Présentation

Sujets de recherche

Exploration de données (Data mining), Bases de données informatiques, Algorithmes, Analyse des réseaux (information), Bioinformatique, Systèmes experts, Qualité de vie et vieillissement, Systèmes d'informations sur la santé.

Disciplines de recherche

Informatique.

Mots-clés

Intelligence artificielle, Transport intelligent, Reconnaissance de formes, Intelligence d'affaires, Bio-informatique, Classification et clustering, Apprentissage artificiel et prédiction en santé, Traitement d'images et Télédétection, Systèmes Intelligents, Forage de données.

Intérêts de recherche

Forage de données, reconnaissance de formes, intelligence artificielle, bioinformatique, transport intelligent, informatique de santé, intelligence d'affaires, prédiction des évenements, réseaux sociaux, détection de changement de régimes dans les données boursières, détection des signes de faillites chez les détenteurs des cartes de crédit, systèmes de recommendation, recherche d'informations sur Internet, habitat intelligent, identification des experts dans les forums de discussions (Internet), recherche des personnes en fuite, etc.

Centre de recherche

Centre de recherche sur le vieillissement du CIUSSS de l’Estrie – CHUS

Langues parlées et écrites

Anglais, Français, Mandarin

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. (Prix / Récompense).
• 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. (Honneur).

Financement

Subvention. (En cours d’évaluation). Chercheur principal. Large-scale Co-evolving Data Mining for Survival Event Prediction. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Discovery Research Program. 365000 $ (2020-2025).

Subvention. (Obtenu). Demandeur principal. Regime Learning and Prediction on Time-series Data (with approved support from Dimensional Research Canada, Essex Asset Management, Quebec Prompt ). Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Collaborative Research and Development Grant. 412500 $ (2020-2022).

Subvention. (Obtenu). Demandeur principal. Institutional Research Support - Graduates. Faculty of Sciences, University of Sherbrooke. Support to recruit graduate students. 150000 $ (2017-2022).

Subvention. (Obtenu). Demandeur principal. Regime Switch Analysis on Time-series Data for Financial Prediction. MITACS / Dimensional Research Canada. Accelerate. 80000 $ (2019-2021).

Subvention. (Obtenu). Co-demandeur. Research on Kernel Learning Methods for High-dimensional Sequence Data with Applications. National Natural Science Foundation of China (NSFC). Basic Research. 118000 $ (2017-2020).

Subvention. (Obtenu). Demandeur principal. Time-dependent Survival Neural Networks for Predicting Incoming Workload and Order Turn Around Time in a Radiology Service. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Engage. 25000 $ (2019-2020).

Subvention. (Obtenu). Demandeur principal. Mining High Dimensional Event Sequences for Predictive Modelling. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Discovery Grants (DG). 215000 $ (2015-2020).

Subvention. (Terminé). Co-demandeur. Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data. Instituts de Recherche en Santé du Canada (IRSC). Project Grant (201603PJT). 305801 $ (2016-2019).

Subvention. (Terminé). Demandeur principal. Sequence Clustering for Regime Change Detection. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Engage. 25000 $ (2017-2018).

Subvention. (Terminé). Demandeur principal. Sequence Clustering for Regime Change Detection. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Engage. 25000 $ (2017-2018).

Subvention. (Terminé). Co-demandeur. 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. 144000 $ (2015-2018).

Subvention. (Terminé). Co-demandeur. 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. 25000 $ (2015-2016).

Subvention. (Terminé). Co-demandeur. 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. 70000 $ (2014-2016).

Subvention. (Terminé). Co-chercheur. Research on Clustering of High-Dimensonal Mixed Type Data. National Natural Science Foundation of China (NSFC). Basic Research. 89000 $ (2012-2015).

Subvention. (Terminé). Co-chercheur. Research on Methods for Mining Event Sequences towards Software Behaviour Identification. National Natural Science Foundation of China (NSFC). Basic Research. 93600 $ (2012-2015).

Subvention. (Terminé). Demandeur principal. Combining Transactional Data Mining and Sequence Mining Techniques for Efficient Real-Time Threat Assessment. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Engage Grants (EG). 25000 $ (2014-2015).

Subvention. (Terminé). Co-chercheur. Centre de recherche sur les environnements intelligents. Universite de Sherbrooke. Institutitonal Centers of Excellences. 108000 $ (2012-2015).

Subvention. (Terminé). Demandeur principal. Mining High-dimensional Data, Sequences and Data Streams. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Discovery Grant. 215000 $ (2010-2015).

