Blessing Guembe | Advanced Computing | Best Researcher Award

Dr. Blessing Guembe | Advanced Computing | Best Researcher Award

PHD at Covenant University, Nigeria

Blessing Guembe is a Research Fellow at the University of Milan, specializing in Explainable AI, Federated Learning, misinformation, privacy, and security. With a Ph.D. in Computer Science from Covenant University, his notable projects include federated learning for asthma prediction and deep learning techniques for malaria diagnosis. He has extensive experience as a senior software developer, having led significant projects at Teesoft Innovations and Reconsoft Nigeria Limited. His technical expertise spans AWS, Azure, Google Cloud, Python, Java, and DevOps, making significant contributions to data analytics and privacy-preserving technologies. Advanced Computing

Professional Profiles

Strengths

Research Contributions:

Explainable AI and Federated Learning: Blessing’s research focuses on cutting-edge areas like explainable AI, federated learning, misinformation, privacy, and security. These topics are highly relevant and impactful in today’s technological landscape.
Publications: Blessing has several notable publications, including reviews on AI-driven cyberattacks and trustworthy machine learning approaches. These publications indicate a strong research output and expertise in cybersecurity and AI.
Projects: Active participation in the KURAMi Project at the University of Milan under Prof. Giovanni Livraga showcases involvement in significant research projects. Advanced Computing

Professional Experience

Research Fellow: Current role at the University of Milan, working on privacy and data protection in social media, demonstrates a focus on real-world applications of research.
Research Assistant: Experience at Covenant University, working on federated learning for medical applications and homomorphic encryption, highlights the practical and impactful nature of Blessing’s work.
Software Development: Extensive experience as a senior software developer at Teesoft Innovations and Reconsoft Nigeria Limited, leading large-scale projects and mentoring junior developers. This indicates strong leadership and technical skills. Advanced Computing

Education

Doctor of Philosophy in Computer Science: Obtained from Covenant University in 2023, reflecting advanced knowledge and research capabilities in the field. Advanced Computing

Technical Skills

Core Competence: Proficiency in various programming languages, cloud platforms, and data science tools (AWS, Azure, Google Cloud Platform, Python, Java, Machine Learning, Deep Learning, XAI, Federated Learning, Privacy-Preserving techniques).
Interdisciplinary Skills: Ability to integrate AI, cybersecurity, and data privacy into research and development projects.

Areas for Improvement

Diversity of Publications:

While the existing publications are strong, increasing the diversity in terms of co-authors and exploring different journals and conferences could enhance visibility and impact. Advanced Computing

Collaborations:

Engaging in more international collaborations and interdisciplinary projects could broaden the scope and application of Blessing’s research.

Grants and Funding:

Securing more research grants and funding would not only support Blessing’s work but also demonstrate the ability to attract resources for innovative projects.

Community Engagement:

Increasing involvement in academic and professional communities, such as presenting at conferences, participating in workshops, and contributing to professional societies, could further establish Blessing as a thought leader in the field.

Publications

  1. Federated Bayesian Optimization XGBoost Model for Cyberattack Detection in Internet of Medical Things, Publication date: 2024.
  2. A Deep Learning Approach For Cassava Leaf Disease Diagnosis, Publication date: 2023.
  3. Explainable artificial intelligence, the fourth pillar of zero trust security, Publication date: 2022.
  4. Machine Learning Techniques for Automatic Long Text Examination in Open and Distance Learning, Publication date: 2022.
  5. The Emerging Threat of Ai-driven Cyber Attacks: A, Publication date: 2022.
  6. Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis, Publication date: 2022.
  7. Trustworthy Machine Learning Approaches for Cyberattack Detection: A Review, Publication date: 2022.
  8. A machine learning prediction of automatic text based assessment for open and distance learning: a reviewPublication date: 2021.
  9. The emerging threat of ai-driven cyber attacks: A review, Publication date: 2022.

 

Conclusion

Blessing Guembe is a strong candidate for the Best Researcher Award due to their significant contributions to explainable AI, federated learning, and cybersecurity. The combination of advanced research, impactful publications, extensive professional experience, and robust technical skills positions Blessing as an influential researcher in the field. Advanced Computing

By enhancing the diversity of publications, seeking more collaborations, securing additional grants, and engaging with the academic community, Blessing could further solidify their standing and potential for future contributions. Overall, Blessing’s profile is highly suitable for consideration for the Best Researcher Award.

 

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Linlin You | Computing | Best Researcher Award

Assoc Prof Dr. Linlin You | Computing | Best Researcher Award

PHD at the University of Pavia,  Italy

Linlin You is an Associate Professor at Sun Yat-sen University, specializing in Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. She obtained her Ph.D. from the University of Pavia and has conducted research at MIT and Singapore-MIT Alliance for Research and Technology. Linlin has authored over 70 publications and holds 21 patents. She serves as an editorial board member for prestigious journals like The Innovation and is a Senior Member of IEEE. Her contributions in advancing distributed computing and federated learning have earned her recognition, including the Smart City Technology Innovation Award, highlighting her impact in sustainable urban development and intelligent systems. computing

Professional Profiles

Academic and Professional Background

Linlin You is an Associate Professor at Sun Yat-sen University and a Research Affiliate at MIT. Formerly a senior postdoc at Singapore-MIT Alliance for Research and Technology, she earned her Ph.D. in Computer Science from the University of Pavia in 2015. Her research spans Smart Cities, Autonomous Systems, Distributed Computing, Federated Learning, and AI-driven solutions for transportation and energy. She has authored over 70 publications in top-tier journals/conferences and holds 21 patents. Linlin serves on editorial boards, including The Innovation (IF 33.2), and is an IEEE Senior Member. computing

Research and Innovations

CEO of Naftan Payesh Integrity and Corrosion Management Co., driving innovation in corrosion management and root cause failure analysis Senior Corrosion Consultant at LifeTech Engineering, advising on global energy projects Technical Manager and Chairman at Miad Tech. Co., specializing in materials characterization and corrosion solutions computing

Areas of Research

Her research focuses on systems and services, computing, and learning.

Research Focuses

Linlin You’s research focuses on Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. Her work explores the impact of urban morphology on solar capacity in three-dimensional cities, accurate modeling of photovoltaic modules using deep learning networks, and personal mobility service systems in urban areas. She has developed innovative federated learning mechanisms and contributed to understanding solar accessibility and sustainable urban development. Linlin’s research also addresses mobility sensing systems, public sentiment analysis for smart city design, and cloud computing service quality management. Her contributions span over 70 publications, numerous patents, and editorial roles in leading journals, demonstrating leadership in advancing technologies for intelligent urban environments.

Publications

  1. From cell tower location to user location: Understanding the spatial uncertainty of mobile phone network data in human mobility research, Publication date: 2024.
  2. Precise Landmark-Map for Bundle Adjustment LiDAR Odometry, Publication date: 2024.
  3. Resource-Aware Split Federated Learning for Edge Intelligence,, Publication date: 2024.
  4. SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems.Publication date: 2024.
  5. Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation, Publication date: 2023.
  6. FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning, Publication date: 2023.
  7. Efficiency-Improved Federated Learning Approaches for Time of Arrival Estimation, Publication date: 2023.
  8. Enterprise oriented software engineering education: a preliminary frameworkPublication date: 2023.
  9. Multi-source data management mechanism and platform,  Publication date: 2022.
  10. Integrated Mobility for Individuals in Smarter Cities: a Crowd-sourcing approach, Publication date: 2022.
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