Le Yao | Computer Science | Best Researcher Award

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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.

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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

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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.

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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

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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.

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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

NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

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Mr. NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

👤 Mr. NIKOLAOS EPISKOPOS, IBM, Greece

Nikolaos Episkopos is an accomplished Data Scientist, Software Developer, and Data Science Consultant with over eight years of professional experience. Based in Athens, Greece, Nikolaos specializes in AI, cybersecurity, and predictive medicine, contributing to impactful EU-funded R&D projects and Open Source Software initiatives. His innovative work includes AI solutions for fraud detection, federated learning toolkits for intrusion detection, and optimizing data pipelines. Passionate about the intersection of technological advancements and societal impact, Nikolaos has played pivotal roles in enhancing banking services, securing SCADA systems, and developing blockchain-based video streaming systems. As a professional with a robust academic background and diverse technical skills, he combines creativity and precision to deliver groundbreaking solutions.

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🌟 Evaluation of Nikolaos Episkopos for the Research for Best Researcher Award

Summary of Suitability

Nikolaos Episkopos demonstrates an exceptional profile as a data scientist and software developer with significant contributions to research and development projects, particularly in the domains of artificial intelligence (AI), cybersecurity, and healthcare. With over eight years of professional experience, he has effectively bridged academia and industry, showcasing the ability to lead and innovate in complex technological domains. His work has resulted in tangible outcomes, including funding acquisitions, novel AI solutions, and impactful publications.

Nikolaos’s recent achievements at IBM and INLECOM highlight his ability to tackle real-world challenges through data-driven innovation. At IBM, he developed advanced AI models for fraud detection, contributing to financial security solutions. His role at INLECOM involved technical project management and significant contributions to EU Horizon projects, where his efforts secured funding and established strategic partnerships. These accomplishments reflect his leadership, technical expertise, and collaborative skills in advancing scientific research.

🎓 Education 

Nikolaos holds a Master’s degree in Data Science & Information Technologies from the National and Kapodistrian University of Athens, where he honed expertise in AI, data analysis, and cybersecurity. Currently pursuing an MSc in Cybersecurity at the University of West Attica, he is expanding his knowledge of threat modeling and advanced cryptographic techniques. His academic journey reflects a commitment to excellence, as he consistently excelled in designing AI models and deploying secure, scalable systems. Nikolaos’ education equips him to navigate the rapidly evolving landscape of AI and cybersecurity, combining rigorous academic training with practical, hands-on experience to solve complex technical challenges effectively.

💼  Professional Experience

Nikolaos Episkopos has held roles at leading organizations like IBM, INLECOM, and MetaMind Innovations. At IBM, he spearheaded the development of AI solutions for card fraud detection and enhanced banking services. At INLECOM, he managed Horizon projects, securing significant funding and partnerships while optimizing algorithms for AI tools. During his tenure at MetaMind Innovations, he developed Federated Learning toolkits for intrusion detection, authored academic papers, and secured SCADA systems. At Fogus Innovations, Nikolaos implemented blockchain-enabled video streaming optimization and co-authored publications on advanced AI platforms. His ability to lead technical projects, develop AI models, and foster innovation underscores his exceptional contribution to the tech industry.

🏅 Awards and Recognition 

Nikolaos has been recognized for his contributions to EU-funded Horizon projects, which have brought substantial funding and technological advancements to his organizations. He co-authored papers published in prestigious journals like IEEE Transactions on Mobile Computing and Computer Science Review, highlighting his expertise in AI and cybersecurity. Additionally, his innovative solutions in fraud detection and SCADA security have been acknowledged within the tech community. Nikolaos’ commitment to open-source projects on GitHub further demonstrates his dedication to knowledge sharing and continuous improvement. His achievements reflect a career driven by excellence and societal impact.

🌍 Research Skills On Computer Science

Nikolaos excels in designing and deploying AI-driven solutions across domains such as cybersecurity, predictive medicine, and fraud detection. His expertise encompasses Federated Learning, blockchain integration, and data analysis using tools like TensorFlow, PyTorch, and Spark. Skilled in optimizing algorithms and building scalable data pipelines, Nikolaos has delivered solutions that reduce execution time and enhance efficiency. His academic research, coupled with industry application, positions him as a thought leader in leveraging AI for societal impact.

