Nadia Tahiri

Financement

• Grant. (Awarded). Principal Investigator. CRSNG - Subventions à la découverte. (2022-2027)
• Grant. (Awarded). Principal Applicant. Subvention de démarrage - nouvelle professeure. (2021-2023)
• Grant. (Awarded). Collaborator. Création d'un pôle francophone en toxicologie prédictive. (2021-2023)
• Grant. (Awarded). Principal Applicant. Centre de recherche en écologie de l’UdeS (CREUS). (2022)
• Grant. (Awarded). Co-investigator. Stage en apprentissage automatique pour la Fabrication Additive. (2022-2022)
• Fellowship. (Completed). Principal Applicant. Élaboration d'une nouvelle approche pour évaluer les risques liés à l'exposition des enfants aux contaminants de l'environnement (dossier classé au 1er rang au FRQS). (2020-2021)
• Fellowship. (Completed). Principal Applicant. Apprentissage machine appliquée pour détection de fraudes pour régimes d'assurances collectives. (2018-2019)
• Scholarship. (Completed). Principal Applicant. Algorithmes bioinformatiques pour la reconstruction d'arbres consensus multiples. (2013-2015)

Publications

Articles de revue

• Tahiri, N; Fichet, B; Makarenkov, V. (2022). Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance. Bioinformatics (Published).
• Tahiri, N; Veriga, A; Koshkarov, A*; Morozov, B. (2022). Invariant transformers of Robinson-Foulds distance matrices for convolutional neural network. Journal of Bioinformatics and Computational Biology (Published).
• Tahiri, N; Koshkarov, A*. (2022). New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric. Classification and Data Science in the Digital Age, Springer Verlag (Accepted).
• Marchitti, SA; Verner, MA; Tahiri, N; Dillingham, C; Chang, D; LaKind, JC; Hines, E; Fenton, S; Kenneke, JF; Goldsmith, MR. (2022). Predicting Breast Milk:Serum Partitioning Using QSAR Models. Chemical Research in Toxicology (Submitted).
• Lévêque, L*; Tahiri, N; Goldsmith, MR; Verner, MA. (2022). Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals across the Placenta. Computational Toxicology (the first two authors are equal contributors and listed in alphabetical order) (Published).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Boc, A; Lord, E; Tahiri, N; Mazoure, B; Makarenkov, V. (2022). Using Clustering and Machine Learning Algorithms for Intelligent Personalized Shopping Recommendation. IEEE Access (Submitted).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Mazoure, B; Tahiri, N; Makarenkov, V. (2022). Using clustering and machine learning methods to provide intelligent grocery shopping recommendations. Classification and Data Science in the Digital Age, Springer Verlag (Accepted).
• Kuitche, E; Qi, Y; Tahiri, N; Parmer, J; Ouangraoua, A. (2020). DoubleRecViz: A Web-Based Tool for Visualizing Transcript-Gene-Species reconciliation. Bioinformatics 37 (13), 1920-1922. (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2018). A new fast method for inferring multiple consensus trees using k-medoids. BMC evolutionary biology 18 (48), 1-12. DOI. (Published).
• Willems, M; Tahiri, N; Makarenkov, V. (2018). Building explicit hybridization networks using the maximum likelihood and Neighbor-Joining approaches. Archives of Data Science, Series A 4 (1), 1-17. DOI. (Published).
• Willems, M; Tahiri, N*; Makarenkov, V. (2015). A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle. Journal of Bioinformatics and Computational Biology 12 (5), 1450024. DOI. (Published).

Chapitres de livre

• Makarenkov, V; Barseghyan, GS*; Tahiri, N. (2022). Inferring multiple consensus trees and supertrees using clustering: a review. Data Mining is More Than Comprehensive Analysis (1-33). Springer Nature. (Submitted).
• Cordeiro de Amorim, R; Tahiri, N; Mirkin, B; Makarenkov, V. (2017). A Median-Based Consensus Rule for Distance Exponent Selection in the Framework of Intelligent and Weighted Minkowski Clustering. Data Science (97-110). Springer Verlag. DOI. (Published).
• Badescu, D; Tahiri, N; Makarenkov, V. (2016). A new fast method for detecting and validating horizontal gene transfer events using phylogenetic trees and aggregation functions. Pattern Recognition in Computational Molecular Biology: Techniques and Approaches. (1, 483-504). Wiley. DOI. (Published).

