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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Sajjad Ali | Computational Methods | Best Researcher Award

Dr. Sajjad Ali | Computational Methods | Best Researcher Award

PHD at Abdul Wali Khan University Mardan, Pakistan

Dr. Sajjad Ali is a Lecturer in Mathematics at Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan. He received his Ph.D. in Mathematics from Abdul Wali Khan University Mardan in 2019. With over a decade of teaching experience, Dr. Ali specializes in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods. He supervises M.Phil and Ph.D. students and is involved in departmental administration. His research interests include mathematical modeling and computational methods. Dr. Ali is proficient in MATLAB, MS Office, Excel, and LaTeX, and he is fluent in Urdu, English, Pashto, and Khohar.

Professional Profiles

Education

Ph.D. in Mathematics (2015 – 2019) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan M.Phil in Mathematics (2009 – 2012) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan Bachelor of Education (B.Ed) (2007 – 2008) Allama Iqbal Open University, Islamabad, Pakistan Master of Science in Mathematics (2005 – 2007) University of Malakand Chakdara, Khyber Pakhtunkhwa, Pakistan Bachelor of Science in Mathematics (2002 – 2004) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan F.Sc. Pre-Engineering (2000 – 2002) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan Metric in Science (1999 – 2000) Government School No.2 Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan.

Skills

Research in Mathematics Teaching and Administrative Skills Software MATLAB MS Office Excel LaTeX

Work History

Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan Lecturer in Mathematics (2011 – Present) Teaching Ph.D./M.Phil courses in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods Teaching BS courses in Partial Differential Equations and Ordinary Differential Equations Supervising M.Phil and Ph.D. research students M.Phil/Ph.D. Coordinator in the Department of Mathematics Member of the Departmental Admission Committee Warden of Ihsan Boys Hostel Tameer e Seerat Degree College, Mardan Campus, Pakistan Lecturer in Mathematics (2010 – 2011) Taught graduate-level Mathematics Hostel Warden Farabi Degree College, Peshawar, Pakistan Lecturer in Mathematics (2009 – 2010) Taught graduate-level Mathematics Government Degree College, Tangi, Charsadda, Pakistan Lecturer in Mathematics (2007 – 2009) Taught graduate-level Mathematics

Interests

Mathematical modeling, Computational methods

Awards

Top Position Holder in Master of Mathematics at University of Malakand (2007) Awardee as Lecturer in Mathematics through the Higher Education Commission Pakistan (2008)

Research Focuse

Dr. Sajjad Ali’s research focuses on the numerical treatment and computational solutions of fractional order differential equations, with applications in reaction-diffusion systems, biological population models, and ion-acoustic waves. His work includes developing iterative and stable methods for solving boundary value problems of nonlinear fractional differential equations. Dr. Ali has collaborated extensively with international researchers, contributing to journals such as Chaos, Solitons & Fractals and the Journal of Advanced Research. His studies also explore the stability analysis and exact solutions of complex mathematical models, emphasizing fractional calculus and its applications in various scientific and engineering problems.

Publications

  1. Nonlinear coupling of upper-hybrid waves with lower-hybrid waves in a degenerate dense plasma, Publication date: 2021.
  2. Unstable mode of ion-acoustic waves with two temperature q-nonextensive distributed electrons, Publication date: 2021.
  3. Computation of solution to fractional order partial reaction diffusion equations, Publication date: 2020.
  4. On stable iterative solutions for a class of boundary value problem of nonlinear fractional order differential equations, Publication date: 2019.
  5. Computation of iterative solutions along with stability analysis to a coupled system of fractional order differential equations, Publication date: 2019.
  6. Approximate solutions to nonlinear fractional order partial differential equations arising in ion-acoustic waves, Publication date: 2019.
  7. Stable monotone iterative solutions to a class of bound-ary value problems of nonlinear fractional order differential equations, Publication date: 2019.
  8. Monotone iterative technique and Ulam-Hyers stability analysis for nonlinear fractional order differential equations with integral boundary value conditions, Publication date: 2019.
  9. Optimum solutions of space fractional order diffusion equation, Publication date: 2018.
  10. On Approximate solutions of fractional Order partial differential equations, Publication date: 2018.
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