Ammar Odeh | CyberSecurity | Best Researcher Award

Assoc. Prof. Dr. Ammar Odeh | CyberSecurity | Best Researcher Award

Director of the Training and Consultation Center, Princess Sumaya University for Technology, Jordan

Dr. Ammar M. Odeh is an Associate Professor of Cybersecurity at Princess Sumaya University for Technology (PSUT), Jordan, with extensive experience in teaching, research, and technology development. Specializing in Cybersecurity, AI, and Blockchain, he has contributed significantly to the academic and industrial landscapes. Dr. Odeh earned his Ph.D. in Computer Science and Engineering from the University of Bridgeport, USA, in 2015, receiving the Phi Kappa Phi Award for academic excellence. He is passionate about advancing technological innovation and has been instrumental in designing student training programs, particularly in Cybersecurity. With a career spanning multiple countries, he has worked at universities in Jordan, Oman, Saudi Arabia, and the USA. His research spans Unicode Steganography, Wireless Networks, and Data Security. Dr. Odeh has also collaborated with industry giants such as Huawei and Orange, contributing to the development of advanced cybersecurity systems.

Profile

Google Scholar

Strengths for the Award

  1. Academic Excellence:
    • Dr. Odeh holds a Ph.D. in Computer Science and Engineering (specializing in Cybersecurity), awarded from the University of Bridgeport, USA, in 2015.
    • He has been recognized with multiple prestigious awards, such as the Phi Kappa Phi Award (2015) for academic excellence.
  2. Research Contributions:
    • Dr. Odeh has a strong research portfolio, with significant contributions to topics like steganography, machine learning, blockchain, AI, and cybersecurity. His research work, published in reputable journals and conferences, includes over 15 cited papers and is highly interdisciplinary.
    • Notable contributions include studies on phishing detection, electronic health record security, blockchain in healthcare, and malware detection.
    • He has been cited by multiple research papers and has contributed to advancing the application of technology in the cybersecurity, healthcare, and education sectors.
  3. Teaching and Training:
    • As an Associate Professor, Dr. Odeh’s teaching interests span Programming Languages, Data Communications, Operating Systems, and Cybersecurity, indicating a broad and deep technical skill set.
    • He has also contributed significantly to curriculum design and student training, particularly in Cybersecurity and AI. His role in training and liaising with industry giants like Orange and Huawei speaks to his practical influence in the tech space.
  4. Industry Engagement:
    • His active collaboration with industry players (such as Orange and Huawei) and his leadership of the training committee underscore his ability to bridge academic and practical applications, facilitating a strong link between the university and the tech industry.
    • His expertise in blockchain and AI in real-world contexts, such as healthcare, further illustrates his ability to contribute to and shape industry innovations.
  5. Leadership in Academia:
    • Dr. Odeh has led and participated in multiple academic committees at Princess Sumaya University for Technology (PSUT), including roles in graduation project committees and quality assurance committees. This shows his involvement in shaping the academic landscape and improving institutional practices.
  6. Impact on Research Community:
    • His work in human-computer interaction, steganography, and network security has not only resulted in technical advancements but has also been widely cited, indicating the impact of his research in the academic community.

Areas for Improvement

  1. Broader Collaborative Networks:
    • Although Dr. Odeh has demonstrated significant industry engagement, a broader range of international collaborations could further enhance his research profile. Engaging with top-tier research institutions globally and fostering collaborative international research projects might help him gain more visibility and influence in the global tech community.
  2. Increased Focus on Interdisciplinary Research:
    • While his research covers important aspects of cybersecurity, interdisciplinary approaches—incorporating more fields like social sciences or ethics in AI and cybersecurity—could further elevate his work. This would help address emerging global challenges like the ethical implications of AI, privacy concerns, and digital rights in an increasingly connected world.
  3. Grant and Funding Acquisition:
    • Dr. Odeh’s research portfolio is impressive, but a focus on obtaining large-scale research funding for interdisciplinary projects, especially in AI and cybersecurity, could amplify the impact of his work and provide more opportunities for cutting-edge projects with higher visibility.
  4. Broader Publication Outreach:
    • While his research is well-published, expanding the scope to higher-impact journals or collaborating with industry journals in areas like blockchain, IoT, and advanced AI security could increase the reach and recognition of his work in broader academic and professional circles.

Education 

Dr. Ammar M. Odeh’s academic journey began with a Bachelor’s degree in Computer Science from Hashemite University, Jordan (1999-2002). He then pursued a Master of Computer Science at the University of Jordan (2004-2006), where he honed his expertise in the field. In 2015, he completed his Ph.D. in Computer Science and Engineering at the University of Bridgeport in Connecticut, USA, with a focus on Cybersecurity. His doctoral research contributed to advancements in data security, wireless network protocols, and AI applications in cybersecurity. During his time at the University of Bridgeport, Dr. Odeh was awarded the prestigious Phi Kappa Phi Award for outstanding academic achievement. This academic foundation has enabled him to establish himself as a leading researcher and educator in Computer Science, particularly in the areas of AI, Blockchain, and Cybersecurity.