📖 Publication Top Notes

1. A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions
  • Author(s): Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, et al.
  • Citation: Computer Science Review, 2025-05
2. To DASH, or Not to DASH? Optimal Video Bitrate Selection and Edge Network Caching in MEC-Empowered Slice-Enabled Networks
  • Author(s): Dionysis Xenakis, Nikolaos Episkopos
  • Citation: IEEE Transactions on Vehicular Technology, 2024-04
3. PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2022-10-24
4. Cache-Aware Adaptive Video Streaming in 5G networks
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2021-05-04
5. SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions
  • Author(s): Aristeidis Farao, Sakshyam Panda, Sofia Anna Menesidou, Entso Veliou, Nikolaos Episkopos, George Kalatzantonakis, Farnaz Mohammadi, Nikolaos Georgopoulos, Michael Sirivianos, Nikos Salamanos, et al.
  • Citation: Trust, Privacy and Security in Digital Business (TrustBus), 2020-09-14
6. On-device caching of popular video content on Android-powered devices
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2018-08-14

Jordi Rodeiro | Computer Science | Best Researcher Award

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Mr. Jordi Rodeiro | Computer Science | Best Researcher Award

 👤 Mr. Jordi Rodeiro, Institut de Recerca Sant Joan de Déu, Spain

Jordi Rodeiro Boliart is an accomplished International Computer Engineering and Sports Science graduate with a Master’s in Data Science and ongoing doctoral studies in Artificial Intelligence at La Salle Bonanova, Barcelona. Jordi is a dynamic professional blending a robust academic foundation with practical expertise. He is dedicated to leveraging data science and AI in health research, particularly autism prediction. With a deep passion for problem-solving and innovation, Jordi has conducted significant work in basketball analytics, biomedical data analysis, and medical imaging. His projects have included building Python tools, web applications, and dashboards that streamline decision-making. Jordi’s multilingual fluency in Catalan, Spanish, and English (C1) and his adaptability, critical thinking, and leadership skills underscore his commitment to excellence. As a mental health researcher, programming professor, and basketball coach, Jordi excels at interdisciplinary collaboration, fostering innovation, and making meaningful contributions to both academia and real-world applications.

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🌟  Summary of Suitability for the Award

Jordi Rodeiro Boliart demonstrates an exceptional combination of academic excellence, multidisciplinary expertise, and impactful research, making him a strong candidate for the Research for Best Researcher Award. His academic journey spans multiple disciplines, including International Computer Engineering, Sports Science, and Data Science, culminating in a PhD in Artificial Intelligence and Autism Prediction. His diverse background equips him with a unique perspective in integrating technology, data science, and health research to address complex societal challenges.

Jordi’s research outputs reflect significant contributions to both applied and theoretical domains. Notably, his final master’s thesis focused on analyzing basketball data to enhance coaching strategies, while his degree project in the biomedical field led to a published scientific paper.

🎓 Education

Jordi Rodeiro Boliart boasts an impressive academic journey beginning with a dual degree in International Computer Engineering (La Salle, UPC) and Sports Science (INEFC Barcelona, UB). He further honed his expertise with a Master’s in Data Science (La Salle Bonanova, Barcelona), culminating in award-winning academic recognition. Currently pursuing a Ph.D. in Artificial Intelligence with a focus on autism prediction, Jordi demonstrates a commitment to cutting-edge research. His educational highlights include a final master’s thesis analyzing basketball data to enhance coaching strategies and a degree project in metabolomics published in a peer-reviewed journal. Jordi has also contributed to projects in medical imaging, such as using x-rays for illness detection. His academic journey is enriched by attending technology seminars at globally renowned institutions like Harvard and MIT, along with specialized training in leadership and organizational behavior. Jordi’s blend of technical and interdisciplinary studies defines his innovative, research-oriented career trajectory.

💼   Professional Experience

Jordi Rodeiro Boliart has a versatile professional background spanning research, teaching, and leadership. As a mental health researcher at Parc Sanitari Sant Joan de Déu, Jordi applies statistics and data science to critical health data, contributing to global assemblies and conferences. He serves as a university professor at La Salle Barcelona, teaching programming, mathematics, and IT software. As a data science intern at Sener, Jordi specialized in Power BI dashboards and analyzing corporate metrics. His engineering research internship included creating biomedical tools for metabolomic analysis, leading to a published paper. Jordi’s sports background complements his tech expertise, with roles as a basketball coach and coordinator, focusing on player development and team strategy. His earlier internships at Alfred Smart Systems and other engineering roles solidified his Python and gateway programming skills. Jordi’s diverse experiences exemplify his ability to integrate technology, data science, and education for impactful contributions.

