Tag: Computer Science Award

Summary of Suitability for the Research for Women Researcher Award

Dr. G. Vijaya M.E. Ph.D. stands out as a highly qualified and accomplished educator with over 18 years of experience across multiple domains, including administration, operations management, teaching, curriculum development, and research. Her extensive academic and professional journey has consistently focused on empowering students, particularly women, and making significant contributions to the educational landscape.

Dr. Vijaya has been deeply involved in initiatives that promote the advancement of women in engineering and technology. As the Principal In-Charge at Bapatla Women’s Engineering College and later as a professor at Sri Krishna College of Engineering and Technology, she has taken leadership roles that directly impact female students. She has been instrumental in spearheading projects that aim to enhance the skill sets of women, such as the Pradhan Mantri Kaushal Vikas Yojana, which focuses on rural self-employment, and the various Faculty Development Programs she has initiated.

🎓 Education

Dr. G. Vijaya holds a Doctorate (Ph.D.) in Computer Science and Engineering from Annamalai University, specializing in Artificial Intelligence and Machine Learning applications. Her Master’s degree (M.E.) in Computer Science has provided her with a strong foundation in software development, data structures, and emerging technologies. She completed her Bachelor’s degree in Computer Science and Engineering, excelling in her academic pursuits and demonstrating a passion for research from an early stage. Throughout her educational journey, she has been an active participant in technical workshops, conferences, and training programs. She has also completed various certifications, including NPTEL courses in Python for Data Science and Soft Skills Development. Her academic background has enabled her to bridge the gap between theoretical knowledge and real-world applications, allowing her to develop innovative solutions and contribute to cutting-edge research in Artificial Intelligence, Cybersecurity, and Computational Biology.

💼 Professional Experience

Dr. G. Vijaya has an extensive career in academia, spanning multiple prestigious institutions. She started as an Assistant Professor at Kalasalingam University and later progressed to Associate Professor at Bapatla Women’s Engineering College. She has served as Principal In-Charge, overseeing institutional development, faculty training, and curriculum enhancement. Currently a Professor at Sri Krishna College of Engineering and Technology, she is actively involved in mentoring students, leading research initiatives, and implementing AI-driven educational advancements. Her expertise includes academic administration, accreditation coordination (NBA, NAAC), and research proposal development. She has successfully secured government funding for AICTE-sponsored projects, focusing on ethical AI and smart technology applications. Additionally, she has played a key role in increasing departmental pass percentages and establishing industry-academic collaborations. With a passion for leadership, she has served as a Board of Studies (BoS) member, IEEE Coordinator, and Department Advisory Committee (DAC) Coordinator.

🏅 Awards and Recognition

Dr. G. Vijaya’s contributions to academia and research have been widely recognized. She was awarded an Honorary Doctorate (D.Litt.) from the University of South America for her exceptional work in Computer Science. She also received the IEAE Young Achiever Award for her research excellence. She has been certified as a Fortinet Certified Associate in Cybersecurity and recognized as a mentor for the IEEE Sustainable Solution for Humanity 2024. Her book, Futuristic Trends in Computing Technologies and Data Sciences, has been published by Iterative International Publishers. She has received multiple Certificates of Appreciation for achieving outstanding student results in AI and Machine Learning courses. Additionally, she secured research funding from AICTE, CSIR, and TNSCST for projects on AI ethics and smart agriculture. Her dedication to education, leadership in accreditation processes, and mentorship in research have made her a distinguished figure in Computer Science.

🌍 Research Skills On Computer Science

Dr. G. Vijaya specializes in Artificial Intelligence, Machine Learning, Deep Learning, and Cybersecurity. She has extensive experience in AI-driven predictive analytics, ethical AI integration, and IoT-enabled smart solutions. Her research contributions extend to Computational Biology, Natural Language Processing, and Blockchain for secure computing. She has successfully guided research projects in AI ethics, data security, and cloud computing. She has authored high-impact research papers and collaborated with industry experts on AI applications. As a Board of Studies (BoS) member, she has helped shape Computer Science curricula to incorporate cutting-edge technologies. She has mentored students in AI-based problem-solving competitions and secured research grants for AICTE-sponsored projects. Her ability to integrate AI methodologies with real-world applications has positioned her as a leading researcher in the field. She continues to contribute to emerging trends in AI governance, cybersecurity, and data-driven decision-making.

Publication Top Notes

Optimization and analysis of microwave-assisted extraction of bioactive compounds from Mimosa pudica L. using RSM & ANFIS modeling
- Authors: V. Ganesan, V. Gurumani, S. Kunjiappan, T. Panneerselvam, …
- Citations: 43
- Year: 2018
An adaptive preprocessing of lung CT images with various filters for better enhancement
- Authors: G. Vijaya, A. Suhasini
- Citations: 36
- Year: 2014
A novel noise reduction method using double bilateral filtering
- Authors: G. Vijaya, V. Vasudevan
- Citations: 17
- Year: 2010
Automatic detection of lung cancer in CT images
- Authors: G. Vijaya, A. Suhasini, R. Priya
- Citations: 15
- Year: 2014
Drivers Drowsiness Detection using Image Processing and I-Ear Techniques
- Authors: S. Ananthi, R. Sathya, K. Vaidehi, G. Vijaya
- Citations: 13
- Year: 2023
A simple algorithm for image denoising based on ms segmentation
- Authors: G. Vijaya, V. Vasudevan
- Citations: 13
- Year: 2010
Bilateral filtering using modified fuzzy clustering for image denoising
- Authors: G. Vijaya, V. Vasudevan
- Citations: 9
- Year: 2010
Image Denoising based on Soft Computing Techniques
- Authors: G. Vijaya, V. Vasudevan
- Citations: 4
- Year: 2011
A review analysis of attack detection using various methodologies in network security
- Authors: P. R. Kanna, S. Gokulraj, K. Karthik, G. Vijaya, G. S. Kumar, G. Rajeshkumar
- Citations: 3
- Year: 2022
Deep learning-based computer-aided diagnosis system

