Asmaa Seyam | Data Science | Best Researcher Award

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Mrs. Asmaa Seyam | Data Science | Best Researcher Award

Ph.D student, University of Wollongong, Australia

Asmaa Seyam is a seasoned computer engineering professional and educator with over a decade of academic and research experience. Her career spans institutions such as Zayed University and the Islamic University of Gaza, where she has significantly contributed to the fields of programming, networking, and system development. Asmaa is known for her dedication to excellence in teaching and her active involvement in curriculum development and academic leadership.

Profile

Scopus

🎓 Education

Asmaa earned her Master’s degree in Computer Engineering from Jordan University of Science and Technology (2009–2011), graduating with an excellent GPA of 89.4%. Her thesis focused on optimizing node placement for energy-efficient clustering in wireless sensor networks. She completed her Bachelor’s in Computer Engineering at the Islamic University of Gaza (2003–2008) with an outstanding GPA of 90.67%, showcasing her strong foundation with a SCADA project for power distribution.

💼 Professional Experience

Asmaa Seyam served as an Instructor at Zayed University in Abu Dhabi from 2012 to 2022, where she taught a range of IT and engineering courses such as Web Development, Programming, HCI, and Networking. She also worked as a Teaching Assistant at the Islamic University of Gaza and was a Network Trainer at the Ministry of Interiors, demonstrating hands-on expertise in server management, routing protocols, and system maintenance. Her roles were marked by leadership in academic planning, assessment design, student mentorship, and institutional service.

🔬 Research Interest

Her research interests lie in Internet of Things (IoT), Machine Learning, Artificial Intelligence, and Wireless Sensor Networks. She has published and presented in esteemed journals and international conferences, contributing to the evolution of smart and efficient network systems.

🏆 Awards and Honors

Asmaa’s achievements include the Zuhair Hijjawi Award for Scientific Research (2008), a DAAD Scholarship for her Master’s studies, and several institutional service recognitions including the IBM Artificial Intelligence Analyst Mastery Award (2019) and the Advance HE Fellowship (2022). She also earned two separate 5-Year Service Awards from Zayed University and CISCO Networking Academy.

📚 Publications

  1. Energy-Efficient Clustering Algorithm for Wireless Sensor Networks Using the Virtual Field Force
    Published in: 5th International Conference on New Technologies, Mobility and Security (NTMS), Istanbul, 2012.
    Cited by: 60+ articles
    📌 IEEE Xplore

  2. Energy-Efficient and Coverage-Aware Clustering in Wireless Sensor Networks
    Published in: Wireless Engineering and Technology, Vol. 3, No. 3, 2012, pp. 142–151.
    Cited by: 90+ articles
    📌 Scientific Research Publishing

  3. Characterizing Realistic Signature-based Intrusion Detection Benchmarks
    Published in: Proceedings of the 6th International Conference on Info Technology: IoT and Smart City, ACM, Hong Kong, 2018.
    Cited by: 20+ articles
    📌 ACM Digital Library

🏁 Conclusion

Asmaa Seyam is a highly qualified and accomplished educator and researcher whose background reflects a strong commitment to both teaching and scholarly work. Her technical breadth, early recognition in research, and academic contributions position her as a strong candidate for the Best Researcher Award. Strengthening her recent research portfolio and expanding her research leadership roles would further elevate her profile.

Hyeryung Jang | Machine Learning | Best Researcher Award

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Assist. Prof. Dr Hyeryung Jang | Machine Learning | Best Researcher Award

Assistant Professor, Dongguk University, South Korea 🧑‍🏫

Hyeryung Jang is an Assistant Professor at the Division of AI Software Convergence at Dongguk University, Seoul, South Korea. His research interests lie at the intersection of communication systems, probabilistic graphical models, and networked machine learning. He has contributed significantly to the development of algorithms for large-scale communication networks, with applications in healthcare, manufacturing, and beyond. He has held academic and research positions at prestigious institutions, including King’s College London and KAIST.