Subvention. (Terminé). Co-demandeur. 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. 193482 $ (2012-2015).

Subvention. (Terminé). Co-chercheur. 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. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Research Tools and Instruments - Category 1. 29965 $ (2013-2014).

Subvention. (Terminé). Demandeur principal. Mining High-dimensional Data, Sequences and Data Streams. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Discovery Accelerator Supplements (DAS). 120000 $ (2010-2013).

Subvention. (Terminé). Co-chercheur. MOIVRE : MOdélisation en Imagerie, Vision et REseaux de neurones. Université de Sherbrooke. Centres d'Excellence. 106800 $ (2009-2012).

Contrat. (Terminé). Chercheur principal. A Software System for Helping the Search of Suicidal Persons. Ville de Sherbrooke. Programme de soutien à l'Innovation. 10000 $ (2011).

Subvention. (Terminé). Demandeur principal. Clustering Algorithms for Mining High Dimensional Data and Structural Data. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Discovery Grant. 105000 $ (2005-2010).

Subvention. (Terminé). Co-demandeur. Integrated Vehicle Navigation and Communication Systems Development. Réseaux de Centres d'Excellence (RCE). AUTO 21. 280000 $ (2007-2009).

Subvention. (Terminé). Co-chercheur. Système de détections des signes précurseurs de faillites chez les consommateurs. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG). Collaborative Research and Development. 344254 $ (2006-2009).

Publications

Articles de revue

• 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. (Article publié).
• EG Tajeuna*, M Bouguessa, S Wang. (2019). Modeling and predicting community structures changes intime evolving social networks. IEEE Transactions on Knowledge and Data Engineering, 31(6), 1166-1180. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• Etienne Gael Tajeuna*, Mohamed Bouguessa, Shengrui Wang. (2018). Mining Users Changeable Electricity Consumption Behaviors. ACM Transactions on Intelligent Systems and Technology, (Révisions requises).
• Jianfei Zhang*, Shengrui Wang and Lifei Chen. (2018). Recurrent Neural Networks for Time-to-Event Prediction. Knowledge and Information Systems (KAIS), (Article soumis).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• Haojun, Sun; Rongbo, Chen*; Yong, Qin; Shengrui, Wang. (2017). Holo-Entropy Based Categorical Data Hierarchical Clustering. Informatica, 28(2), 303-328. (Article publié).
• 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), (Article soumis).
• Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang. (2017). Modeling and Predicting Community Structures Changes in Time Evolving Social Networks. IEEE Transactions on Knowledge and Data Engineering (TKDE), (Article soumis).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• Lifei Chen*, Shengrui Wang, Kaijun Wang, Jianping Zhu. (2016). Soft subspace clustering of categorical data with probabilistic distance. Pattern Recognition, 51, 322-332. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• J-P. Sehi Glouzon*, T. Xiong, F. Bolduc, J-P Perreault and S. Wang. (2014). DHCS: A Web Server for Clustering Biological Sequences. Bioinformatics, (Article soumis).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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, (Article soumis).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• D. Wei, Q. Jiang, Y. Wei and S. Wang. (2012). A Novel Hierarchical Clustering Algorithm for Gene Sequences. BMC Bioinformatics, 13(1), 174-189. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• H. Sun* and S. Wang. (2011). Measuring the component overlapping in the Gaussian mixture model. Data Mining and Knowledge Discovery, 23(3), 479-502. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• Mohamed Bouguessa* and Shengrui Wang. (2009). Mining Projected Clusters in High-Dimensional Spaces. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 21(4), 507-522. (Article publié).
• L. Chen*, Q. Jiang and S. Wang. (2008). A hierarchical method for determining the number of clusters. Journal of Software, 19(1), 62-72. (Article publié).
• 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. (Article publié).