Authors: G. Vijaya
Citations: 2
Year: 2022

Le Yao | Computer Science | Best Researcher Award

Published on 17/02/202517/02/2025 by Academic Excellence

Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

Professional Profile

Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

🎓 Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

💼 Professional Experience

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022–present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019–2022) at Zhejiang University’s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yao’s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

🏅 Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yao’s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

🌍 Research Skills On Computer Science

Le Yao’s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

Publication Top Notes

Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application
- Authors: L Yao, Z Ge
- Citation: 295
- Year: 2017
- Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498
Big data quality prediction in the process industry: A distributed parallel modeling framework
- Authors: L Yao, Z Ge
- Citation: 108
- Year: 2018
- Journal: Journal of Process Control, 68, 1-13
Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure
- Authors: B Shen, L Yao, Z Ge
- Citation: 102
- Year: 2020
- Journal: Control Engineering Practice, 94, 104198
Scalable semisupervised GMM for big data quality prediction in multimode processes
- Authors: L Yao, Z Ge
- Citation: 90
- Year: 2018
- Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692
Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data
- Authors: L Yao, Z Ge
- Citation: 80
- Year: 2016
- Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138
Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data
- Authors: L Yao, Z Ge
- Citation: 62
- Year: 2019
- Journal: Engineering Applications of Artificial Intelligence, 81, 450-465
Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis
- Authors: L Yao, Z Ge
- Citation: 62
- Year: 2017
- Journal: Control Engineering Practice, 61, 72-80
Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction
- Authors: L Yao, Z Ge
- Citation: 61
- Year: 2020
- Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792
Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach
- Authors: L Yao, Z Ge
- Citation: 60
- Year: 2017
- Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993
Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data
- Authors: W Shao, L Yao, Z Ge, Z Song
- Citation: 53
- Year: 2018
- Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373

Tajunisha N | Computer Science | Best Faculty Award

Published on 13/02/202513/02/2025 by Academic Excellence

Dr. Tajunisha N | Computer Science | Best Faculty Award

Dr. Tajunisha N, Sri Ramakrishna College of Arts & Science for Women, India

Dr. N. Tajunisha is a distinguished academician and researcher in Computer Science, currently serving as Professor and Head of the Department at Sri Ramakrishna College of Arts & Science for Women. With over 27 years of academic experience and 23 years of research expertise, she specializes in Data Mining, Machine Learning, Big Data Analytics, and Networks. She earned her Ph.D. from Mother Teresa Women’s University, Kodaikanal, and has been an influential figure in research and development. As a leader, she has held key positions such as Research Coordinator, IQAC Coordinator, and Institution Innovation Cell (IIC) President. Her contributions to academia include publishing research papers in prestigious journals, securing research funding, and mentoring Ph.D. scholars. Recognized with the Senior Educator and Scholar Award, she actively collaborates with institutions like IBM, Rently, and L&T EDUTECH. Her work continues to shape the future of Computer Science education and research.

Professional Profile

Suitability of Dr. N. Tajunisha for the Research for Best Faculty Award

Dr. N. Tajunisha is a distinguished academic leader with a strong record of excellence in teaching, research, and institutional development. With 27 years of academic experience and 23 years of research expertise, she has significantly contributed to the fields of Data Mining, Machine Learning, Big Data Analytics, and Networks. As a Professor and Head of the Department of Computer Science at Sri Ramakrishna College of Arts & Science for Women, she has played a crucial role in shaping the institution’s academic and research landscape.

Her research contributions are noteworthy, with 34 journal publications, 25 conference papers, and 9 SCOPUS-indexed papers, along with securing Rs. 3.17 lakhs from UGC for a Minor Research Project and Rs. 76 lakhs for a DST-FIST project. Dr. Tajunisha’s role as a Ph.D. guide, having successfully mentored three doctoral scholars and currently supervising five more, reflects her dedication to research mentorship. Her collaborations with IBM, Rently, L&T EDUTECH, Easy Design System, and VConnect highlight her industry engagement, while her participation in Doctoral Committees, Board of Studies (BOS), and Programme Advisory Committees showcases her leadership in academic governance.

🎓 Education

Dr. N. Tajunisha has an extensive educational background in Computer Science and Mathematics. She earned her Ph.D. in Computer Science from Mother Teresa Women’s University, Kodaikanal, in 2013, focusing on advanced research methodologies. Before that, she completed her M.Phil. in Computer Science from Bharathiar University, where she developed expertise in data analysis and computational techniques. Her academic journey began with a Master of Computer Applications (MCA) and a Bachelor’s degree in Mathematics from Madurai Kamaraj University, providing her with a strong mathematical foundation essential for algorithm development and computational problem-solving. Her diverse academic background has equipped her with critical analytical skills, enabling her to contribute significantly to the fields of Data Mining and Machine Learning. Her education and continuous professional development have played a crucial role in her ability to drive research innovations and mentor future scholars in Computer Science.