🏅Awards and Recognitions

Jordi Rodeiro Boliart’s contributions have been widely recognized through various awards and honors. He received the prestigious Malaspina Award as part of the Empower consortium in 2023 and was a HackB finalist in the same year. Jordi was acknowledged with an academic excellence certificate for the best master’s record in Data Science (2023) and emerged as the LS Future Lab – Impact Challenge Hackathon winner in 2022. He represented his university as a National Model United Nations delegate in New York (2022) and participated in an international cooperation project in Perú. Jordi’s outstanding research on metabolomics earned him the opportunity to present at the Metabolomics 2022 conference. Beyond academia, Jordi is a certified Level II basketball coach, an FCBQ leadership trainee, and a master-certified Gannon Baker basketball coach. These accolades reflect his exceptional abilities in technical innovation, leadership, and interdisciplinary collaboration.

🌍  Research Skills On Computer Science

Jordi Rodeiro Boliart excels in applying advanced research methodologies to interdisciplinary challenges. His expertise includes data science, artificial intelligence, and object-oriented programming. Jordi has developed sophisticated tools for biomedical research, basketball analytics, and mental health studies. His doctoral research focuses on autism prediction through AI, combining statistical analysis and data visualization techniques. Jordi’s proficiency spans Python, MATLAB, MySQL, and Power BI, with skills in machine learning and medical image processing. He has designed Python programs to predict basketball outcomes, web apps for metabolomics, and diagnostic tools for x-rays. Jordi’s critical thinking, decision-making, and integrity define his research approach. His ability to present findings, such as at the Metabolomics 2022 conference, underscores his communication and analytical skills. Jordi’s research bridges academia and practical applications, demonstrating a commitment to addressing complex problems in health and technology.

📖 Publication Top Notes

1. The longitudinal relationship among physical activity, loneliness, and mental health in middle-aged and older adults: Results from the Edad con Salud cohort
  • Authors: Jordi Rodeiro, Beatriz Olaya, Josep Maria Haro, Aina Gabarrell-Pascuet, José Luis Ayuso-Mateos, Lea Francia, Cristina Rodríguez-Prada, Blanca Dolz-del-Castellar, Joan Domènech-Abella
  • Year: 2024
  • Citation: DOI: 10.1016/j.mhpa.2024.100667
2. The association of material deprivation with major depressive disorder and the role of loneliness and social support: A cross-sectional study
  • Authors: Joan Domènech-Abella, Carles Muntaner, Jordi Rodeiro, Aina Gabarrell-Pascuet, Josep Maria Haro, José Luis Ayuso-Mateos, Marta Miret, Beatriz Olaya
  • Year: 2024
  • Citation: DOI: 10.1016/j.jad.2024.09.071
3. Feasibility of an occupational e-mental health intervention for enhancing workplace mental health (EMPOWER RCT): Effectiveness and lessons learned (Preprint)
  • Authors: Carlota de Miquel, Christina M. Van der Feltz-Cornelis, Leona Hakkaart-van Roijen, Dorota Merecz-Kot, Marjo Sinokki, Jordi Rodeiro, Jennifer Sweetman, Kaja Staszewska, Ellen Vorstenbosch, Daniele Porricelli et al.
  • Year: 2024
  • Citation: DOI: 10.2196/preprints.66041
4. Trends of use of drugs with suggested shortages and their alternatives across 52 real-world data sources and 18 countries in Europe and North America
  • Authors: Marta Pineda-Moncusí, Alexandros Rekkas, Álvaro Martínez Pérez, Angela Leis, Carlos Lopez Gomez, Eric Fey, Erwin Bruninx, Filip Maljković, Francisco Sánchez-Sáez, Jordi Rodeiro et al.
  • Year: 2024
  • Citation: DOI: 10.1101/2024.08.28.24312695
5. CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources
  • Authors: Jordi Rodeiro, Ester Vidaña-Vila, Joan Navarro, Roger Mallol
  • Year: 2023

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Raghad K Mohammed | Computer Science | Academic Excellence Award

Posted on by sciencefather

Dr. Raghad K Mohammed | Computer Science | Academic Excellence Award

👤 Dr. Raghad K Mohammed, College of Computer Science and Information Technology, Iraq

Raghad Khaled Mohammed, born on September 29, 1978, is a dedicated academic professional specializing in Computer Networks. She serves as a Lecturer at the College of Dentistry, University of Baghdad, where she has contributed significantly to education and research since 2005. A Muslim, married, and a mother of two, Raghad has consistently balanced her personal and professional life with distinction. Her academic journey began with a Bachelor’s degree from Al-Rafidain in 2002, followed by a Master’s in Computer Networks from the University of Technology in 2005. She is currently pursuing a PhD in Computer Science and Information Technology at the University of Anbar. Her professional roles have included leadership positions, such as Head of the Planning and Quality Assurance Units, showcasing her commitment to academic excellence and institutional development.