Profile

Google Scholar

🎓 Education

Hyeryung Jang earned his Ph.D. in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), South Korea, from March 2012 to February 2017. His doctoral thesis, titled Optimization and Learning of Graphical Models: A Stochastic Approximation Approach, was supervised by Prof. Yung Yi and co-advised by Prof. Jinwoo Shin. He also holds a Master’s degree in Electrical Engineering from KAIST, completed between March 2010 and February 2012, with a thesis on the Economic Benefits of ISP-CDN and ISP-ISP Cooperation, under the guidance of Prof. Yung Yi. Hyeryung Jang completed his Bachelor’s degree in Electrical Engineering at KAIST in February 2010.

💼 Experience

Hyeryung Jang currently serves as an Assistant Professor in the Division of AI Software Convergence at Dongguk University, where he has been leading the Intelligence and Optimization in Networks (ION) lab since March 2021. Before this, he was a Research Associate at King’s College London, in the Centre for Telecommunications Research, Department of Engineering, from March 2018 to February 2021. His post-doctoral research was conducted at KAIST from March 2017 to February 2018. Hyeryung also gained valuable experience as a Research Intern at Los Alamos National Laboratory in the USA during the summer of 2015.

🔬 Research Interests

Hyeryung Jang’s research interests are centered on mathematical modeling and communication systems, with a particular emphasis on networked machine learning. He explores innovative learning algorithms for probabilistic graphical models, deep learning, and reinforcement learning. His work aims to improve the stability and representation quality of generative models such as GANs, VAEs, and diffusion models. Jang is also focused on the learning and inference of graphical models, specifically for applications like robust recommendation systems and communication-efficient algorithms. Moreover, his research delves into efficient learning methods to address noisy data and real-world challenges in fields like healthcare, highlighting his broad interdisciplinary approach to solving complex problems in communication networks.

🏆 Awards

Hyeryung Jang has received recognition for his groundbreaking work in networked machine learning, contributing to innovative applications in healthcare and telecommunications. His research has been published in top-tier journals such as IEEE Transactions on Communications, IEEE Transactions on Neural Networks and Learning Systems, and Journal of Medical Internet Research (JMIR).

📚 Publications Top Notes

LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks with Graph Contrastive Learning, IEEE Access, Dec. 2023.

In-Home Smartphone-based Prediction of Obstructive Sleep Apnea in Conjunction with Level 2 Home Polysomnography, JAMA Otolaryngology-Head & Neck Surgery, Nov. 2023.

Prediction of Sleep Stages via Deep Learning using Smartphone Audio Recordings in Home Environments, Journal of Medical Internet Research, June 2023.

Real-time Detection of Sleep Apnea based on Breathing Sounds and Prediction Reinforcement using Home Noises, Journal of Medical Internet Research, Feb. 2023.

Conclusion

Given his strong academic credentials, innovative contributions, and high-impact research, Hyeryung Jang is undoubtedly a strong contender for the Best Researcher Award. His work not only advances theoretical knowledge but also drives practical applications that address critical real-world challenges, particularly in communication systems and healthcare. Jang’s passion for interdisciplinary research and teaching further solidifies his suitability for this prestigious recognition.

Nordine Quadar | Digital Twin | Best Researcher Award

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Mr. Nordine Quadar | Digital Twin | Best Researcher Award

Researcher, Royal Military College of Canada, Canada

🌟 Nordine Quadar is a seasoned Technical Manager, Researcher, and Cybersecurity Expert with a proven track record in managing multidisciplinary teams and conducting cutting-edge research. Currently based in Montreal, Canada, Nordine specializes in cybersecurity for Unmanned Aerial Vehicles (UAVs) and has extensive experience in machine learning, data analysis, and software-defined radio. With over two decades of expertise spanning technical management, research, and academia, Nordine’s impactful contributions include several high-profile publications and successful projects in diverse domains such as IoT, automotive networks, and power systems.

Publication Profile

Google Scholar

Education

🎓 Nordine Quadar holds a Ph.D. in Computer Science (2022–2025) from the Royal Military College of Canada, where he focuses on enhancing UAV cybersecurity using Edge AI. He earned his Master of Applied Science in Electrical & Computer Engineering (2015–2018) and Bachelor of Applied Science in Electrical Engineering (2011–2014), both from the University of Ottawa, Canada. His research has consistently explored cutting-edge innovations, including MIMO-CDMA systems and cybersecurity for critical systems.

