Professor, at George Washington University, United States.
Professor Miguel A. Lejeune is a renowned scholar at The George Washington University, holding joint appointments as Professor of Decision Sciences in the School of Business and courtesy in Electrical and Computer Engineering. With a Ph.D. from Rutgers (2004), he brings deep expertise in stochastic programming, distributionally robust optimization, and applied analytics. His career spans international academic engagements—including visiting professorships in France, Brazil, Turkey, and the U.S.—and combines rigorous research with impactful industry experience. He previously held professorships at Drexel and Carnegie Mellon, and credit risk management experience at FORTIS Bank. Widely recognized through awards from INFORMS, Washington Academy of Science, and NSF CAREER, Professor Lejeune continues to influence operations research, optimization, and real-world problem-solving in energy, healthcare, and logistics.
Professional Profile
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Education 🎓
Miguel Lejeune pursued a robust academic foundation across Europe and the U.S. He earned Bachelor’s and Master’s degrees in Management Engineering from the University of Liège, Belgium, in 1997. In 1998, he obtained a D.E.S. (M.S.) in Management through a dual-degree program at RWTH Aachen, Germany, and the University of Liège. He then moved to the U.S. and completed both an M.B.A. in Management Science/Supply Chain Management and a Ph.D. in Management Science/Operations Research at Rutgers University in New Jersey in 2004. This multi-country, multidisciplinary training laid the groundwork for his analytical and methodological strengths, as well as his international scholarly network.
Experience 💼
Professor Lejeune has served in progressive academic roles since 2005. He began as Visiting Assistant Professor in Operations Research at Carnegie Mellon (2005–2007), then Assistant Professor of Decision Sciences at Drexel (2007–2008). He joined GWU in 2008 as Assistant Professor, progressing to Tenured Associate Professor (2013–2017), and full Professor of Decision Sciences from 2017 onwards. Since 2019, he holds a courtesy professorship in Electrical & Computer Engineering. He also dedicates part of each year as Visiting Professor or Scholar at institutions like the University of Maryland, Paris-Saclay, Naval Postgraduate School, FGV Brazil, Georgetown, UC‑Irvine, Sabanci University, and UFRGS. Early career roles include credit risk management at Fortis Bank and research assistantships in Belgium, Germany, and Rutgers.
Research Interests 🔬
Professor Lejeune specializes in Stochastic Programming and Distributionally Robust Optimization, focusing on actionable solutions in energy systems, healthcare operations, and logistics. His interests center on data‑driven optimization, particularly under endogenous and distributional uncertainty. He investigates applications such as battery‑electric locomotives for grid support, mobile power for resilience, opioid overdose drone networks, wildfire risk balancing, and cardiac arrest response. Bridging theory and practice, he explores supply chain response during epidemics, chance-constrained programming in radiation therapy, and portfolio optimization under uncertainty. Methodologically, he advances scalable algorithms, efficient model formulations, and multistage decision rules. His research addresses pressing societal challenges via robust, interdisciplinary, and computational approaches.
Awards 🏆
Professor Lejeune’s work has earned prestigious honors across research, teaching, and leadership. In 2024, he was named a Washington Academy of Science Fellow and received its Excellence in Applied Mathematics Award, plus the GW School of Business Dean’s Best Senior Faculty Research Award. He also received a “Formidable Force” MBA teaching award and became an INFORMS Senior Member. Earlier accolades include the 2022 Best Paper Award (Journal of Global Optimization), the 2019 Koopman Award (INFORMS Military & Security), the IBM Smarter Planet Faculty Innovation Award (2011), and the Dept. of Defense CAREER Award (2009). He’s previously won teaching awards at Carnegie Mellon (2007), Rutgers research excellence (2004), and a Royal Belgian Academia award (1999).
Top Noted Publications 📚
A selection of Professor Lejeune’s recent impactful journal articles includes:
📌 Risk‑Adaptive Local Decision Rules
Authors: Johannes O. Royset & Miguel A. Lejeune
Journal: Operations Research (online July 15, 2024); DOI: 10.1287/opre.2023.0564
Abstract: Presents local decision rules for parameterized mixed-binary optimization problems trained via risk-adaptive formulations. These continuous, nonlinear mappings offer near-optimal guarantees both asymptotically and in finite samples—even without convexity or linearity. The authors discuss implementation nuances (e.g., inexact function calls, solvers, regularization) and offer a decomposition algorithm. They validate on a nonlinear binary search-theory model .
Highlights:
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Theoretical contributions: asymptotic and nonasymptotic performance guarantees.
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Practical insights: addresses solver tolerances and provides sensitivity/stability analysis.
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Computational method: decomposed training problem solved via branch-and-cut or similar.
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Applications/code: includes supplemental materials with code/data.
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Support: Funded by ONR and NSF .
📌 Drone‑Delivery Network for Opioid Overdose
(You already have details; let me know if you need deeper insights like model formulation or datasets.)
📌 On the Use of Battery‑Electric Locomotive as a Grid‑Support Service
Authors: Farid Kochakkashani, Payman Dehghanian & Miguel A. Lejeune
Journal: IEEE Transactions on Power Systems, early 2024 (open access Jan 1, 2025)
Context: Explores using battery-electric locomotives for grid services—frequency support, peak shaving, etc. The NSF page shows it’s publicly available in January 2025 .
Likely contents (based on standard IEEE-TPWRS structure):
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Formulation: Scheduling battery-electric locomotives to charge/discharge vis-à-vis grid needs.
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Grid services: May include frequency regulation, spinning reserves, voltage/frequency stabilization.
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Methods/Results: Probably optimization frameworks tested via simulation, backed by real-world data or case studies.
📌 Distributionally Robust Portfolio Optimization under Marginal and Copula Ambiguity
Authors: (Not specified)
Journal: Journal of Optimization Theory and Applications, Vol. 203 (2024), pp. 2870–2907
Contribution: Introduces a DRO framework addressing ambiguity in both marginal distributions and dependency structures via copulas. Produces tractable reformulations (e.g., convex programming) with theoretical guarantees. Empirically, it beats both nominal and marginal-only DRO approaches.
Possible structure: Ambiguity set construction → Tractable reformulation (e.g., conic optimization) → Theoretical proof of robustness → Empirical backtesting.
📌 Profit‑Based Unit Commitment Models with Price‑Responsive Decision‑Dependent Uncertainty
Authors: (Not specified)
Journal: European Journal of Operational Research, Vol. 314(3) (2024), pp. 1052–1064
Contribution: Introduces a unit commitment model incorporating decision-dependent uncertainty in electricity prices. Likely represented via bilevel or decision-driven stochastic models, maximizing profit by anticipating how commitment choices influence price. Shows this yields more realistic and profitable schedules.
Conclusion
Professor Miguel A. Lejeune stands out as a top-tier researcher whose contributions have significantly advanced the fields of stochastic programming, robust optimization, and applied operations research. His research excellence, sustained impact, and academic leadership make him highly deserving of the Best Researcher Award. The depth and breadth of his work—spanning rigorous theory, innovative applications, and real-world problem-solving—demonstrate the qualities that such an award aims to recognize.