Professional Profile

scopus

🌟 Suitability for the Research for Academic Excellence Award

Summary of Suitability
Raghad Khaled Mohammed’s extensive academic journey and professional accomplishments demonstrate her dedication to higher education and research, making her a strong candidate for the Research for Academic Excellence Award. With a career spanning nearly two decades, her contributions as a lecturer at the University of Baghdad’s College of Dentistry, along with leadership roles in quality assurance, planning, and continuing education, reflect her commitment to fostering academic and institutional excellence.

🎓  Education

Raghad Khaled Mohammed’s academic qualifications reflect her dedication to advancing knowledge in Computer Science. She earned her Bachelor’s degree in 2002 from Al-Rafidain, laying the foundation for her career. In 2005, she completed a Master’s degree in Computer Networks from the Informatics Institute for Graduate Studies, University of Technology, Baghdad. This specialization equipped her with technical expertise in designing and managing network systems. Currently, she is pursuing a PhD in Computer Science and Information Technology at the University of Anbar, demonstrating her commitment to lifelong learning and academic growth. Her academic progression highlights her passion for integrating innovative solutions and knowledge-sharing within the field of computer science, with a focus on practical applications that benefit both academia and industry.

💼  Professional Experience

Raghad Khaled Mohammed has a rich professional journey at the University of Baghdad. She started as an Assistant Lecturer in 2005, demonstrating a strong foundation in teaching and academic research. From 2006 to 2009, she led the Planning Department, showcasing her organizational and strategic planning skills. In 2010, she was promoted to Lecturer, reflecting her academic and professional growth. Between 2016 and 2018, she excelled as the Head of the Quality Assurance Unit, where she implemented initiatives to enhance educational standards. Her leadership continued in 2024 as the Head of the Continuing Education Unit, focusing on faculty and student skill development. Raghad’s multifaceted roles underline her expertise in education, administration, and her dedication to fostering an environment of continuous improvement and innovation.

🏅 Awards and Recognition

Raghad Khaled Mohammed’s career is marked by achievements and recognition in academia. Her contributions to quality assurance earned her institutional accolades during her tenure as the Head of the Quality Assurance Unit. Her innovative initiatives in the Planning Department were lauded for their impact on academic progress and administrative efficiency. As a researcher and educator, she has been acknowledged for her role in advancing the field of Computer Networks, earning respect among peers and students alike. Raghad has also been recognized for her leadership in Continuing Education, where she played a pivotal role in professional development programs. These accolades affirm her commitment to academic excellence and her ability to inspire positive change within her institution.

🌍 Research Skills On Computer Science

Raghad Khaled Mohammed possesses diverse research skills, particularly in Computer Networks and Information Technology. Her expertise includes network architecture design, security protocols, and system optimization. She is skilled in using advanced simulation tools and programming languages to develop innovative solutions for complex networking challenges. Raghad’s research focuses on bridging the gap between theoretical concepts and real-world applications, aiming to enhance efficiency and cybersecurity in digital systems. Her ability to integrate interdisciplinary approaches, coupled with her technical expertise, ensures impactful contributions to academia and industry. With ongoing doctoral studies, her research skills continue to evolve, driving advancements in Computer Science and Information Technology.

📖 Publication Top Notes

Title: U-Net for Genomic Sequencing: A Novel Approach to DNA Sequence Classification
  • Authors: Mohammed, R.K.; Alrawi, A.T.H.; Dawood, A.J.
    Year: 2024
    Journal: Alexandria Engineering Journal
    Volume and Pages: 96, pp. 323–331
    Citations: 0
Title: Optimizing Genetic Prediction: Define-by-Run DL Approach in DNA Sequencing
  • Authors: Mohammed, R.K.; Alrawi, A.T.H.; Dawood, A.J.
    Year: 2023
    Journal: Journal of Intelligent Systems
    Volume and Pages: 32(1), Article ID: 20230130
    Citations: 0
Title: Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Transfer Learning
  • Authors: Al-Saffar, R.; Mohammed, R.K.; Abed, W.M.; Hussain, O.F.
    Year: 2022
    Journal: NeuroQuantology
    Volume and Pages: 20(1), pp. 105–119
    Citations: 1