Articles de conférence

• Tahiri, N; Koshkarov, A*. (2022). New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric. International Federation of Classification Societies (IFCS). (Accepted).
• Li, W*; Koshkarov, A*; Luu, ML*; Tahiri, N. (2022). Phylogeography: Analysis of genetic and climatic data of SARS-CoV-2. Scientific Computing with Python (SciPy). (Submitted).
• Koshkarov, A*; Li, W*; Luu, ML*; Tahiri, N. (2022). Phylogeography: Analysis of genetic and climatic data of SARS-CoV-2. Scientific Computing with Python (SciPy). (Accepted).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Mazoure, B; Tahiri, N; Makarenkov, V. (2022). Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations. International Federation of Classification Societies (IFCS). (Accepted).
• Bocéno, A; Bloch, S; Tahiri, N; Verner, MA. (2021). Comparing an Acceptable Exposure Level Based on In Vitro Studies of PFOA Hepatotoxicity to Levels Measured in Epidemiologic Studies. Society of Toxicology (SOT), virtual conference. (Published).
• Tahiri, N. (2021). Invasive insects through phylogeography. 12th Annual symposium of Quebec Centre for biodiversity science (QCBS). (Published).
• Li, W*; Luu, ML*; Tahiri, N. (2021). La phylogéographie : à la recherche de la vérité lorsque tout est en mouvement. Nuit des chercheuses et des chercheurs (Finaliste du concours de vulgarisation scientifique). (Accepted).
• Lévêque, L*; Tahiri, N; Goldsmith, MR; Verner, MA. (2021). Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals Across the Placenta. American Society for Cellular and Computational Toxicology (ASCCT). (Published).
• Aouabed, Z; Abdar, M; Tahiri, N; Champagne Gareau, J; Makarenkov, V. (2020). A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease. Learning and Analytics in Intelligent Systems (Springer), 480-489. (Published).
• Bocéno, A; Bloch, S; Tahiri, N; Verner, MA. (2020). A case study evaluating the use of in vitro data on perfluorooctanoic acid (PFOA) hepatotoxicity to derive acceptable exposure levels. The Society of Toxicology of Canada (STC), virtual conference. (Published).
• Tahiri, N; Lévêque, L*; Verner, MA. (2020). Predicting the Transfer of Chemicals through Lactation Using Quantitative Structure-Activity Relationship (QSAR) Modeling. Society of Toxicology (SOT). (Published).
• Tahiri, N; Mazoure, B; Makarenkov, V. (2019). An intelligent shopping list based on the application of partitioning and machine learning algorithms. Proceedings of the 18th Python in Science Conference, 85 - 92. (Published).
• Lévêque, L*; Tahiri, N; Verner, MA. (2019). Predicting the placental transfer of chemicals using quantitative structure-activity relationship (QSAR) modeling. Society of Toxicology of Canada (STC). (Published).
• Tahiri, N; Lévêque, L*; Verner, MA. (2019). Quantitative structure-activity relationship (QSAR) modeling as a tool to assess lactational exposure for data-poor chemicals. Society of toxicology of Canada (STC). (Published).
• Tahiri, N. (2018). A new fast method for inferring multiple consensus trees using k-medoids. Canadian Celebration of Women in Computing (CAN-CWiC). (Published).
• Tahiri, N. (2018). An intelligent shopping list based on partitioning and machine learning algorithms. Neural Information Processing Systems (NeurIPS). (Published).
• Tahiri, N. (2017). A new clustering method for building multiple supertrees using k-means. Proceedings of NIPS-2017, (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2017). A new fast method for building multiple consensus trees using k-medoids. Proceedings of SMC-2017, 31-37. (Published).
• Tahiri, N; Badran, N; Dion-Phénix, H; Meniaï, I; Makarenkov, V. (2017). Avancement des connaissances en bioinformatique en développant un nouvel algorithme pour l’analyse des arbres phylogeographiques. Association Canadienne-Française pour l'Avancement des Sciences (ACFAS). (Published).
• Tahiri, N; Badran, N. (2016). New algorithm to find the relation between genetic and geographic distribution of species. Symposium Sciences biologiques. (Published).
• Tahiri, N*; Willems, M; Makarenkov, V. (2015). Inférence des super-arbres multiples en utilisant l'algorithme des k-moyennes. Proceedings of SFC-2015, 110-114. (Published).
• Cordeiro de Amorim, R; Tahiri, N*; Mirkin, BG; Makarenkov, V. (2015). Minkowski weighted k-means clustering with amedian-based consensus rule. Proceedings of IFCS-2015, 90-110. DOI. (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2014). Classification d’arbres phylogénétiques basée surl’algorithme des k-moyennes. Proceedings of SFC-2014, 49-54. (Published).
• Tahiri, N*; Boc, A; Willems, M*; Makarenkov, V. (2012). Classification des langues Indo-Européennes basée sur un modèle d’identification de transferts horizontaux de gènes. Proceedings of SFC-2012, (Published).