Experience 

Dr. Ammar M. Odeh has a diverse teaching and research background, with over 15 years of experience across multiple institutions. He currently serves as an Associate Professor in the Department of Computer Science at Princess Sumaya University for Technology (PSUT), Jordan, where he has been since 2019. Prior to his current role, he was an Assistant Professor at PSUT and University of AlMaarefa, Saudi Arabia (2015-2019), where he taught courses on Computer Security, Data Communications, and Programming Languages. Dr. Odeh’s early career included teaching roles at Sur College of Applied Science in Oman (2009-2011) and Philadelphia University in Jordan (2006-2009). He also worked as a Graduate Assistant/Research Assistant at the University of Bridgeport, USA, where he supported cybersecurity research and developed tools for data analysis and project evaluation. His wide-ranging experience in academia has made him a respected leader in his field.

Awards and Honors 

Dr. Ammar M. Odeh has received multiple awards and honors throughout his academic and professional career. Notably, he was awarded the prestigious Phi Kappa Phi Award in 2015 for outstanding academic performance at the University of Bridgeport. Dr. Odeh also received a Full Graduate Scholarship from the Computer Science Department at the University of Bridgeport (2012-2015). His work has been recognized with various other accolades, including the UPE Award in 2014. He is an active member of several professional societies, such as the IEEE Communications Society, IEEE Computer Society, ACM, and UPE. These memberships not only validate his expertise in Computer Science but also keep him connected to the latest developments in technology. Dr. Odeh’s ability to bridge academia and industry is reflected in his collaborations with major tech companies like Huawei and Orange, making him a leader in Cybersecurity and Blockchain research.

Research Focus 

Dr. Ammar M. Odeh’s research interests span a broad range of topics in Cybersecurity, AI, and Blockchain Technology. His primary focus includes Unicode Steganography, where he develops techniques for secure communication through hidden text in digital formats. He also explores Wireless Network Security, aiming to enhance data translation security across networks. Additionally, Dr. Odeh is dedicated to Human-Computer Interaction, improving the user experience through innovative security measures. He has contributed to research on the security and privacy of electronic health records, leveraging his expertise in AI and Machine Learning for more effective solutions in healthcare. Dr. Odeh has published several papers on the detection of phishing websites and malware, and his work on blockchain applications in the healthcare sector has garnered significant attention. His interdisciplinary approach to Cybersecurity and AI positions him as a prominent researcher in these rapidly evolving fields.

Publications 

  • Security and privacy of electronic health records: Concerns and challenges 🏥🔒
  • Machine learning techniques for detection of website phishing: A review for promises and challenges 🖥️🔍
  • Analysis of blockchain in the healthcare sector: Application and issues ⛓️💡
  • Performance evaluation of AODV and DSR routing protocols in MANET networks 🌐📶
  • Steganography by multipoint Arabic letters 🅰️🔤
  • PDF malware detection based on optimizable decision trees 📄⚙️
  • Detection in adverse weather conditions for autonomous vehicles via deep learning 🚗🌧️
  • Efficient detection of phishing websites using multilayer perceptron 🌐🛡️
  • Analysis of ping of death DoS and DDoS attacks 💻💥
  • Steganography in Arabic text using Kashida variation algorithm (KVA) 🇸🇦🔐
  • Quantum key distribution by using public key algorithm (RSA) 🔑📡
  • PHIBOOST – A novel phishing detection model using Adaptive boosting approach 📧⚡
  • Steganography in text by using MS Word symbols 📝🔑
  • A lightweight double-stage scheme to identify malicious DNS over HTTPS traffic using a hybrid learning approach 🌐🛡️
  • Security and privacy of electronic health records: Concerns and challenges 🏥🔐
  • Novel steganography over HTML code 🌍🔒
  • Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection 🤖📡
  • Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs 📡🌱
  • Impact of COVID-19 pandemic on education: Moving towards e-learning paradigm 💻📚

Conclusion

Dr. Ammar M. Odeh stands out as a leading researcher in his fields of specialization, particularly Cybersecurity, AI, and Blockchain. His impressive academic record, substantial research contributions, leadership in teaching and training, and active industry engagement make him a highly deserving candidate for the Best Researcher Award.While there are areas for growth in expanding his collaborative network and pursuing interdisciplinary research, his achievements to date reflect a dedication to advancing technology and education. Dr. Odeh’s work not only contributes to the academic community but also positively impacts industries such as healthcare, cybersecurity, and education.Given his strong credentials, extensive research output, and leadership in both academia and industry, Dr. Ammar Odeh is highly suitable for the Researcher of the Year Award.

