Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

Senior Lecturer at University Sains Malaysia, Malaysia

Dr. Sukumar Letchmunan is a Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia (USM), where he has been serving since 2012. He holds a PhD in Computer Science from the University of Strathclyde, UK, with a focus on pragmatic cost estimation for web applications. Dr. Sukumar has over two decades of academic and research experience, previously serving as a lecturer at Wawasan Open University, Cybernetics College of Technology, and a part-time lecturer at Universiti Putra Malaysia. His career spans teaching, curriculum development, research supervision, and leading national research grants. He is passionate about software engineering, agile project management, and integrating machine learning into practical computing applications.

đŸ”čProfessional Profile:

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🎓Education Background

  • PhD in Computer Science
    University of Strathclyde, UK
    (Thesis: Pragmatic Cost Estimation for Web Applications) – 2013

  • Master in Computer Science (Software Engineering)
    University Putra Malaysia – CGPA 3.479

  • Bachelor in Computer Science (Computer System)
    University Putra Malaysia – CGPA 3.678

  • Diploma in Computer Science
    University Putra Malaysia – CGPA 3.4

đŸ’Œ Professional Development

Senior Lecturer
School of Computer Sciences, Universiti Sains Malaysia (USM) | Nov 2012 – Present

  1. Programme Manager for Bachelor in Software Engineering

  2. Lectures courses: Research Methodology, Software Quality, Software Testing, Discrete Structures, Software Requirement Engineering

  3. Supervised 2 PhD and 8 Master students to graduation

  4. Secured national research grants (e.g., FRGS) totaling over RM 400,000

  5. Awarded “Employee of the Year 2022”

  6. Served as Industrial Fellow under CEO@Faculty programme

Lecturer
Wawasan Open University (WOU) | Aug 2005 – Dec 2007

  1. Developed and authored teaching modules

  2. Published adapted academic book on Microsoft Office 2003

Lecturer
Cybernetics College of Technology (CICT) | May 2001 – Aug 2005

  1. Coordinated diploma programs and supported student recruitment

  2. Named “Best Lecturer” for three consecutive years (2002–2004)

Part-time Lecturer
Universiti Putra Malaysia (UPM) | Jun 2003 – May 2005

  1. Taught core courses including Java Programming

🔬Research Focus

  • Software Engineering and Metrics for Web Applications

  • Agile Project Management & Software Cost Estimation

  • Machine Learning Applications in Software Systems

  • Energy-Efficient Software Design

  • Emotion Modeling for Intelligent Interfaces

  • Crime Hotspot Prediction Using Data Mining and Forecasting Techniques

📈Author Metrics:

  • Prolific Publisher: Over 20 peer-reviewed journal papers between 2020–2022 in journals such as Mathematics, Fractal and Fractional, Symmetry, and Journal of Applied Mathematics and Informatics.

  • Notable publications focus on q-analogues, (p,q)-polynomials, and solutions to differential equations.

  • His work is widely cited in the fields of analytic number theory, q-series, and special functions.

🏆Awards and Honors:

  • Employee of the Year – Universiti Sains Malaysia, 2022

  • Best Lecturer – Cybernetics College of Technology (2002, 2003, 2004)

  • Industrial Fellow – CEO@Faculty Programme

  • Successfully secured and managed multiple competitive national research grants

📝Publication Top Notes

✅ 1. Auto Feature Weighted C-Means Type Clustering Methods for Color Image Segmentation

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, H. Qiu

  • Journal: Engineering Applications of Artificial Intelligence

  • Volume: 153

  • Article Number: 110768

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes a novel clustering approach with automatic feature weighting to improve color image segmentation, enhancing performance in complex visual scenes.

✅ 2. Robust Multi-View Fuzzy Clustering with Exponential Transformation and Automatic View Weighting

  • Authors: Z. Liu, H. Qiu, M. Deveci, S. Letchmunan, L. MartĂ­nez

  • Journal: Knowledge-Based Systems

  • Volume: 315

  • Article Number: 113314

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Presents a fuzzy clustering framework that handles multiple data views using automatic weighting and an exponential transformation for better separation.

✅ 3. Novel Distance Measures on Complex Picture Fuzzy Environment: Applications in Pattern Recognition, Medical Diagnosis and Clustering

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, G. Ulutagay, K. Ullah

  • Journal: Journal of Applied Mathematics and Computing

  • Volume: 71

  • Issue: 2

  • Pages: 1743–1775

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Introduces new distance metrics for picture fuzzy sets and demonstrates effectiveness in diverse uncertain environments.

✅ 4. START: A Spatiotemporal Autoregressive Transformer for Enhancing Crime Prediction Accuracy

  • Authors: U. M. Butt, S. Letchmunan, M. Ali, H. H. R. Sherazi

  • Journal: IEEE Transactions on Computational Social Systems

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Combines transformer architecture with spatiotemporal autoregression to improve predictive accuracy in urban crime analytics.

✅ 5. Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems

  • Authors: D. Wang, S. Letchmunan, J. Liao, H. Qiu, Z. Liu

  • Journal: Journal of Intelligent Decision Making and Information Science

  • Volume: 2

  • Pages: 156–173

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes novel similarity functions to handle high-complexity fuzzy information in multi-criteria decision-making contexts.

.Conclusion:

Dr. Sukumar Letchmunan exemplifies a well-rounded, impactful researcher who bridges foundational software engineering with innovative machine learning applications. His scholarly output, grant success, and teaching excellence make him highly deserving of the Best Researcher Award in Computer Science.