Autres contributions

Gestion d'évènements

• Scientific committee member. (2021-2024) Google Developer Groups (GDG) Cloud Sherbrooke, virtual conference, GDG 2021. (Conference).
• Co-chair, Financial Aid Committee. (2022) for Scientific Computing with Python (SciPy), virtual conference, SciPy 2022. (Conference).
• Co-chair. (2021) Birds of a Feather Committee for Scientific Computing with Python (SciPy), virtual conference, SciPy 2021. (Conference).
• Committee Member, Organizing committee and Program committee. (2016-2021) Montreal Bioinformatics Users Group (MonBUG). (Seminar).
• Committee Member. (2021) Program committee of the 1st International Applied Bioinformatics Conference (iABC), Istanbul University, Turkey, iABC 2021. (Conference).
• Scientific committee member. (2021) Women Techmakers Montreal (WTM), virtual conference, WTM 2021. (Conference).
• Co-Chair. (2020) Financial Aid Committee for Scientific Computing with Python (SciPy), Austin, TX, USA, SciPy 2020. (Conference).
• Scientific committee member. (2020) Women Techmakers Montreal (WTM), virtual conference, WTM 2020. (Conference).
• Committee Member, Organizing committee and Program committee. (2019) Scientific Computing with Python (SciPy), Austin, TX, USA, SciPy 2019. (Conference).
• Scientific committee member. (2019) Women Techmakers Montreal (WTM), Montreal, Canada, WTM 2019. (Conference).
• Scientific committee member. (2019) Women Techmakers Montreal (WTM-2019, Montreal, Canada). (Conference).
• Scientific committee member. (2018) Google Developer Groups (GDG) Montreal, École de technologie supérieure (ÉTS), Montreal, Canada, GDG 2018. (Seminar).
• Scientific committee member. (2018) Women Techmakers Montreal (WTM-2019, Montreal, Canada). (Conference).
• Scientific committee member. (2017) Association for Computing Machinery (ACM) - Canadian Celebration Of Women In Computing, Université du Québec à Montréal (UQÀM), Montreal, Canada, CAN-CwiC-2017. (Conference).
• Chair of Poster Session. (2017) Canadian Celebration of Women in Computing (CAN-CWiC-2017, Montreal, Canada). (Conference).
• Chair of Grad Student Session. (2017) Canadian Celebration of Women in Computing (CANCWiC-2017, Montreal, Canada). (Conference).

Activités de collaboration internationale

• Organizer. France. Bioinformatics Working Group - as part of the Sherbrooke-Montpellier 2019 meetings.

