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Nadia Tahiri

Professeure, Faculté des sciences

FAC. SCIENCES Informatique

Présentation

Sujet de recherche

Bioinformatics, Algorithms

Disciplines de recherche

Computer Science, Applied Mathematics

Mots-clés

Bioinformatics, Algorithms, Phylogenetic tree, Clustering, Classification, Computational biology, Supertree, Consensus tree, Bioinformatic software

Intérêts de recherche

Development of bioinformatics software. Study of mechanisms of reticulate evolution, hybridization and lateral gene transfer. Reconstruction of genetic structures from incomplete data sets.

Langues parlées et écrites

Arabe, Anglais, Français, Espagnol (castillan)

Diplômes

(2021). Occupational and Environmental Health (Post-doctorate, PostDoctorate). Université de Montréal.

(2019). GPA : 4.3/4.3, Algorithmes bioinformatiques pour la reconstruction d'arbres consensus et de super- arbres multiples (Doctorate, Doctorate of Science). Université du Québec à Montréal.

(2012). GPA : 4.3/4.3, Un nouvel algorithme pour retrouver les relations phylogénétiques entre la distribution géographique des espèces et leurs compositions génétiques (Master's Thesis, Masters of Science). Université du Québec à Montréal.

(2010). GPA : 4.26/4.3, Un nouvel outil pour l'inférence d'arbres phylogénétiques (Diploma, DESS in bioinformatics). Université du Québec à Montréal.

(2002). Bioinformatics, Cell Biology and Physiology (Bachelor's, Bachelors of Science). Université de Bourgogne.

(2001). Science and Technology (Diploma, Bachelors of Science). Université de Bourgogne.

Expérience académique

Assistant professor. (2021-). Université de Sherbrooke. Canada.

Postdoctoral researcher. (2019-2021). Université de Montréal. Canada.

Financement

  • Grant. (Under Review). Principal Investigator. NSERC - Discovery Grant. (2022-2027)
  • Grant. (Awarded). Principal Applicant. Starting grant - new professor. (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 (First rank in the 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