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Daniel Aloise
M.Sc. (PUC-Rio, Brésil) et un Ph.D. (Poly)

Research interests and affiliations

Research interests
  • Data Science
  • Big Data
  • Optimization
  • Mathematical Programming
Expertise type(s) (NSERC subjects)
  • 1601 Operations research and management science
  • 2510 Adaptive, learning and evolutionary systems
  • 2713 Algorithms
  • 2715 Optimization

Publications

Recent publications
Journal article
Silva, A., Aloise, D., Coelho, L.C. & Rocha, C. (2021). Heuristics for the dynamic facility location problem with modular capacities. European Journal of Operational Research, 290(2), 435-452. Retrieved from https://doi.org/10.1016/j.ejor.2020.08.018
Conference paper
Fournier, Q., Aloise, D., VAzhari, S.V. & Tétreault, F. (2021). On improving deep learning trace analysis with system call arguments. Paper presented at the 18th IEEE/ACM International Conference on Mining Software Repositories (MSR 2021) (pp. 120-130).
Journal article
Costa, L.R., Aloise, D., Gianoli, L.G. & Lodi, A. (2021). The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case. IEEE Internet of Things Journal, 8(9), 7316-7332. Retrieved from https://doi.org/10.1109/JIOT.2020.3039261
Conference paper
Haouas, M.N., Aloise, D. & Pesant, G. (2020). An Exact CP Approach for the Cardinality-Constrained Euclidean Minimum Sum-of-Squares Clustering Problem. Paper presented at the 17th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2020), Vienna, Austria (pp. 256-272). Retrieved from https://doi.org/10.1007/978-3-030-58942-4_17

Supervision at Polytechnique

COMPLETED

  • Master's Thesis (5)

    • Haouas, M.N. (2020). Résolution exacte du problème de partitionnement de données avec minimisation de variance sous contraintes de cardinalité par programmation par contraintes (Master's Thesis, Polytechnique Montréal). Retrieved from https://publications.polymtl.ca/4207/
    • Heutte, N. (2020). A Divide-and-Conquer Approach to Employee Scheduling (Master's Thesis, Polytechnique Montréal). Retrieved from https://publications.polymtl.ca/5362/
    • Moins, T. (2020). Modèle hybride combinant réseau de neurones convolutifs et modèle basé sur le choix pour la recommandation de sièges (Master's Thesis, Polytechnique Montréal). Retrieved from https://publications.polymtl.ca/5336/
    • Boucaud, L. (2019). Mécanismes d'attention pour les modèles convolutifs dans le cadre de la prédiction de trajectoires (Master's Thesis, Polytechnique Montréal). Retrieved from https://publications.polymtl.ca/3951/
    • Hulot, P. (2018). Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems (Master's Thesis, École Polytechnique de Montréal). Retrieved from https://publications.polymtl.ca/3160/