Mohamed Wahbi is a seasoned AI and optimization engineer, currently a Staff Engineer at Qualcomm in Ireland with over a decade of experience spanning academia and industry. He holds a joint PhD in Artificial Intelligence from the University of Montpellier and Mohammed V University, Morocco, and has built a strong track record as a researcher and educator across Europe. His career blends research leadership with hands-on engineering, from assistant professor roles in France to a Staff Research Scientist position at Collins Aerospace and now Qualcomm, focusing on data-driven decision-making, optimization, and multi-agent coordination. He is an active open-source contributor to constraint programming, notably for Choco-Solver, where he improved internal representations, added propagation-guided local search, and stabilized propagation with permanent propagators. His work demonstrates a unique blend of theoretical AI with practical solver engineering, enabling scalable decision-making in constrained environments. Based in Munster, Ireland, his interests span AI, machine learning, operations research, and computational game theory.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at University of Montpellier, France
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at University Mohammed V-Agdal, Morocco
Bachelor of Science (B.Sc.), Mathematics and Computer Science, Bachelor of Science (B.Sc.), Mathematics and Computer Science at University Ibn Zohr, Morocco
An open-source Java library for Constraint Programming
Role in this project:
Back-end Developer
Contributions:5 commits, 7 PRs, 34 comments in 2 days
Contributions summary:Mohamed primarily focused on enhancing the Choco Solver library's internal representation and debugging capabilities. Their commits include updates to `toString` methods for key classes like `IntDecision` and `DecisionPath`, improving the clarity of decision-making information during debugging. Further, they implemented a Propagation Guided LNS from Perron et al. CP2004 for more effective constraint solving and improved the solver's representation for clearer model inspection, adding the decision path. Finally, the user corrected and improved the propagation trigger to account for permanent propagators, ensuring the solver's stability.
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