💼 Professional Experience

Dr. N. Tajunisha has over 27 years of academic experience and 23 years in research, significantly shaping Computer Science education. As the Professor & Head of the Department of Computer Science at Sri Ramakrishna College of Arts & Science for Women, she has spearheaded numerous academic and research initiatives. From 2013 to 2018, she served as the Research Coordinator, facilitating advanced research projects and securing substantial funding, including a Rs. 76 lakh DST-FIST grant. She has also played a pivotal role as the IQAC Coordinator (2018-2022), ensuring institutional excellence. Additionally, she has served as the Institution Innovation Cell (IIC) President, fostering entrepreneurship and innovation. Her industry collaborations with IBM, Rently, and L&T EDUTECH have enriched student learning experiences. She has contributed as a Board of Studies member in multiple colleges and played an active role in doctoral committees and inspection commissions under Bharathiar University.

🏅 Awards and Recognition

Dr. N. Tajunisha’s contributions to academia have been widely recognized through numerous awards and honors. She received the prestigious Senior Educator and Scholar Award from NFED in 2017 for her outstanding contributions to Computer Science education. She has also been honored with the Best Paper Award at the IEEE International Conference held at Satyabhama University in 2010. Her research excellence is reflected in her extensive publication record, including nine Scopus-indexed journal papers. Her leadership in institutional development led to Sri Ramakrishna College of Arts & Science for Women achieving an A+ grade in NAAC Cycle II under her tenure as IQAC Coordinator. Additionally, she has been an invited session chair at international conferences and serves as a reviewer for top-tier journals. Her commitment to fostering innovation and research has positioned her as a thought leader in Data Mining, Machine Learning, and Big Data Analytics.

🌍 Research Skills On Computer Science

Dr. N. Tajunisha’s research expertise spans Data Mining, Machine Learning, Big Data Analytics, and Networks. She has successfully guided three Ph.D. scholars and is currently mentoring five more, contributing to advancements in computational intelligence and predictive analytics. Her research has secured substantial funding, including a Rs. 3.17 lakh UGC Minor Research Project and Rs. 76 lakh for a DST-FIST project. With 34 journal papers, 25 conference papers, and two books to her credit, she has made significant contributions to her field. She actively collaborates with industry leaders like IBM, Rently, and L&T EDUTECH, ensuring practical applications of her research. Her ability to integrate academic knowledge with real-world solutions makes her a leading researcher in her domain. She has also been a reviewer for international journals and a committee member in doctoral research evaluations, further enhancing her impact in the field.

Publication Top Notes

Performance analysis of k-means with different initialization methods for high-dimensional data
Author(s): VSN Tajunisha
Journal: International Journal of Artificial Intelligence and Applications
Citations: 35
Year: 2010
An efficient method to improve the clustering performance for high dimensional data by principal component analysis and modified K-means
Author(s): N Tajunisha, V Saravanan
Journal: International Journal of Database Management Systems
Citations: 20
Year: 2011
An increased performance of clustering high dimensional data using Principal Component Analysis
Author(s): N Tajunisha, V Saravanan
Conference: 2010 First International Conference on Integrated Intelligent Computing
Citations: 19
Year: 2010
A study on evolution of data analytics to big data analytics and its research scope
Author(s): S Sruthika, N Tajunisha
Conference: 2015 International Conference on Innovations in Information, Embedded and …
Citations: 14
Year: 2015
Predicting Student Performance Using MapReduce
Author(s): N Tajunisha, M Anjali
Journal: International Journal of Emerging and Computer Science
Citations: 14
Year: 2015
A new approach to improve the clustering accuracy using informative genes for unsupervised microarray data sets
Author(s): N Tajunisha, V Saravanan
Journal: International Journal of Advanced Science and Technology
Citations: 10
Year: 2011
Automatic classification of ovarian cancer types from CT images using deep semi-supervised generative learning and convolutional neural network
Author(s): N Nagarajan, P.H. Tajunisha
Journal: Revue d’Intelligence Artificielle
Citations: 9
Year: 2021
Classification of cancer datasets using artificial bee colony and deep feed-forward neural networks
Author(s): M Karunyalakshmi, N Tajunisha
Journal: International Journal of Advanced Research in Computer and Communication …
Citations: 8
Year: 2017
Concept and Term-Based Similarity Measure for Text Classification and Clustering
Author(s): B Sindhiya, N Tajunisha
Journal: IJERST
Citations: 7
Year: 2014
Optimal Parameter Selection-Based Deep Semi-Supervised Generative Learning and CNN for Ovarian Cancer Classification
Author(s): PH Nagarajan, N Tajunisha
Journal: ICTACT Journal on Soft Computing
Citations: 5
Year: 2023

