Miguel Lejeune | Decision Sciences | Best Researcher Award

Published on by Award and Honors

Prof. Miguel Lejeune | Decision Sciences | Best Researcher Award 

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

Scopus

ORCID

Google Scholar

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:

  • Theoretical contributions: asymptotic and nonasymptotic performance guarantees.

  • Practical insights: addresses solver tolerances and provides sensitivity/stability analysis.

  • Computational method: decomposed training problem solved via branch-and-cut or similar.

  • Applications/code: includes supplemental materials with code/data.

  • 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):

  • Formulation: Scheduling battery-electric locomotives to charge/discharge vis-à-vis grid needs.

  • Grid services: May include frequency regulation, spinning reserves, voltage/frequency stabilization.

  • 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.

Jiseong Byeon | Computer Science | Best Researcher Award

Published on by Award and Honors

Mr. Jiseong Byeon | Computer Science | Best Researcher Award 

Mr. Jiseong Byeon at Department of Industrial and Systems Engineering, Dongguk University, South Korea.

Jiseong Byeon is a passionate and emerging researcher in the field of artificial intelligence and computer vision, currently pursuing an M.S. in Industrial and Systems Engineering at Dongguk University, Seoul. With a multidisciplinary academic background combining global business and systems engineering, Jiseong brings a unique blend of strategic thinking and technical expertise. His research is centered around the development of intelligent image-based systems, particularly in the medical domain. He has experience working with advanced deep learning frameworks and has contributed to projects involving 3D human modeling and predictive analytics. Known for his curiosity and collaborative spirit, he aims to advance healthcare and human-computer interaction through innovative AI models. 📸🧠💡

Professional Profile

ORCID

🎓 Education

Jiseong Byeon is currently enrolled in a Master’s program in Industrial and Systems Engineering at Dongguk University, Seoul, beginning in September 2024. He previously earned his Bachelor of Arts in Global Business from Dong-A University in Busan, graduating in August 2024. His educational journey has been a unique blend of global business principles and technical problem-solving, giving him a diverse perspective on interdisciplinary research. During his undergraduate years, Jiseong began exploring data science and AI applications, which led him to transition fully into research-focused engineering. Through academic coursework, hands-on lab experiences, and independent study, he has built a solid foundation in data analytics, deep learning, and applied computer vision techniques. 🏫📚🧑‍🎓

💼 Experience

Jiseong Byeon has amassed valuable research experience across both undergraduate and graduate levels. Currently serving as a Graduate Researcher at Dongguk University since September 2024, he is engaged in developing models for 3D human body reconstruction using Vision Transformer architectures. This cutting-edge work aims to transform how AI interprets and renders human anatomy in digital formats. Previously, from March 2022 to August 2024, he worked as an Undergraduate Research Assistant at Dong-A University. There, he contributed to building encoding-based click prediction models and performed in-depth crime factor analysis using Seoul city data. These diverse experiences have honed his data interpretation skills and technical creativity, preparing him for advanced research and real-world AI application. 🖥️🔍📊

🔬 Research Interests

Jiseong Byeon’s research interests lie at the intersection of artificial intelligence, computer vision, and human modeling. His key areas include Image-to-Image Translation using the Pix2Pix framework, 3D Human Body Modeling, and Vision Transformers for medical applications. He is deeply motivated to apply deep learning algorithms to tasks that require detailed visual interpretation—especially those in the medical field where accurate prediction can significantly enhance outcomes. His work also explores how AI can be used for real-time inference and post-surgical visualization, such as predicting body shape changes. Additionally, Jiseong is keen on exploring the scalability of such models for widespread, ethical, and efficient implementation. 🤖🧬👨‍⚕️

🏆 Awards

While still early in his research career, Jiseong Byeon has shown exceptional promise and has been consistently recognized by his academic mentors for his innovation and diligence. He has been nominated for several internal research awards at Dong-A University, particularly for his work on crime prediction modeling and click prediction systems. His transition to graduate-level research was also supported by faculty recommendations based on the excellence of his undergraduate research projects. With his first peer-reviewed publication accepted and increasing involvement in high-impact research domains, he is a strong candidate for early-career research recognition and award nominations. 🏅📈🌟

📚 Top Noted Publications

Byeon has contributed to a peer-reviewed article that showcases the application of deep learning in medical image analysis:

The paper titled “Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation” by Kim, M., Byeon, J., Chang, J., and Youm, S., published in Applied Sciences in 2025, presents a novel approach to forecasting post-liposuction body contours using RGB image-to-image translation techniques.

Key Details:

  • Authors: M. Kim, J. Byeon, J. Chang, and S. Youm

  • Publication Year: 2025

  • Journal: Applied Sciences

  • Citation Count: Cited by 3 articles as of 2025

Research Highlights:

The study focuses on leveraging RGB image-to-image translation methods to predict the outcomes of liposuction procedures. By utilizing preoperative images, the model aims to generate realistic visualizations of post-surgical body shapes, enhancing patient consultations and surgical planning.

Related Works:

While direct citations of this paper are limited, related research in the domain includes:

  • Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: This study introduces a deep learning approach to generate 3D body models from 2D images, facilitating obesity monitoring and body shape analysis. scholarworks.dongguk.edu+2Dongguk University+2MDPI+2

  • Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values: This research emphasizes the use of 3D body data to enhance obesity diagnosis models, reflecting detailed body type information. MDPI+4ResearchGate+4MDPI+4

  • Predictive Model for Abdominal Liposuction Volume in Patients with Obesity Using Machine Learning: This study develops a machine learning model to predict liposuction volumes, aiding in surgical planning for obese patients.

Conclusion

Jiseong Byeon is a highly promising early-career researcher with a strong foundation in computer vision, deep learning, and real-world applications. His current trajectory suggests significant potential for future impact in both academic and applied AI research. While it may be slightly early for a top-tier “Best Researcher Award”, he is exceptionally well-positioned for a “Rising Star” or “Promising Researcher” recognition. With continued publication, international exposure, and leadership development, he could become a strong contender for major awards in the near future.