Jing Wang | Artificial Intelligence | Best Researcher Award

Dr. Jing Wang | Artificial Intelligence | Best Researcher Award

Assistant Professor, Southeast University, China

Jing Wang is an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. With a Ph.D. from Southeast University under Prof. Xin Geng, Jing has made significant strides in machine learning, focusing on multi-label learning and explainable machine learning. Jing is a recognized contributor to multiple esteemed journals and conferences, with impactful research on label distribution learning.

Publication Profile

ORCID

Strengths for the Award:

  1. Solid Academic Background: The candidate has pursued advanced degrees in Computer Science from reputable institutions, including a Ph.D. from Southeast University under the supervision of renowned professors.
  2. Focused Research Interests: The candidate’s research concentrates on machine learning, with a particular emphasis on multi-label learning and explainable machine learning—fields of significant current interest.
  3. Prolific Publication Record: The candidate has authored numerous high-quality journal and conference papers, many in well-regarded venues such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, and AAAI Conference on Artificial Intelligence.
  4. Academic Service and Leadership: The candidate has served as a lead guest editor and guest editor for special issues in reputable journals and has been a program committee member and reviewer for major conferences and journals, showcasing their commitment to advancing their field.
  5. Collaboration and Recognition: The candidate’s work involves collaboration with other established researchers, and they have published in leading journals and conferences, reflecting their recognition and influence in the research community.

Areas for Improvement:

  1. Research Impact and Application: While the candidate has published extensively, there is limited information on the real-world impact and applications of their research. Emphasizing how their work has been applied or can be applied to solve practical problems in industry or society could strengthen their profile.
  2. Awards and Honors: Although the candidate has made notable academic contributions, there is no mention of individual awards or recognitions, which could further validate their research impact and excellence.
  3. International Collaboration and Diversity of Research Areas: Expanding collaborations beyond their current network, potentially with international researchers from diverse fields, could enhance their research’s global reach and interdisciplinary impact.

 

🎓 Education

Ph.D. in Computer Science from Southeast University, China, supervised by Prof. Xin Geng. M.Sc. in Computer Science from Northeast University, China, supervised by Prof. Xingwei Wang. B.Sc. in Computer Science from Suzhou University of Science and Technology, China.

🏆 Experience

Jing Wang serves as an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. Jing actively contributes to the academic community as a guest editor for renowned journals and as a program committee (PC) member and reviewer for prestigious conferences, including AAAI, UAI, and ECML.

🔍 Research Focus

Jing Wang’s research delves into machine learning, with a particular emphasis on multi-label learning and explainable machine learning. Jing’s work is notable for pioneering approaches in label distribution learning, leveraging common and label-specific feature fusion spaces, and developing innovative methodologies for driver distraction detection and open-world few-shot learning.

🏅 Awards and Honors

Lead Guest Editor for IEEE Transactions on Consumer Electronics on “When Consumer Electronics Meet Large Models: Opportunities and Challenges.” Guest Editor for the International Journal of Machine Learning and Cybernetics on “Reliable and Interpretable Machine Learning: Theory, Methodologies, Applications, and Beyond.” Program Committee Member for AAAI-23, UAI-24, and ECML-24.Reviewer for several high-impact journals, including IEEE TNNLS, IEEE TMM, IEEE TAI, IEEE JBHI, and Medical Image Analysis (MIA).

📚 Publications Top Notes

Jing Wang has authored numerous high-impact papers in top-tier journals and conferences. Key publications include works on label distribution learning in Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems, contributing to the understanding of label-specific feature fusion and fuzzy label correlation in machine learning. Jing’s research on “Driver Distraction Detection Using Semi-supervised Lightweight Vision Transformer” has been recognized for its innovative application in Engineering Applications of Artificial Intelligence.

Jing Wang, Fu Feng, Jianhui Lv, and Xin Geng. “Residual k-Nearest Neighbors Label Distribution Learning.” Pattern Recognition (PR), 2024, in press.

Zhiyun Zhang, Jing Wang†, and Xin Geng. “Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space.” International Journal of Machine Learning and Cybernetics, 2024, in press.

Jing Wang, Zhiqiang Kou, Yuheng Jia, Jianhui Lv, and Xin Geng. “Label Distribution Learning by Exploiting Fuzzy Label Correlation.” IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024, in press.

Zhiqiang Kou, Jing Wang, Yuheng Jia, and Xin Geng.* “Inaccurate Label Distribution Learning.” IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2024, in press.

Jing Wang and Xin Geng. “Explaining the Better Generalization of Label Distribution Learning for Classification.” SCIENCE CHINA Information Sciences (SCIS), 2024, in press.

Conclusion:

The candidate demonstrates a strong research profile with a solid foundation in machine learning, a prolific publication record, and active involvement in the academic community. Their focused research in multi-label learning and explainable AI aligns well with contemporary challenges and advancements in artificial intelligence. To strengthen their candidacy for the Best Researcher Award, they could emphasize the practical impact of their research, seek additional recognitions or awards, and pursue more diverse and international collaborations. Overall, the candidate is highly suitable for the award, with a promising future in their research career.