Daniel Aloise

Daniel Aloise is a Full Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He is a member of GERAD (Group for Research in Decision Analysis) and IVADO (Institute for Data Valorization), focusing on data science, optimization, and mathematical programming. His career spans institutions in Brazil and Canada, with significant contributions to clustering, classification, and operational research.

• Ph.D. in Exact algorithms for minimum sum-of-square clustering (HEC Montréal, 2009)

Research Interests include data mining, optimization, mathematical programming, and algorithms. His work addresses challenges in big data, clustering algorithms, and classification models, applying these to diverse fields such as psychology, engineering, marketing, and disaster response. He explores polynomial-time algorithms for complex clustering problems and deep learning frameworks for unsupervised classification.

Recent Articles emphasize optimization techniques (Benders decomposition, column generation), wireless signal prediction, bike-sharing inventory rebalancing, and serious games for disaster response data. These works integrate operations research, machine learning, and computational efficiency.

Scientific Awards include the 2024 Omega Best Paper Award, 2023 CAPTRS Serious Games Award, and multiple CNPq Productivity Scholarships (2015–2018, 2012–2014). He received distinctions for his Ph.D. thesis and placement in international competitions.

Supervision covers 7 Ph.D. and 12 Master's theses completed at Polytechnique Montréal, addressing topics like bug severity detection, anomaly analysis, and vehicle routing optimization. His lab collaborates with industry partners on real-time decision-making systems.