Ahlem Ayari | Computer Science | Best Researcher Award

Published on 07/02/202507/02/2025 by Academic Excellence

Dr. Ahlem Ayari | Computer Science | Best Researcher Award

Dr. Ahlem Ayari, Higher Institute of Management of Sousse, Tunisia

Ahlem Ayari is a dedicated PhD student at the National Engineering School of Sousse (ENISo), specializing in cloud computing, edge computing, and high-performance computing. She has extensive experience as a lecturer and trainer in computer science, particularly in database management, object-oriented programming, and web development. With a strong foundation in Java, C, JavaScript, and other programming languages, Ahlem has contributed to various software and web-based projects. Her research focuses on distributed systems, artificial intelligence, and machine learning algorithms. She has actively participated in international conferences and academic collaborations, highlighting her expertise in deploying e-health applications on cloud and edge computing environments. Ahlem is passionate about advancing technology in healthcare and has successfully developed innovative platforms that integrate deep learning algorithms for medical data analysis. She remains committed to contributing to technological advancements and mentoring the next generation of computer scientists through her academic and research pursuits.

Professional Profile

Suitability for the Research for Best Researcher Award – Ahlem Ayari

Ahlem Ayari demonstrates a strong academic background and technical expertise, making her a promising candidate for the Research for Best Researcher Award. She is currently a PhD student at the National Engineering School of Sousse (ENISo), Tunisia, specializing in advanced computing fields such as distributed systems, high-performance computing, artificial intelligence, and machine learning. Her research contributions, particularly in cloud and edge computing for e-health applications, highlight her commitment to solving real-world challenges through innovative technology.

Her experience extends beyond academia, as she has actively contributed to teaching and training at multiple institutions, including Higher Institute of Management (ISG) and The Maghreb Institute of Economic Sciences and Technology (IMSET). She has taught courses in database management, programming, UML, MERISE, and cloud computing, demonstrating her ability to disseminate knowledge effectively. Additionally, her industry experience as a web developer strengthens her profile by bridging the gap between theoretical research and practical application.

🎓 Education

2023-2024: PhD Student in Computer Science, “Mars” Laboratory, National Engineering School of Sousse (ENISo), Sousse University. Supervisor: Prof. Mohamed Nazih Omri, Co-supervisor: Hassen Hamdi.
2021-2023: Master in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Hassen Hamdi.
2017-2020: Bachelor’s Degree in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Kamel Garrouch.
2017: Mathematics Baccalaureate, Ibn Rachik Kairouan.

Ahlem Ayari’s academic journey reflects her commitment to interdisciplinary learning, combining computer science and business computing. Her doctoral research focuses on cloud and edge computing, aiming to optimize computational efficiency and data security in medical applications. Throughout her studies, she has acquired expertise in artificial intelligence, database management, software engineering, and statistical analysis, contributing to her proficiency in designing and implementing complex computational systems.

💼 Professional Experience

Trainer, Maghreb Institute of Economic Sciences and Technology (IMSET) (2024)
- Taught courses on E-commerce, UML, MERISE, and SQL database management.
- Conducted practical training in software development and IT systems.
Assistant Master, Higher Institute of Management (ISG), Sousse (2024)
- Lectured in object-oriented programming (Java), cloud computing, big data, and C2I (Certificate in Computer Science and Internet).
Trainer, Higher Institute of Management (ISG), Sousse (2024)
- Conducted LaTeX training for CV, report, and presentation creation.
Web Developer, Access Leader (March 2020 – September 2020)
- Developed a web application using Angular for truck sales management.

🏅 Awards and Recognition

12th International Conference of ISG Sousse (2023): Recognized for research on deploying e-health applications in edge and cloud computing environments.
28th International Conference on Knowledge-Based and Intelligent Information Engineering Systems, Seville, Spain (2024): Presented innovative research on IoMT-based e-health applications.
Academic Distinction: Awarded recognition for excellence in cloud computing and AI research.
Best Research Presentation Award: Acknowledged for outstanding work in high-performance computing.

🌍 Research Skills On Computer Science

Ahlem Ayari possesses advanced research skills in distributed computing, cloud and edge computing, big data analytics, and AI-driven applications. Her expertise extends to designing and implementing machine learning models for real-time data processing. She is proficient in various programming languages (Java, C, Python) and frameworks (Angular, Symfony, Node.js). Her research methodologies involve deep learning algorithms for e-health applications and optimizing computational infrastructures. Ahlem actively contributes to international conferences, presenting innovative solutions in high-performance computing.

Publication Top Notes

E-health Application In IoMT Environment Deployed in An Edge And Cloud Computing Platforms

Author: A., Ayari, Ahlem, H., Hassen, Hamdi, K.A., Alsulbi, Khlil Ahmad

Yukun Shi | Computer Science | Best Scholar Award

Published on 06/02/202506/02/2025 by Academic Excellence

Assoc. Prof. Dr. Yukun Shi | Computer Science | Best Scholar Award

Assoc. Prof. Dr. Yukun Shi, Beijing University of Chemical Technology, China

Dr. Yukun Shi is an accomplished researcher and Associate Professor at the Department of Information Science and Technology, Beijing University of Chemical Technology. He specializes in multi-agent systems, control system network attacks, and distributed estimation. Dr. Shi earned his Ph.D. in Control Science and Engineering from Beijing University of Chemical Technology in 2022. His academic journey includes a one-year research visit to the University of Victoria, Canada, in 2021. His contributions to the field are significant, particularly in advancing secure state estimation and consensus control. He has published extensively in top-tier journals, addressing challenges in network security and distributed control. With a strong background in system modeling and cybersecurity, Dr. Shi continues to drive innovations in multi-agent collaboration and resilience against malicious attacks. His research not only contributes to theoretical advancements but also has practical implications for industrial and technological applications worldwide.

