Taehyun Lee | Behavioral Economics | Research Excellence Award

Mrs. Taehyun Lee | Behavioral Economics | Research Excellence Award

Korea University | South Korea

Mrs. Taehyun Lee is an economist specializing in policy analysis and impact evaluation, with over five years of experience in public expenditure review, labor market policies, and quantitative monitoring of government interventions. She is currently a permanent researcher at the Korea Employment Information Service, leading research at the Youth Policy Center and previously at the National Work Programs & KLoSA Panel Team. Her work focuses on evaluating the effectiveness of nationwide vocational training, direct job creation programs, and social support initiatives for marginalized populations, applying advanced econometric methods such as Propensity Score Matching and Cox Proportional Hazards models to large-scale administrative and survey datasets. Mrs. Lee has contributed to international comparative studies, engaging scholars from China, Sweden, the U.S., and Europe, and has co-authored several high-impact publications, including ESI highly cited papers. She holds an M.A. in Economics (Labor Economics) from Sogang University, a B.A. in Philosophy and International Studies from Ewha Womans University, and is a Ph.D. candidate in Psychology at Korea University, focusing on behavioral insights for policy and economic decision-making. She has received recognition for research excellence, including the Excellent Paper Award from the Korea Social Science Data Archive, and has successfully led cross-national research collaborations. Her work significantly informs evidence-based policy design for labor markets and social protection systems.

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Featured Publication


The relationship between household debt and the Big Five personality traits


– Journal of Behavioral and Experimental Economics, 2025 | Contributors: Taehyun Lee; Almas Heshmati

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

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Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.