Faisal Alshami | Machine Learning | Best Researcher Award

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Faisal Alshami | Machine Learning | Best Researcher Award

Dalian University of Technology | China

Author Profile

Google Scholar

Early Academic Pursuits

Faisal Alshami’s academic journey reflects a deep commitment to software engineering and technological innovation. He began his undergraduate studies at Sana’a University, Yemen, earning a BSc in Network Technology and Computer Security (2008–2012). His undergraduate thesis, “General Management System for Plant Protection,” showcased his early ability to integrate security and system management using ASP.NET, C#, and VPN with OSPF protocols, signaling his strong foundation in both networking and software development. Building on this groundwork, Faisal pursued a Master’s in Software Engineering at Northeastern University, China (2019–2022), where he specialized in advanced machine learning techniques. His master’s thesis, “Design and Implementation of Web API Recommendation System Based on Deep Learning,” utilized CNN, BLSTM, K-modes, and Word2Vec, demonstrating his growing expertise in AI-driven software solutions. Currently, Faisal is advancing his academic pursuits with a PhD in Software Engineering at Dalian University of Technology, China, focusing on federated learning, distributed systems, blockchain, edge computing, and graph neural networks (GNNs).

Professional Endeavors

Alongside his academic progression, Faisal has accumulated over 5 years of professional experience in the software and networking industry. His early career as a VoIP Engineer/Developer at Communication Services Company (2013–2015) allowed him to develop communication APIs and optimize large-scale systems. As a Network Manager and Systems Engineer at EliteTecs (2015–2016), he designed high-reliability networks using advanced protocols such as OSPF, EIGRP, and WiMAX, showcasing his expertise in secure and resilient infrastructures. His role as Full-Stack Developer and DevOps Lead at Almorisi Exchange Company (2016–2018) highlighted his ability to manage mission-critical systems with real-time performance and security. Here, Faisal excelled in building scalable architectures, simulation frameworks, and automated DevOps pipelines, which contributed to operational excellence.

Contributions and Research Focus

Faisal’s research is strategically positioned at the intersection of distributed systems, intelligent computing, and aerospace applications. His focus includes:

  • Federated learning and secure communication for multi-agent systems such as satellite constellations.

  • Edge computing and real-time distributed systems tailored for resource-constrained environments.

  • Robust machine learning frameworks for aerospace, automation, and high-reliability embedded systems.

  • Blockchain integration with AI to enhance security in data networks.

  • Simulation and testing methodologies to ensure fault tolerance in mission-critical software.
    This body of research reflects his ambition to address pressing challenges in space exploration, aerospace engineering, and advanced communication networks.

Impact and Influence

Faisal’s impact lies in bridging the gap between theory and applied innovation. His academic research is not confined to publications alone but extends into real-world applications in secure communications, high-availability systems, and intelligent software architectures. By combining his professional experience with cutting-edge research, Faisal has influenced the fields of network security, distributed computing, and AI-driven system optimization, making his contributions valuable to both academia and industry.

Academic Cites

His work has strong potential for academic citations due to its interdisciplinary nature—linking software engineering, AI, networking, and aerospace technologies. His focus on federated learning, blockchain, and edge computing positions his research at the forefront of emerging scholarly and industrial discussions, ensuring that his publications will attract citations in journals focusing on AI, distributed systems, cybersecurity, and aerospace software engineering.

Legacy and Future Contributions

Faisal Alshami is on a trajectory to build a lasting legacy in intelligent, secure, and scalable software engineering systems. His research is particularly impactful in aerospace applications and secure communications, areas that are becoming increasingly vital in a digital and space-driven era. As he progresses with his doctoral research, Faisal is expected to contribute significantly to the development of resilient federated learning frameworks, advanced distributed architectures, and mission-critical simulations. His blend of academic depth and industry experience ensures that his future work will leave a lasting influence on next-generation computing systems and aerospace engineering technologies.

Other Notable Highlights

  • Certifications: Faisal holds multiple certifications, including Neural Networks & Deep Learning (DeepLearning.AI), CCNP, CCNA, and advanced language certifications (Chinese HSK4, English YALI).

  • Training: He gained practical exposure at NEUSOFT Project Training, where he contributed to developing the Borrow-Seller System (BSS) using Java, Spring Boot, Vue.js, and Android Studio.

  • Core Competencies: His expertise spans software architecture, DevOps, distributed systems, full-stack development, secure networking, and agile collaboration.

Conclusion

In conclusion, Faisal Alshami is an emerging leader in the domain of software engineering, distributed systems, and intelligent computing. His academic journey, professional experiences, and research pursuits demonstrate a rare combination of technical mastery, innovation, and practical problem-solving skills. With his ongoing doctoral work and focus on future technologies such as federated learning, blockchain, and aerospace applications, Faisal is poised to make significant contributions that will influence both academia and industry for years to come.

Notable Publications

"A detailed analysis of benchmark datasets for network intrusion detection system" 

  • Author: M Ghurab, G Gaphari, F Alshami, R Alshamy, S Othman
  • Journal: Asian Journal of Research in Computer Science
  • Year: 2021

"Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm" 

  • Author: R Alshamy, M Ghurab, S Othman, F Alshami
  • Journal: International Conference on Advances in Cyber Security
  • Year: 2021

 

 

Steven Weidong Su| Distributed system| Top Researcher Award

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Prof. Steven Weidong Su | Distributed system | Top Researcher Award

Professor, Shandong First Medical University,China📝

Professor Steven Su is a leading researcher and educator in the fields of medical AI, robotics, and systems control. With a Ph.D. from ANU, he has held significant academic and leadership roles at institutions like Shandong First Medical University and UTS. He is recognized for his work on AI Exoskeletons for stroke rehabilitation and the development of novel medical sensors. As a senior member of IEEE and an editor for several prestigious journals, he continues to shape the future of engineering and medical technologies through his teaching, research, and professional contributions.