Professional Profile

Suitability for the Research for Best Scholar Award – Yukun Shi

Dr. Yukun Shi, an Associate Professor at the Beijing University of Chemical Technology, has demonstrated remarkable academic and research excellence in the field of control science and engineering. His expertise spans critical areas such as multi-agent systems, control system network attacks, distributed estimation, and consensus control, making his contributions highly relevant to modern automation and cybersecurity challenges. His work is particularly notable in the area of secure state estimation, where he has investigated the robustness of networked control systems against malicious sensor attacks, an emerging concern in industrial and cyber-physical systems.

Dr. Shi’s research output includes several publications in prestigious IEEE journals, such as IEEE Transactions on Automation Science and Engineering and IEEE Transactions on Control of Network Systems, highlighting his ability to contribute cutting-edge advancements in his field. His scholarly work is well-cited, reflecting both its impact and recognition within the scientific community. Additionally, his international exposure, including a research visit at the University of Victoria, Canada, underscores his global perspective and collaborative research approach.

🎓 Education

Dr. Yukun Shi pursued his Ph.D. in Control Science and Engineering at Beijing University of Chemical Technology, graduating in 2022. His doctoral research focused on secure state estimation in multi-agent systems under adversarial conditions, bridging control theory with cybersecurity. As part of his academic development, he undertook a one-year research visit to the University of Victoria, Canada, in 2021, where he collaborated on cutting-edge projects related to network security and control systems. His education provided him with a strong foundation in distributed control, estimation algorithms, and robust filtering techniques. Throughout his studies, Dr. Shi honed his expertise in tackling cyber threats to industrial control systems, laying the groundwork for his future research in resilient multi-agent networks. His academic journey is marked by rigorous training, innovative problem-solving, and contributions to the field of control and automation engineering.

💼 Professional Experience

Dr. Yukun Shi currently serves as an Associate Professor at the Department of Information Science and Technology, Beijing University of Chemical Technology. With a research focus on multi-agent systems, network security, and distributed estimation, he has made significant contributions to securing cyber-physical systems. His professional journey includes leading research projects on sensor attacks, consensus control, and fault-tolerant filtering in distributed networks. Dr. Shi actively collaborates with international institutions to develop advanced methodologies for improving the resilience of control systems against malicious threats. His role extends beyond research, encompassing mentorship, curriculum development, and industry partnerships. He is a sought-after speaker at academic conferences and has peer-reviewed numerous articles in high-impact journals. His dedication to cybersecurity and control engineering has positioned him as a thought leader in the field, driving innovation and practical solutions to safeguard modern industrial and technological infrastructures.

🏅 Awards and Recognition

Dr. Yukun Shi has received multiple accolades for his pioneering work in control systems and cybersecurity. He has been recognized for his contributions to secure multi-agent systems and networked control security. His research papers have been published in high-impact journals, earning him best paper awards at leading automation and control conferences. Dr. Shi has also been a recipient of prestigious research grants that support his work in developing robust estimation algorithms against cyber threats. His outstanding contributions have been acknowledged by industry associations, positioning him as a key figure in distributed system security. His work has not only influenced academia but also guided practical implementations in industrial automation and cybersecurity frameworks. Additionally, Dr. Shi has served as a reviewer for top-tier journals, further highlighting his expertise and influence in the scientific community. His relentless pursuit of excellence continues to shape the future of secure control systems.

🌍 Research Skills On Computer Science

Dr. Yukun Shi possesses a robust research skill set centered around multi-agent systems, control system security, and distributed estimation. His expertise includes developing secure state estimation techniques to mitigate network attacks in cyber-physical systems. He specializes in designing fault-tolerant control algorithms that enhance the resilience of distributed networks. His research also encompasses consensus control strategies to improve synchronization in multi-agent environments. Dr. Shi is proficient in advanced filtering techniques, such as Kalman filtering and observer design, to ensure accurate system monitoring despite adversarial interference. He actively applies mathematical modeling and optimization methods to enhance decision-making in complex systems. His work in secure control frameworks has broad applications in autonomous systems, industrial automation, and networked infrastructures. With a keen focus on practical implementation, Dr. Shi’s research continues to bridge theoretical advancements with real-world security challenges, contributing to the evolution of resilient cyber-physical networks.

