen, and Alain Vanasse. (2016). Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data. ICDM 2016: IEEE International Conference on Data Mining. (Published).
• Jianfei Zhang*, Lifei Chen, Alain Vanasse, Josiane Courteau, Shengrui Wang. (2016). Survival prediction by an integrated learning criterion on intermittently varying healthcare data. 30th AAAI Conference on Artificial Intelligence (AAAI-16). (Published).
• Etienne Gael Tajeuna*, Mohamed Bouguessa, Shengrui Wang. (2016). Tracking Communities over Time in Dynamic Social Network. 341-345, DOI. (Published).
• Mauricio Chiazzaro*, Belkacem Chikhaoui* and Shengrui Wang. (2015). Discovering and Tracking Influencer-Influencee Relationships Between Online Communities. The 2015 International Conference on Data Science and Advanced Analytics (DSAA2015). (Published).
• Etienne Gael Tajeuna*, Mohamed Bouguessa, Shengrui Wang. (2015). Tracking the Evolution of Communities Structures in Time-Evolving Social Networks. The 2015 International Conference on Data Science and Advanced Analytics (DSAA2015). (Submitted).
• M. Chiazzaro*, B. Chikhaoui* and S. Wang. (2014). A New Granger Causal Model for Influence Evolution in Dynamic Social Networks. National Conf. on Artificial Intelligence (AAAI-2015). (Published).
• L. Chen, G Guo, S. Wang, X. Kong. (2014). Kernel Learning Method for Distance-Based Classification of Categorical Data. 2014 IEEE UK Workshop on Computational Intelligence (UKCI), (Published).
• B Chikhaoui*, S Wang, H Pigot. (2013). Causality-Based Model for User Profile Construction from Behavior Sequences. Proceedings of WAINA, 461-468. (Published).
• L. Chen* and S. Wang. (2013). Central clustering of categorical data with automated feature weighting. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, 1260-1266. (Published).
• S. Renaud-Deputter*, T. Xiong*, S. Wang. (2013). Combining collaborative filtering and clustering for implicit recommender systems. Proceedings of IEEE AINA 2013, 748-755. (Published).
• Emilie Au*, M. Bouguessa, S. Wang. (2013). Document Modeling Using Syntactic and Semantic Information. Proceedings of WAINA, 203-206. (Published).
• B. Chikhaoui*, S. Wang and H. Pigot. (2012). A New Statistical Model For Activity Discovery and Recognition in Pervasive Environments. Proceedings of ICPR-2012, 3435-3438. (Published).
• L. Chen* and S Wang. (2012). Automated Feature Weighting in Naive Bayes for High-dimensional Data Classification. Proceedings of CIKM-2012, 1243-1252. (Published).
• L. Chen* and S. Wang. (2012). Centroid-based Clustering for Graph Datasets. Proceedings of ICPR-2012, 2144-2147. (Published).
• L. Chen*, G. Guo and S. Wang. (2012). Nearest Neighbor Classification by Partially Fuzzy Clustering. Proceedings of MAW, 789-794. (Published).
• B. Chikhaoui*, S. Wang and H. Pigot. (2012). Towards causal models for building behavioral user profile in ubiquitous computing applications. Proceedings of Ubicomp '12, 598-599. (Published).
• T. Xiong*, S. Wang, Q. Jiang, J. Z. Huang. (2011). A New Markov Model for Clustering Categorical Sequences. Proceedings of IEEE ICDM-2011, 854-863. (Published).
• B. Chikhaoui*, S. Wang, H. Pigot. (2011). A frequent pattern mining approach for ADLs recognition in smart environments. Proceedings of IEEE AINA 2011, 248-255. (Published).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2011). Semi-Supervised Parameter-free Divisive Hierarchical Clustering of Categorical Data. Proceedings of PAKDD2011, 265-276. (Published).
• H. Ni, B. Abdulrazak, D. Zhang, S. Wu*, Z. Yu, X. Zhou, S. Wang. (2010). Towards Non-intrusive Sleep Pattern Recognition in Elder Assistive Environment. LNCS, 96-109. (Published).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2009). A New MCA-Based Divisive Hierarchical Algo. for Clustering Categorical Data. Proceedings of IEEE ICDM-2009, 1058-1063. (Published).
• A. Kelil*, A. Nordell-Markovits*, S. Wang. (2009). Classification of categorical sequences. 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. (Published).
• M. Bouguessa*, Dumoulin, S. Wang. (2008). Identifying authoritative actors in question answering forums-The case of Yahoo! Answers. Proc. of KDD2008, 866-874. (Published).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2008). Personal bankruptcy prediction using sequence clustering. Proc. of KDD2008 Workshop, 32-38. (Published).
• L. Chen*, Q. Jiang, S. Wang. (2008). Probability model for projective clustering on high dimensional data. Proceedings of IEEE ICDM-2008, 755-760. (Published).
• A. Kelil*, S. Wang, R. Brzezinski. (2008). SAF: A substitution and alignment free similarity measure for protein seq…. Proc. of ICBB’08, 81-89. (Published).
• A. Kelil* and S. Wang. (2008). SCS: A new similarity measure for categorical sequences. Proceedings of IEEE ICDM-2008, Best Papers Awards,, 343-352. (Published).

Autres contributions

Cours enseignés

• Science des données. IFT599/799. (2021-08-21 à 2023-12-22).(3CR).
• Algorithmes et structures de données. IFT436. (2016-08-25 à 2017-12-10).(3CR).
• Sujets choisis en intelligence artificielle. IFT704. (2016-06-10 à 2018-08-25).(3CR).
• Recherche d'information et forage de données. IFT501. (2015-08-25 à 2020-12-22).(3CR).
• Forage de données. BIN 701. (2015-05-06 à 2017-08-20).(3CR).