Profile

Google Scholar

Education 🎓

Professor Steven Su holds a Ph.D. in Statistical Optimisation and its Application in Modelling and Control from the prestigious Australian National University (ANU). He completed both his Master of Engineering in Automation Engineering and Bachelor of Engineering in Automation at the Harbin Institute of Technology in China. His advanced education in engineering and optimisation theory has laid the foundation for his distinguished career in research and teaching, where he has made significant contributions to fields such as robotics, medical AI, and systems control.

Professional Experience 💼

Professor Steven Su has over 15 years of experience in academia, with a strong focus on teaching, research, and leadership in the fields of engineering and medical AI. He currently serves as the Associate Dean (Teaching and Education) at the College of Medical Artificial Intelligence and Big Data at Shandong First Medical University, overseeing academic programs and research leadership for 3,000 students and 130 faculty members. Previously, he was a Senior Lecturer and Associate Professor at the University of Technology Sydney (UTS), where he directed postgraduate programs and developed cutting-edge projects, including AI-powered exoskeletons for stroke rehabilitation. His leadership extends to roles such as Chief Technical Officer at Sydney Robotics Academy, guiding robotics competitions and innovation. With a background in process modeling and control, he has held roles at the University of New South Wales and contributed significantly to medical and industrial research projects.

Research Interests 🔬

Professor Steven Su’s research focuses on the integration of advanced engineering principles with medical applications, particularly in wearable and portable monitoring devices, robotic modeling and control, and machine learning. His work explores the potential of smart sensing and the Internet of Things (IoT) to develop innovative solutions for healthcare, including rehabilitation robotics, AI-powered medical devices, and systems for personalized health monitoring. He is particularly interested in the modeling and control of systems used in wearable technologies, with a strong emphasis on medical AI and its application in rehabilitation robotics. Additionally, his research delves into biomedical signal processing and the development of intelligent systems for clinical environments. Professor Su has contributed to the advancement of AI-exoskeletons and other assistive technologies aimed at improving patient care, particularly for stroke survivors. His research bridges the gap between engineering, medical science, and technology, with a focus on improving healthcare outcomes.

Author Metrics 🏆

  • Publications: Professor Su has authored numerous papers in journals like Int. J. Circuit Theory & Applications, International Journal of Advanced Robotic Systems, and Applied Soft Computing.
  • Citations: His work on robotics, AI, and medical technologies has been cited widely within the research community.
  • Editorial Roles: Associate Editor for International Journal of Advanced Robotic Systems (2017-2020), Editorial Member for Applied Soft Computing (2014-2017), and Editor for Int. J. Circuit Theory & Applications (2021-present).

Publications Top Notes 📚

  1. Backstepping control of electro-hydraulic system based on extended-state-observer with plant dynamics largely unknown
    • Authors: Q Guo, Y Zhang, BG Celler, SW Su
    • Journal: IEEE Transactions on Industrial Electronics
    • Volume: 63, Issue: 11
    • Pages: 6909-6920
    • Year: 2016
    • Citation Count: 237
  2. Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine
    • Authors: H Candra, M Yuwono, R Chai, A Handojoseno, I Elamvazuthi, HT Nguyen, …
    • Conference: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
    • Year: 2015
    • Citation Count: 179
  3. Nonlinear modeling and control of human heart rate response during exercise with various work load intensities
    • Authors: TM Cheng, AV Savkin, BG Celler, SW Su, L Wang
    • Journal: IEEE Transactions on Biomedical Engineering
    • Volume: 55, Issue: 11
    • Pages: 2499-2508
    • Year: 2008
    • Citation Count: 179
  4. Identification and control for heart rate regulation during treadmill exercise
    • Authors: SW Su, L Wang, BG Celler, AV Savkin, Y Guo
    • Journal: IEEE Transactions on Biomedical Engineering
    • Volume: 54, Issue: 7
    • Pages: 1238-1246
    • Year: 2007
    • Citation Count: 167
  5. Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint
    • Authors: Q Guo, Y Zhang, BG Celler, SW Su
    • Journal: IEEE Transactions on Neural Networks and Learning Systems
    • Volume: 30, Issue: 12
    • Pages: 3572-3583
    • Year: 2018
    • Citation Count: 152
  6. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems
    • Authors: M Yuwono, BD Moulton, SW Su, BG Celler, HT Nguyen
    • Journal: Biomedical Engineering Online
    • Volume: 11, Article Number: 1-11
    • Year: 2012 (Note: This seems to be a 2012 publication based on the context of other similar works)
    • Citation Count: 109

Conclusion

Professor Steven Weidong Su’s qualifications, leadership, and contributions to medical AI and robotics make him a strong contender for the Top Researcher Award. His pioneering work in integrating engineering with healthcare technologies—particularly in stroke rehabilitation and wearable health devices—has the potential to transform healthcare systems globally. With his track record of high-impact publications, leadership roles in academia, and dedication to improving patient outcomes through technological advancements, he has demonstrated exceptional academic and professional achievements.