Publication Top Notes

Title: Optimal Output-Feedback Controller Design Using Adaptive Dynamic Programming: A Permanent Magnet Synchronous Motor Application
- Authors: Zhongyang Wang, Huiru Ye, Youqing Wang, Yukun Shi, Li Liang
- Citation: IEEE Transactions on Circuits and Systems II: Express Briefs
- Year: 2025
Title: Distributed Filter Under Homologous Sensor Attack and Its Application in GPS Meaconing Attack
- Authors: Yukun Shi, Wenjing He, Li Liang, Youqing Wang
- Citation: IEEE Transactions on Automation Science and Engineering
- Year: 2024
Title: Event-triggered distributed secure state estimation for homologous sensor attacks
- Authors: Yukun Shi, Haixin Ma, Jianyong Tuo, Youqing Wang
- Citation: ISA Transactions
- Year: 2023
Title: Distributed Secure State Estimation of Multi-Agent Systems Under Homologous Sensor Attacks
- Authors: Yukun Shi, Youqing Wang, Jianyong Tuo
- Citation: IEEE/CAA Journal of Automatica Sinica
- Year: 2023
Title: Online Secure State Estimation of Multiagent Systems Using Average Consensus
- Authors: Yukun Shi, Youqing Wang
- Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Year: 2022
Title: Asymptotically Stable Filter for MVU Estimation of States and Homologous Unknown Inputs in Heterogeneous Multiagent Systems
- Authors: Yukun Shi, Changqing Liu, Youqing Wang
- Citation: IEEE Transactions on Automation Science and Engineering
- Year: 2022
Title: Secure State Estimation of Multiagent Systems With Homologous Attacks Using Average Consensus
- Authors: Yukun Shi, Changqing Liu, Youqing Wang
- Citation: IEEE Transactions on Control of Network Systems
- Year: 2021

Lin Chen | Computer Science | Best Researcher Award

Published on 28/01/202528/01/2025 by Academic Excellence

Prof. Lin Chen | Computer Science | Best Researcher Award

Prof. Lin Chen, Macao Polytechnic University, Macau

Lin Chen, a distinguished scholar and innovator in Computer Science, serves as a full professor at Macao Polytechnic University since 2025. He obtained his B.Sc. in Electrical Engineering from Southeast University (2002), an M.Sc. in Networking from the University of Paris 6 (2005), and an Engineer Diploma and Ph.D. in Computer Science from Telecom ParisTech (2005, 2008). Dr. Chen further achieved his Habilitation thesis at the University of Paris-Sud in 2017. His illustrious career spans academia and research, including tenures as an associate professor at the University of Paris-Sud and a full professor at Sun Yat-sen University. Renowned for contributions to distributed algorithms, energy-efficient systems, and network security, he has published over 100 papers, with several receiving accolades. He is a Junior Member of the Institut Universitaire de France (IUF) and an editor for IEEE Systems Journals, demonstrating leadership in advancing the field of networked systems.

Professional Profile

Suitability for the “Research for Best Researcher Award” – Lin Chen

Lin Chen’s qualifications and achievements make him an outstanding candidate for the “Research for Best Researcher Award.” As a full professor of Computer Science at Macao Polytechnic University, his distinguished academic and research background places him among the leading experts in his field. His educational journey, which spans multiple prestigious institutions, reflects a profound commitment to advancing knowledge in Computer Science and Networking. Holding a Ph.D. in Computer Science and Networking from Telecom ParisTech (ENST), along with a Habilitation thesis from the University of Paris-Sud, Lin’s academic credentials are both comprehensive and prestigious.

Lin Chen’s research contributions, particularly in the realm of distributed algorithms and protocols for emerging networked systems, are exemplary. His work on energy efficiency, resilience, and security is highly relevant to current and future technological advancements. Having published over 100 journal and conference papers, with several highly cited works, Lin’s research is not only influential but recognized as a major contribution to the field. His three ESI Highly Cited Papers and multiple Best Paper and Best Student Paper awards underline his impact in the academic community.

🎓 Education

Lin Chen’s academic journey reflects a steadfast commitment to excellence and interdisciplinary learning. He earned his B.Sc. in Electrical Engineering from Southeast University in 2002, laying a strong foundation in technical problem-solving and system design. Driven by his passion for innovation, he pursued an M.Sc. in Networking at the University of Paris 6 in 2005, where he developed expertise in network protocols and optimization. His quest for advanced knowledge led him to Telecom ParisTech (ENST), where he received an Engineer Diploma and a Ph.D. in Computer Science and Networking in 2005 and 2008, respectively, focusing on distributed systems and secure network architecture. Further solidifying his credentials, he achieved his Habilitation thesis at the University of Paris-Sud in 2017, showcasing his authority in energy-efficient and resilient systems. This educational foundation underscores Dr. Chen’s expertise in bridging theoretical innovation with practical applications in emerging technologies.

💼 Professional Experience

Lin Chen boasts a remarkable professional trajectory spanning prestigious institutions and transformative research. He began his academic career as an associate professor in the Department of Computer Science at the University of Paris-Sud (2009–2019), where he spearheaded groundbreaking projects on distributed algorithms and energy-efficient systems. In 2019, he advanced to a full professorship at Sun Yat-sen University, focusing on secure and resilient networked systems, before joining Macao Polytechnic University in 2025. Dr. Chen’s leadership extends beyond academia, serving as the Chair of the IEEE TCGCC SIG on Green and Sustainable Networking. He has significantly influenced the field through editorial roles in IEEE Systems Journals and guest editing for top-tier publications. His prolific contributions include organizing international conferences, such as ICCCN and INFOCOM workshops, underscoring his commitment to fostering innovation in computer science. His work remains a testament to his dedication to advancing next-generation networking technologies.