Présentations

• (2022). Analyse d'arbres phylogénétiques en utilisant des données géographiques et climatiques. Centre de recherche en écologie de l'Université de Sherbrooke (CREUS). Sherbrooke, Canada
• (2022). Bio-géographie : avancées et reculs des approches. ÉcoWathever. Sherbrooke, Canada
• (2022). Modélisation de la relation quantitative de structure-activité (QSAR) du passage placentaire des contaminants environnementaux. Interdisciplinary Seminar on Bioinformatics (BIF7002). Canada
• (2022). Modélisation de la relation quantitative de structure d'activité (RQSA) du passage placentaire des contaminants environnementaux. BIF7002 - Séminaire de bio-informatique. Montréal, Canada
• (2022). New algorithms for inferring multiple alternative consensus trees and supertrees. Club Mathématique. Canada
• (2022). New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric. International Federation of Classification Societies (IFCS) - Classification and Data Science in the Digital Age. Porto, Portugal
• (2022). New metrics for phylogenetic tree classification. Séminaire de Bioinformatique. Montpellier, France
• (2022). Nouvelles métriques pour la classification d’arbres phylogénétiques. Club mathématique. Sherbrooke, Canada
• (2022). Pour encourager et accompagner les jeunes femmes scientifiques à prendre leur place. Programme de Mentorat pour les femmes en sciences. Sherbrooke, Canada
• (2022). The benefits and dangers of artificial intelligence. Google Developer Groups Cloud Sherbrooke. Sherbrooke, Canada
• (2022). The impostor syndrome for scientists. Google Developer Groups Cloud Sherbrooke. Sherbrooke, Canada
• (2021). Analyse d'arbres phylogénétiques en utilisant des données géographiques et climatiques. Centre de recherche en écologie de l'Université de Sherbrooke (CRÉUS). Canada
• (2021). Classification des langues Indo-Européennes. Women Developers Academy, North America (WDA), virtual conference. Montreal, Canada
• (2021). Comprendre la phylogénie aux enfants. First edition of La Nuit des chercheur.euse.s. Sherbrooke, Canada
• (2021). La phylogénie expliquée aux niaiseux. La Nuit des chercheuses et des chercheurs au musée de la nature et des sciences de Sherbrooke (MNS2). Sherbrooke, Canada
• (2021). Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals across the Placenta. Meetings of the Montreal Bioinformatics Users Group (MonBUG), virtual conference. Montreal, Canada
• (2020). A case study evaluating the use of in vitro data on perfluorooctanoic acid (PFOA) hepatotoxicity to derive acceptable exposure levels. Society of Toxicology of Canada (STC), virtual conference. Montreal, Canada
• (2019). Algorithmes bio-informatiques pour la reconstruction d'arbres consensus et de super-arbres multiples. BIF7002 - Bioinformatics graduate seminar. Montreal, Canada
• (2019). MonBUG's 10 year anniversary. Meetings of the Montreal Bioinformatics Users Group (MonBUG). Montreal, Canada
• (2019). Quantitative structure-activity relationship (QSAR) modeling as a tool to assess lactational exposure for data-poor chemicals. Society of Toxicology of Canada (STC). Ottawa, Canada
• (2018). An intelligent shopping list based on the application of partitioning and machine learning algorithms. Python Conference (PyCon). Toronto, Canada
• (2018). Clustering multiple supertrees with k-means. Canadian Celebration of Women in Computing (CAN-CWiC). Halifax, Canada
• (2018). Inférence de super-arbres phylogénétiques multiples en utilisant l’algorithme des k-moyennes. INF8881 - Master's in Computer Science Seminar I. Montreal, Canada
• (2018). Une liste d'achats intelligente basée sur l'application des algorithmes de partitionnement et d'apprentissage automatique. Société Francophone de Classification (SFC). Paris, France
• (2017). A new clustering method for building multiple supertrees using k-means. European Conference on Data Analysis (ECDA). Wroclaw, Poland
• (2017). A new fast method for building multiple consensus trees using k-medoids. Société Marocaine de Classification (SMC). Tangier, Morocco
• Nadia Tahiri. (2017). Artificial intelligence and journalism. Hacks/Hackers Montréal meetup. Montreal, Canada
• (2017). Avancement des connaissances bioinformatique en développant un nouvel algorithme pour l’analyse des arbres phylogéographiques. Association Canadienne-Française pour l'Avancement des Sciences (ACFAS), 85e ACFAS Congress - Le calcul informatique de pointe pour l’avancement des connaissances et l’innovation. Montreal, Canada
• (2017). Building explicit hybridization networks using the maximum likelihood and neighbor-joining approaches. European Conference on Data Analysis (ECDA). Wroclaw, Poland
• (2017). Classification of phylogenetic trees. Advances and Thoughts at the Genome Centre (ATGC). Montreal, Canada
• (2017). Climate Change. Model United Nations Framework Convention (MUNF3C). Montreal, Canada
• (2016). Inférence de super-arbres phylogénétiques multiples en utilisant l’algorithme des k-moyennes. Symposium Biological Sciences of University of Montreal. Montreal, Canada