Experience

💼 With a career spanning roles as a Cybersecurity Researcher, Technical Manager, Data Scientist, and Electrical Engineer, Nordine has consistently excelled in leadership and innovation. At Thales Digital Identity and Security, he spearheads research on UAV cybersecurity. As a Regional Technical Manager at DC Group, he ensures operational excellence across critical power systems. His roles at Royal Military College, Xlscout, Schneider Electric, and other leading organizations underscore his technical acumen and project management prowess.

Research Interests

🔬 Nordine’s research focuses on cybersecurity, machine learning, and IoT applications, with a keen interest in unmanned aerial systems and AI-driven anomaly detection. His work delves into RF signal analysis, time-series data modeling, and edge AI to secure modern technological ecosystems. He also explores emerging trends in digital twin technology and intrusion detection systems for automotive networks.

Awards

🏆 Nordine has been recognized for his innovative contributions across diverse fields. His research has garnered international acclaim, with several of his papers accepted in prestigious journals and conferences. His dedication to advancing cybersecurity and IoT technologies exemplifies his commitment to excellence in research and industry.

Publications

  • The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review, Applied Sciences, 2024. Read Here
  • Intrusion Detection Systems in Automotive Ethernet Networks: Challenges, Opportunities, and Future Research Trends, IEEE Internet of Things Magazine, 2024. Cited by 15.
  • IoT-AI/Machine Learning Experimental Testbeds: The Missing Piece, IEEE Internet of Things Magazine, 2024. Cited by 12.
  • Recommendation Systems: Models, Techniques, Application Fields, and Ethical Challenges, 7th International Conference on Big Data and Internet of Things (BDIoT ’24), 2024. To appear.
  • Cybersecurity for Unmanned Aerial Vehicles: Concerns, Practices, and Conceptual Measures, Conference of the International Society of Military Sciences, 2023. To appear.

Sarah Di Grande | Analytics | Best Researcher Award

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Ms. Sarah Di Grande | Analytics | Best Researcher Award

PhD student, University of Catania, Italy

Sarah Di Grande is a driven researcher and data scientist currently pursuing a PhD in Systems, Energy, Computer, and Telecommunications Engineering at the University of Catania, Italy. With expertise in machine learning and a focus on sustainable water-energy optimization, she has contributed extensively to data science applications in renewable energy and smart city initiatives.

Profile

Orcid

Education 🎓

Sarah completed a Master’s in Data Science for Management at the University of Catania in 2022, graduating summa cum laude with a thesis on unsupervised machine learning for photovoltaic systems. She also holds a Bachelor’s degree in Business Economics from the same institution and graduated from Liceo Megara with top honors in 2017. Her studies have centered on advanced machine learning, big data, and data security.

Experience 💼

Currently, Sarah is a PhD student and researcher at the University of Catania, working in collaboration with Darwin Technologies on machine learning-based water-energy optimization. She previously interned as a data scientist at BaxEnergy, where she applied predictive maintenance techniques for photovoltaic panels, gaining hands-on experience in industrial data science applications.

Research Interests 🔬

Her research is dedicated to leveraging artificial intelligence for sustainable energy systems, focusing on machine learning applications in hydropower forecasting, urban traffic prediction, and water distribution network optimization. Sarah’s work aims to enhance resource management and promote sustainability in smart cities.

Awards 🏆

Sarah has received recognition for her innovative contributions, winning the Start-Cup Sicilia 2023 for her work on the “Smart Knee Project,” a device aimed at diagnosing knee osteoarthritis. She also secured second place in the University of Catania’s Start-Cup competition for the same project.

Publications Top Notes📚

Sarah has contributed numerous papers to international conferences and journals, exploring AI in hydropower, water distribution, and urban traffic management. Some key publications include:

“A Proactive Approach for the Sustainable Management of Water Distribution Systems” (2023) in 12th International Conference on Data Science, Technology and Applications – DATA [cited by 10 articles].

“Detection and Prediction of Leakages in Water Distribution Networks” (2023) in DATA 2023 [cited by 7 articles]

“A Machine Learning Approach for Hydroelectric Power Forecasting” (2023) in 14th International Renewable Energy Congress – IREC [cited by 5 articles].

“Data Science for the Promotion of Sustainability in Smart Water Distribution Systems” (2024) in Communications in Computer and Information Science, Springer [cited by 12 articles].