Sukumar Letchmunan | Computer Science | Best Researcher Award

Published on by Database Scientist

Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.

Brigitte Jaumard | Cloud Computing | Best Researcher Award

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Prof. Dr. Brigitte Jaumard | Cloud Computing | Best Researcher Award

Concordia University | Canada

Prof. Dr. Brigitte Jaumard is a preeminent authority in computer science and operations research, specializing in the optimization of communication networks and the application of artificial intelligence. As a Professor in the Department of Computer Science and Software Engineering at Concordia University, her distinguished career is marked by significant academic and industrial contributions. Her foundational education includes a Ph.D., awarded with highest honors, from École Nationale Supérieure des Télécommunications (ParisTech) and a Thèse d’habilitation from Université Pierre et Marie Curie. She has held prestigious appointments, including a Tier I Canada Research Chair (2001-2019), and impactful roles in industry such as Principal Data Scientist at Ericsson’s Global AI Accelerator and Scientific Director at the Computer Research Institute of Montreal (CRIM). Her pioneering research, supported by major grants from NSERC and MITACS, focuses on developing sophisticated models and algorithms for 5G/B5G networks, energy efficiency, and network virtualization. The exceptional impact and volume of her work are demonstrated by a remarkable citation count of over 11,130, an h-index of 46, and an i10-index of 179, alongside multiple best paper awards from leading IEEE conferences. A dedicated mentor to numerous graduate students and postdoctoral fellows, she also actively shapes her field through service on technical program committees, editorial boards, and national grant selection panels, solidifying her legacy as a leader who bridges foundational research with transformative industrial innovation.

Profile: Scopus | Google Scholar

Featured Publications

  • Hansen, P., Jaumard, B., & Savard, G. (1992). New branch-and-bound rules for linear bilevel programming. SIAM Journal on Scientific and Statistical Computing, 13(5), 1194–1217.

  • Hansen, P., & Jaumard, B. (1997). Cluster analysis and mathematical programming. Mathematical Programming, 79(1), 191–215.

  • Hansen, P., & Jaumard, B. (1990). Algorithms for the maximum satisfiability problem. Computing, 44(4), 279–303.

  • Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European Journal of Operational Research, 107(1), 1–18.

  • Audet, C., Hansen, P., Jaumard, B., & Savard, G. (2000). A branch and cut algorithm for nonconvex quadratically constrained quadratic programming. Mathematical Programming, 87(1), 131–152.

  • Audet, C., Hansen, P., Jaumard, B., & Savard, G. (1997). Links between linear bilevel and mixed 0–1 programming problems. Journal of Optimization Theory and Applications, 93(2), 273–300.

  • Hansen, P., & Jaumard, B. (1995). Lipschitz optimization. In C. A. Floudas & P. M. Pardalos (Eds.), Handbook of global optimization (pp. 407–493). Springer.

  • Du Merle, O., Hansen, P., Jaumard, B., & Mladenovic, N. (1999). An interior point algorithm for minimum sum-of-squares clustering. SIAM Journal on Scientific Computing, 21(4), 1485–1505.