Daniel Aloise

Daniel Aloise is a Full Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal since 2016. He also holds an Assistant Professor position at Universidade Federal do Rio Grande do Norte, Brazil, since 2009. Aloise earned his Ph.D. in Applied Mathematics from Polytechnique Montréal in 2009 under Pierre Hansen, focusing on exact methods for minimum sum-of-squares clustering (k-means). His research spans data mining, machine learning, optimization, and mathematical programming.

• Education: Ph.D. (Polytechnique Montréal), M.Sc. (Pontifical Catholic University of Rio de Janeiro), B.Eng. (Universidade Federal do Rio Grande do Norte).
• Affiliations: Canada Excellence Research Chair in Data Science for Real-Time Decision-Making; Member of GERAD (Group for Research in Decision Analysis).

His research bridges optimization techniques with real-time data science challenges, producing impactful work in clustering algorithms, wireless signal prediction, and humanitarian logistics. Recent projects include HurricaneLog for disaster response data collection and novel methods for optimizing bike-sharing systems. Aloise's work frequently appears in leading journals like INFORMS Journal on Computing and IEEE Transactions.

Scientific honors include:

• Canada Excellence Research Chair in Data Science for Real-Time Decision-Making
• GERAD membership

Aloise has supervised 7 Ph.D. and 12 Master’s students at Polytechnique Montréal, focusing on topics like bug deduplication, 3D mapping optimization, and personnel scheduling. His methodological contributions include variable neighborhood search heuristics and Lagrangian-based constraint analysis.