Mohamed El Tonbari
Machine Learning & Optimization
Apple
I am an Applied Scientist at Apple, developing optimization and machine learning solutions for logistics and supply chain problems.
I received my PhD in Operations Research at the School of Industrial and Systems Engineering at Georgia Tech with a minor in Machine Learning, and my B.S.E. in Operations Research & Financial Engineering from Princeton University, with a minor in Applications of Computing.
I am generally interested in optimization under uncertainty, sequential decision-making problems, and machine learning.
Papers
Consensus-Based Dantzig-Wolfe Decomposition, El Tonbari M., Ahmed S., European Journal of Operational Research, 2023
Multi-criteria Course Mode Selection and Classroom Assignment Under Sudden Space Scarcity, Navabi-Shirazi M., El Tonbari M., Boland N., Nazzal D., Steimle L.,
Manufacturing & Service Operations Management, 2022Two-Stage Distributionally Robust Optimization with Binary Variables: A Disaster Management Application, El Tonbari M., Toriello A., Nemhauser G. in revision
Data-Driven Two-Stage Conic Optimization with Rare High-Impact Zero-One Uncertainties, Subramanyam A., El Tonbari M., Kibaek K.
Minimizing Cycle Time Variance in Smart Warehousing, El Tonbari M., McGinnis L.
Talks
2021 INFORMS Annual Meeting, 2021 IFORS Conference: Two-Stage Distributionally Robust Optimization with Binary Variables: A Disaster Management Application, October 2021
2019 INFORMS Annual Meeting, Distributed Dantzig-Wolfe Decomposition, Seattle, WA, October 2019
2018 INFORMS Annual Meeting, Minimizing Cycle Time Variance in Smart Warehousing, Phoenix, AZ, November 2018
2017 INFORMS Annual Meeting, Model Based Approach to Smart Warehousing, Houston, TX, November 2017