đŸŸ© Recommendation: Strongly Recommended

đŸŸ© Award Title Fit: Best Researcher Award – Software Engineering & Intelligent Systems

Lechen Li | Data Science | Best Researcher Award

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Assist. Prof. Dr. Lechen Li | Data Science | Best Researcher Award

Assistant Professor, at Hohai University, China📖

Lechen Li, Ph.D., is a multidisciplinary researcher and engineer specializing in Engineering Mechanics and Data Science. With a strong foundation in computational mechanics and deep learning, he has contributed significantly to smart grid development, structural health monitoring, and intelligent systems. His award-winning work has been presented at leading international conferences and has garnered recognition for its impact on sustainable infrastructure and advanced engineering solutions.

Profile

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Google Scholar Profile

Education Background🎓

Dr. Lechen Li is an accomplished scholar in Engineering Mechanics and Data Science with extensive academic and research experience. He earned his Ph.D. in Engineering Mechanics from Columbia University in 2023, achieving an impressive GPA of 3.889/4.0. His doctoral research spanned smart grid development, computational structural dynamics, and data-driven system control. Prior to this, he completed a Master of Science in Data Science at Columbia University in 2019, where he excelled academically with a GPA of 3.917/4.0 and received the prestigious Robert A.W. and Christine S. Carleton Scholarship. Dr. Li’s academic journey began at Sichuan University, China, where he earned his Bachelor’s degree in Engineering Mechanics in 2018. Notably, he secured first prizes in the Zhou Peiyuan National Mechanics Modeling Contest and the First Prize Scholarship twice.

Professional ExperienceđŸŒ±

Dr. Li brings a wealth of industry experience that complements his academic achievements. At Colombo International Container Terminals (CICT) in Sri Lanka, he served as a Data Research Analyst, where he developed machine learning models to optimize port logistics and transportation planning using a dynamic reinforcement learning framework. Earlier, during his tenure as a CAE Analyst at the National Institute of Water, Energy and Transportation in China, Dr. Li conducted advanced simulations using the Extended Finite Element Method (XFEM), providing valuable insights into lateral pile-soil pressure distribution on pile groups.

Research Interests🔬

Dr. Li’s research is centered on:

  • Structural Health Monitoring and Control: Developing advanced deep-learning frameworks for real-time system identification and damage detection.
  • Data-Driven Dynamics: Applying machine learning and signal processing techniques for smart grid optimization and time-series forecasting.
  • Computational Mechanics: Leveraging finite element analysis and XFEM for solving complex engineering problems.
  • Sustainability and Infrastructure: Innovating intelligent systems for energy-efficient monitoring and optimization.

Author Metrics 

  • Publications: Dr. Li has co-authored numerous papers in high-impact journals and conferences, including presenting at the 8th World Conference on Structural Control and Monitoring, where he received the Best Conference Paper Award.
  • Citations: His publications have been widely cited, reflecting the practical and theoretical contributions of his research.
  • Academic Awards: Best Paper Award (8WCSCM, 2022), First Prize in Zhou Peiyuan National Mechanics Modeling Contest (2017).

Publications Top Notes 📄

1. Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Applied Energy, 2021
  • Citations: 82
  • Summary: This study focuses on improving short-term electricity load forecasting at the apartment level. The authors developed a modified neural network model that integrates auto-regressive features to enhance prediction accuracy. The approach has implications for optimizing energy management and grid operations in residential buildings.

2.Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Energy and Buildings, 2021
  • Citations: 32
  • Summary: This paper examines the effects of COVID-19 lockdowns on residential electricity consumption patterns. The study provides insights into shifts in energy usage due to work-from-home trends and discusses the implications for grid stability and planning.

3.Structural damage assessment through a new generalized autoencoder with features in the quefrency domain

  • Authors: L. Li, M. Morgantini, R. Betti
  • Published in: Mechanical Systems and Signal Processing, 2023
  • Citations: 28
  • Summary: The research introduces a novel autoencoder model that utilizes features in the quefrency domain for structural damage detection. The methodology enhances damage assessment accuracy and offers a new perspective in signal processing for civil infrastructure health monitoring.

4. A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure systems

  • Authors: L. Li, R. Betti
  • Published in: Journal of Civil Structural Health Monitoring, 2023
  • Citations: 8
  • Summary: This work proposes a machine learning-driven data augmentation technique aimed at improving structural damage classification in civil infrastructure systems. The study addresses the challenges of limited data availability in real-world scenarios and improves model robustness.

5. Experimental investigation of the dynamic mechanical properties of concrete under different strain rates and cyclic loading

  • Authors: L. Gan, Y. Liu, Z. Zhang, Z. Shen, L. Li, H. Zhang, H. Jin, W. Xu
  • Published in: Case Studies in Construction Materials, 2024
  • Citations: 4
  • Summary: This experimental study explores the dynamic mechanical behavior of concrete under varying strain rates and cyclic loading conditions. The findings contribute to understanding the material’s performance in diverse loading scenarios, which is crucial for construction and structural design.

Conclusion

Dr. Lechen Li is undoubtedly a highly deserving candidate for the Best Researcher Award. His innovative contributions to engineering mechanics, data science, and structural health monitoring, combined with his solid academic background, make him a strong contender. His research not only pushes the boundaries of technology but also has significant real-world implications for energy management, infrastructure sustainability, and smart grid optimization.

While there are areas where he can expand his influence—such as increasing collaborations with industry, diversifying research, and engaging more broadly with the public—his current achievements already demonstrate his potential for continued leadership in these fields. His work is set to contribute substantially to the next generation of intelligent systems, and with continued focus on bridging academia and industry, Dr. Li will undoubtedly remain at the forefront of his field.

Hence, Dr. Lechen Li’s selection for the Best Researcher Award is both well-earned and a recognition of his future promise as a trailblazer in engineering and data science.