Activités de collaboration internationale

• Co-Investigator. France. Collaboration with pre. Armelle Brun, Université Lorraine, co-supervision of a PhD student.
• Co-investigator. China. Collaboration with the team headed by Dr. Chen of the Fujian Normal University (FJNU). Principal membres are Gongde Guo; Qingshan Jiang; Shengrui Wang. A research grant ($118,000) was obtained from Research National Natural Science Foundation of China (NSFC). The project title is "Research on Kernel Learning Methods for High-dimensional Sequence Data with Applications". This grant pays my lodging and (china) domestic transport when I visit FJNU as well as some conference expenses. I co-supervise students within this collaboration. I was able to high an excellent PhD student for my lab at the Université de Sherbrooke too. The research of this collaboration is related to my Discovery research program.
• Co-investigator. China. Collaboration with the team headed by Dr. Chen of the Fujian Normal University (FJNU). Principal membres are Gongde Guo; Qingshan Jiang; Shengrui Wang. A research grant ($93600) was obtained from Research National Natural Science Foundation of China (NSFC). The project is on Methods for Mining Event Sequences towards for Behaviour Identification. This grant pays my lodging and (china) domestic transport when I visit FJNU as well as some conference expenses. I co-supervise students within this collaboration. I was able to high an excellent Ph.D. student for my lab at the Université de Sherbrooke too. The research of this collaboration is related to my Discovery research program.
• Co-investigator. China. Collaboration with the team headed by Dr. H. Sun of Shantou University. Principal membres are Mei Sun; Shengrui Wang; Wuqin Chen. A research grant ($89000) was obtained from Research National Natural Science Foundation of China (NSFC). The project is on Research on Clustering of High-Dimensonal Mixed Type Data. This grant pays my lodging and (china) domestic transport when I visit Shantou University as well as some conference expenses. I co-supervise students within this collaboration. The research of this collaboration is related to my Discovery research program.
• Invited professor. France. I collaborated with Pr. Patrick Gallinari, then director of Lip6 of Université Pierre et Marie Curie (Paris 6) on a research project on high-dimensional massive data classification. I was an invited professor at Paris 6 for one month. The project yielded a publication in a top data mining journal Data Mining and Knowledge Discovery. It also resulted in admission of one of my Master's graduate in the doctorate program at Paris 6 under the supervision of Pr. Gallinari.

Présentations

• (2021). Analyse de survie pour prédire des évènements associés à la MPOC. ACFAS Workshop : Le CIRIUS et les défis des systèmes de santé apprenants de demain : exemple d’application aux maladies rares à travers le réseau franco-québécois Ensemble. Sherbrooke, Canada
• Jean Marie Tshimula. (2020). A pre-training approach for stance classification in online forums. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Leiden, Netherlands
• Belkacem Chikhaoui. (2020). Community Mining and Cross-Community Discovery in Online Social Networks. International Conference on Network-Based Information Systems. Vancouver, Canada
• Jean Marie Tshimula. (2020). On predicting behavioral deterioration in online discussion forums. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Leiden, Netherlands
• (2019). Analyse de survie pour prédire des évènements associés à la MPOC. JourStat2019-Données massives et statistique en santé - organized by la Société Statistique de Montréal. Montreal, Canada
• (2019). Time-dependent Survival Neural Network for Remaining Useful Life Prediction. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019). Macao, Macao
• (2018). Regime switch analysis in time series. 2018 International Workshop on Big Data and IoT. Fuzhou, China
• (2018). Sparse Pattern Detection and Categorical Sequence Clustering. SIAT Workshop on Big Data Technology and Applications - Shenzhen Institutes of Advanced Technology, - Chinese Academy of Sciences. Shenzhen, China
• (2018). Survival Analysis for Predicting COPD Failures. Seminar in computer science - Université du Québec à Montréal. Montreal, Canada
• (2018). Survival Classification with Two-tied Labeling of Censored Data. 32nd IEEE International Conference on Advanced Information Networking and Applications. Cracow, Poland
• (2017). Sequential Representation of Clinical Data for Full-fitting Survival Prediction: Healthcare Study on COPD. IEEE AINA 2017. Taipei, Taiwan
• (2016). Discovering and Tracking Influencer-Influencee Relationships between Online Communities. College of Mathematics and Computer Science Fujian Normal University. Fuzhou, China
• (2016). Modeling Hazard in Longitudinal Clinical Data for Predicting COPD Failure. International Workshop on Collaborative Data Analytics. Fuzhou, China
• (2016). Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data. Shenzhen Key Lab for High Performance Data Mining Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. ShenZhen, China
• (2014). Efficient and Effective Methods for Mining Ultra-High Dimensional Data. Advanced Computing and Digital Engineering Lecture, by Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences. Shen Zhen, China
• (2013). Clustering Categorical Sequences: New Statistical Models and Algorithm. Seminar Lip6 - University Pierre et Marie Curie. Paris, France
• (2012). Data Mining and Applications. EB Games. Montreal, Canada
• (2011). Discovering Knowledge-Sharing Communities in Question-Answering Forums. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. ShenZhen, China
• (2010). An Objective Approach to Determining the Number of Clusters. The Fifth International Conference on Operations Research (CIRO'10). Marrakech, Morocco
• (2010). High-dimensional Categorical Data Mining and Applications. IEEE ICIS 2010. Xiamen, China