Articles de conférence

• 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). (Article soumis).
• 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). (Article soumis).
• 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). (Article soumis).
• 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). (Article publié).
• E. G. Tajeuna*, M. Bouguessa, and S. Wang,. (2019). Survival Analysis for Time Series Forecasting. 36th IEEE International Conference on Data Engineering (2020). (Article soumis).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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). (Article publié).
• Etienne Gael Tajeuna*, Mohamed Bouguessa, Shengrui Wang. (2016). Tracking Communities over Time in Dynamic Social Network. 341-345, DOI. (Article publié).
• 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). (Article soumis).
• 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). (Article soumis).
• 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). (Article publié).
• 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), (Article publié).
• B Chikhaoui*, S Wang, H Pigot. (2013). Causality-Based Model for User Profile Construction from Behavior Sequences. Proceedings of WAINA, 461-468. (Article publié).
• 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. (Article publié).
• S. Renaud-Deputter*, T. Xiong*, S. Wang. (2013). Combining collaborative filtering and clustering for implicit recommender systems. Proceedings of IEEE AINA 2013, 748-755. (Article publié).
• Emilie Au*, M. Bouguessa, S. Wang. (2013). Document Modeling Using Syntactic and Semantic Information. Proceedings of WAINA, 203-206. (Article publié).
• 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. (Article publié).
• L. Chen* and S Wang. (2012). Automated Feature Weighting in Naive Bayes for High-dimensional Data Classification. Proceedings of CIKM-2012, 1243-1252. (Article publié).
• L. Chen* and S. Wang. (2012). Centroid-based Clustering for Graph Datasets. Proceedings of ICPR-2012, 2144-2147. (Article publié).
• L. Chen*, G. Guo and S. Wang. (2012). Nearest Neighbor Classification by Partially Fuzzy Clustering. Proceedings of MAW, 789-794. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• 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. (Article publié).
• A. Kelil*, A. Nordell-Markovits*, S. Wang. (2009). Classification of categorical sequences. 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. (Article publié).
• M. Bouguessa*, Dumoulin, S. Wang. (2008). Identifying authoritative actors in question answering forums-The case of Yahoo! Answers. Proc. of KDD2008, 866-874. (Article publié).
• T. Xiong*, S. Wang, A. Mayers and E. Monga. (2008). Personal bankruptcy prediction using sequence clustering. Proc. of KDD2008 Workshop, 32-38. (Article publié).
• L. Chen*, Q. Jiang, S. Wang. (2008). Probability model for projective clustering on high dimensional data. Proceedings of IEEE ICDM-2008, 755-760. (Article publié).
• A. Kelil*, S. Wang, R. Brzezinski. (2008). SAF: A substitution and alignment free similarity measure for protein seq…. Proc. of ICBB’08, 81-89. (Article publié).
• A. Kelil* and S. Wang. (2008). SCS: A new similarity measure for categorical sequences. Proceedings of IEEE ICDM-2008, Best Papers Awards,, 343-352. (Article publié).

Autres contributions

Cours enseignés

• Algorithmes et structures de données. IFT436. (2016-08-25 à 2017-12-10). Université de Sherbrooke. Canada. Niveau : Premier cycle. (3CR).
• Sujets choisis en intelligence artificielle. IFT704. (2016-06-10 à 2018-08-25). Université de Sherbrooke. Canada. Niveau : Deuxième cycle. (3CR).
• Recherche d'information et forage de données. IFT501. (2015-08-25 à 2018-12-10). Université de Sherbrooke. Canada. Niveau : Premier cycle. (3CR).
• Forage de données. BIN 701. (2015-05-06 à 2017-08-20). Université de Sherbrooke. Canada. Niveau : Deuxième cycle. (3CR).

Activités de collaboration internationale

• Co-investigator. (2017-2020). Chine. Collaboration de recherche avec Fujian Normal University (Profs Lifei Chen, Gongde Guo, etc) incluant co-publications, co-supervision, et co-applications pour des financements.
• Co-investigator. (2012-2015). Chine.
• Co-investigator. (2012-2015). Chine.
• Invited professor. (2013). France.

Présentations

• (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, Chine.
• (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, Chine.
• (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, Pologne.
• (2017). Sequential Representation of Clinical Data for Full-fitting Survival Prediction: Healthcare Study on COPD. IEEE AINA 2017. Taipei, Taïwan, Province de Chine.
• (2016). Discovering and Tracking Influencer-Influencee Relationships between Online Communities. College of Mathematics and Computer Science Fujian Normal University. Fuzhou, Chine.
• (2016). Modeling Hazard in Longitudinal Clinical Data for Predicting COPD Failure. International Workshop on Collaborative Data Analytics. Fuzhou, Chine.
• (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, Chine.
• (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, Chine.
• (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, Chine.
• (2010). An Objective Approach to Determining the Number of Clusters. The Fifth International Conference on Operations Research (CIRO'10). Marrakech, Maroc.
• (2010). High-dimensional Categorical Data Mining and Applications. IEEE ICIS 2010. Xiamen, Chine.