🏅 Awards and Recognition

Lin Chen’s exceptional contributions have garnered numerous accolades, reflecting his influence in computer science and networking. He received the 2018 CNRS Bronze Medal, a prestigious recognition reserved for exemplary researchers, being one of only two awardees in ICT that year. His research has produced over 100 impactful publications, including three journal papers recognized as ESI Highly Cited Papers and three conference papers awarded Best Paper or Best Student Paper honors. Dr. Chen’s leadership and expertise have earned him the distinction of Junior Member of the Institut Universitaire de France (IUF). His editorial roles with IEEE Systems Journals and guest editorships in leading publications like IEEE Wireless Communications Magazine further underscore his contributions. As a driving force behind initiatives such as the IEEE TCGCC SIG on Green Networking, he continues to shape the future of secure, energy-efficient, and sustainable networked systems, inspiring researchers worldwide.

🌍 Research Skills On Computer Science

Lin Chen is a leading researcher specializing in distributed algorithms, energy efficiency, resilience, and network security. His work is characterized by a multidisciplinary approach, seamlessly integrating theoretical insights with practical solutions. He has developed innovative protocols for emerging networked systems, addressing critical challenges in energy efficiency and sustainable computing. Dr. Chen’s expertise extends to secure network architecture, ensuring robust communication frameworks for dynamic and large-scale systems. His research leverages advanced methodologies, including machine learning and artificial intelligence, to optimize network performance and enhance resilience. With a focus on green networking, he has pioneered strategies for reducing energy consumption in ultra-dense networks. His technical acumen is complemented by his extensive experience in project leadership and collaboration, as demonstrated by his active participation in international conferences and editorial roles. Dr. Chen’s research continues to influence the development of scalable, secure, and sustainable next-generation technologies.

Publication Top Notes

Routing metrics of cognitive radio networks: A survey
Authors: M Youssef, M Ibrahim, M Abdelatif, L Chen, AV Vasilakos
Journal: IEEE Communications Surveys & Tutorials
Citation: 388
Year: 2013
A game theoretical framework on intrusion detection in heterogeneous networks
Authors: L Chen, J Leneutre
Journal: Information Forensics and Security, IEEE Transactions on
Citation: 190
Year: 2009
An auction framework for spectrum allocation with interference constraint in cognitive radio networks
Authors: L Chen, S Iellamo, M Coupechoux, P Godlewski
Journal: INFOCOM, 2010 Proceedings IEEE
Citation: 124
Year: 2010
Joint multiuser DNN partitioning and computational resource allocation for collaborative edge intelligence
Authors: X Tang, X Chen, L Zeng, S Yu, L Chen
Journal: IEEE Internet of Things Journal
Citation: 110
Year: 2020
Energy-efficiency maximization for cooperative spectrum sensing in cognitive sensor networks
Authors: M Zheng, L Chen, W Liang, H Yu, J Wu
Journal: IEEE Transactions on Green Communications and Networking
Citation: 101
Year: 2016
A distributed demand-side management framework for the smart grid
Authors: A Barbato, A Capone, L Chen, F Martignon, S Paris
Journal: Computer Communications
Citation: 98
Year: 2015
On oblivious neighbor discovery in distributed wireless networks with directional antennas: Theoretical foundation and algorithm design
Authors: L Chen, Y Li, AV Vasilakos
Journal: IEEE/ACM Transactions on Networking
Citation: 88*
Year: 2017
Secure cooperative spectrum sensing and access against intelligent malicious behaviors
Authors: W Wang, L Chen, KG Shin, L Duan
Journal: INFOCOM, 2014 Proceedings IEEE
Citation: 86*
Year: 2014
On heterogeneous neighbor discovery in wireless sensor networks
Authors: L Chen, R Fan, K Bian, M Gerla, T Wang, X Li
Journal: 2015 IEEE Conference on Computer Communications (INFOCOM)
Citation: 80
Year: 2015
An efficient auction-based mechanism for mobile data offloading
Authors: S Paris, F Martignon, I Filippini, L Chen
Journal: IEEE Transactions on Mobile Computing
Citation: 75
Year: 2015

Phong Lam Nguyen Duy | Computer Science | Best Researcher Award

Published on 27/01/202527/01/2025 by Academic Excellence

Mr. Phong Lam Nguyen Duy | Computer Science | Best Researcher Award

👤 Mr. Phong Lam Nguyen Duy, University of Engineering and Technology – Vietnam National University, Vietnam

Phong Lam Nguyen Duy is a motivated undergraduate student in the Computer Science Department at the University of Engineering and Technology, Vietnam National University, Hanoi. Born on July 6, 2004, in Ha Dong, Hanoi, Phong Lam is passionate about exploring cutting-edge technologies in data science and artificial intelligence. His primary research interests include automated data quality assurance, machine learning algorithms, and advancements in large language models. Apart from academics, Phong Lam is actively involved in volunteering, demonstrating a commitment to fostering community development through initiatives like the ICPC Asia Pacific Championship and Hanoi Green Summer programs. A proactive learner and aspiring researcher, Phong Lam has already contributed as a university research assistant at the Intelligence Software Engineering Laboratory, where he leverages his problem-solving skills and technical expertise. Phong Lam aspires to contribute significantly to the field of Computer Science and aims to bridge gaps between theoretical concepts and real-world applications.

Professional Profile

Suitability for the “Research for Best Researcher Award”

Summary of Suitability:
Phong Lam Nguyen Duy demonstrates remarkable potential as a candidate for the “Research for Best Researcher Award.” Currently pursuing undergraduate studies in the Computer Science Department at Vietnam National University, Hanoi, Phong has already begun contributing to cutting-edge research fields, including automated data quality assurance, machine learning, and large language models. These areas are highly relevant and impactful in today’s rapidly evolving technological landscape, showcasing his alignment with contemporary research priorities.

Phong’s involvement as a university research assistant at the Intelligence Software Engineering Laboratory since February 2024 highlights his active engagement in research at an early stage of his academic career. His recent publication, “Leveraging Local and Global Relationships for Corrupted Label Detection” (2025), reflects his ability to contribute to academic discourse and address challenges in machine learning—a field critical for advancements in artificial intelligence.

🎓 Education

Phong Lam Nguyen Duy is pursuing his undergraduate degree in Computer Science at the University of Engineering and Technology, Vietnam National University, Hanoi. Since his enrollment in September 2022, he has been immersed in a rigorous academic curriculum focused on Information and Communication Technologies. The program emphasizes critical areas such as software development, data analysis, and systems design, providing him with a robust foundation in computer science. The university’s strong research culture has further fueled his interest in machine learning and automated data quality assurance. Phong Lam has actively engaged in research initiatives and academic projects, allowing him to apply his theoretical knowledge in practical contexts. The vibrant academic environment at Vietnam National University has cultivated his technical skills and problem-solving abilities, enabling him to stay at the forefront of technological advancements. He views his education as the stepping stone to a thriving career in computer science and artificial intelligence.

💼 Professional Experience

Phong Lam Nguyen Duy is currently a research assistant at the Intelligence Software Engineering Laboratory, located in Hanoi, Vietnam. Since February 2024, he has been collaborating with faculty and fellow researchers to tackle challenges in automated data quality assurance and machine learning. His work primarily involves developing methodologies that improve data accuracy and reliability while optimizing machine learning models for large-scale datasets. Phong Lam’s role includes conducting literature reviews, designing experiments, and implementing cutting-edge algorithms to solve complex problems. His contributions are instrumental in advancing projects that integrate theoretical computer science with practical applications. As a research assistant, he has honed his analytical, programming, and communication skills, fostering his growth as a budding researcher. This professional experience has not only solidified his technical expertise but also instilled a passion for lifelong learning and innovation, preparing him for future endeavors in the rapidly evolving field of artificial intelligence.

🏅 Awards and Recognition

Phong Lam Nguyen Duy has been recognized for his academic excellence, volunteer contributions, and research potential. His participation as a volunteer for the prestigious ICPC Asia Pacific Championship 2024 earned him commendations for his organizational skills and dedication to promoting computer science education. Additionally, his involvement in the Hanoi Green Summer 2023 showcased his commitment to community service, where he actively participated in environmental sustainability initiatives. Phong Lam’s academic achievements at Vietnam National University include consistent top performance in his courses, particularly in areas related to machine learning and data science. His appointment as a research assistant at the Intelligence Software Engineering Laboratory further highlights his aptitude and potential for innovation in the field. Through these accolades, Phong Lam has established himself as a well-rounded individual, excelling academically while contributing to society and pursuing impactful research in computer science.

🌍 Research Skills On Computer Science

Phong Lam Nguyen Duy possesses a strong skill set in computational research and data science. His expertise includes automated data quality assurance, where he develops methodologies to identify and correct errors in datasets, ensuring reliability for machine learning applications. Phong Lam has a keen understanding of machine learning algorithms and their optimization, with experience in designing and training models for diverse applications. His research focus also encompasses advancements in large language models, where he explores their capabilities for natural language processing tasks. As a research assistant, he has gained hands-on experience in experimental design, data preprocessing, and implementing scalable solutions. Proficient in programming languages like Python and R, Phong Lam is adept at leveraging tools such as TensorFlow and PyTorch for deep learning projects. His analytical mindset and problem-solving abilities make him an invaluable contributor to the ever-evolving landscape of artificial intelligence and computer science research.

Publication Top Notes

Title: Leveraging local and global relationships for corrupted label detection

Journal: Future Generation Computer Systems
Year: 2025

Nagalakshmi R Velmurugan | Computer Science | Best Faculty Award

Published on 09/01/202509/01/2025 by Academic Excellence

Dr. Nagalakshmi R Velmurugan | Computer Science | Best Faculty Award

Dr. Nagalakshmi R Velmurugan, SRM institute of science and Technology, India

Dr. R. Nagalakshmi, a dedicated academician and researcher in Computer Science Engineering, has over 12 years of teaching and administrative experience. She currently serves as an Associate Professor at SRM Institute of Science and Technology, Ramapuram, Chennai. Dr. Nagalakshmi completed her Ph.D. in Computer Science Engineering from Kalinga University, focusing on grid and cluster computing. She holds an M.E. and B.E. in Computer Science Engineering from Anna University, Chennai, and a Diploma in Computer Science from Sri Balaji Polytechnic College. Her career is marked by a passion for research in AI, data science, and operating systems, complemented by proficiency in Python, Power BI, and SQL. A dynamic educator, she excels in fostering innovation and creativity through seminars, webinars, and technical events. With numerous publications and impactful projects, Dr. Nagalakshmi is committed to advancing knowledge and nurturing the next generation of technology leaders.

Professional Profile