Mr. Hussain Ahmad Madni | Hematology | Best Researcher Award

University of Udine | Italy

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 HUSSAIN AHMAD MADNI: A DEDICATED PH.D. SCHOLAR IN COMPUTER SCIENCE

📚 EARLY ACADEMIC PURSUITS

Hussain Ahmad Madni began his academic journey with a strong foundation in computer science and engineering. His early education emphasized critical subjects like mathematics, physics, and chemistry, which paved the way for his future in technology and research.

  • Matriculation: Board of Intermediate and Secondary Education, D.G. Khan, Pakistan (2005–2007).
  • Intermediate (F.Sc. Pre-Engineering): Board of Intermediate and Secondary Education, D.G. Khan, Pakistan (2008–2010).
  • Bachelor’s Degree: B.Sc. in Computer System Engineering, The Islamia University of Bahawalpur, Pakistan (2010–2014).

Hussain advanced his expertise through postgraduate studies at COMSATS University Islamabad, Pakistan, earning an MS in Computer Science (2017–2018) with a thesis titled Introduction Detection System Using Deep Learning. He is now pursuing a Ph.D. in Computer Science and Artificial Intelligence at the University of Udine, Italy (2022–2024), focusing on Trust in Cloud Computing.

🧪 PROFESSIONAL ENDEAVORS

Hussain‘s career trajectory demonstrates his versatility and commitment to the field. His experience spans academia, research, and software engineering:

  • Research Assistant: COMSATS University Islamabad, Pakistan (2019–2021).
  • Senior Software Engineer: SK Technologies, Islamabad, Pakistan (2018–2019).
  • Software Engineer: Integrated Dynamic Solutions (IDS), Rawalpindi, Pakistan (2015–2017).

🔬 CONTRIBUTIONS AND RESEARCH FOCUS ON HEMATOLOGY

Hussain’s research centers on cutting-edge technologies such as:

  • Distributed Machine Learning
  • Federated Learning
  • Swarm Learning
  • Cybersecurity
  • Cloud Computing Trust

His innovative studies aim to enhance the security and privacy of data and models, tackling challenges in a rapidly evolving digital world.

🌍 IMPACT AND INFLUENCE

Hussain’s work is contributing significantly to technological advancements in distributed and federated learning. His studies on Blockchain-based Swarm Learning and Fully Homomorphic Encryption have addressed critical issues like gradient leakage and client security, showcasing his influence in the field.

📈 ACADEMIC CITATIONS AND PUBLICATIONS

Hussain’s dedication is evident in his extensive list of publications in prestigious journals and conferences:

  1. Exploiting Data Diversity in Multi-Domain Federated Learning (2024).
  2. Robust Federated Learning for Heterogeneous Model and Data (2024).
  3. Blockchain-Based Swarm Learning for Gradient Leakage Mitigation (2023).
  4. Swarm-FHE: Fully Homomorphic Encryption-Based Swarm Learning for Malicious Clients (2023).

🏅 HONORS & AWARDS

  • 2024: Oxford Machine Learning Summer School, UK.
  • 2023: International Computer Vision Summer School, Italy.
  • 2021: MIUR, Italy Scholarship for Ph.D. in Computer Science.
  • 2017: PEEF Scholarship, Pakistan for MS in Computer Science.
  • 2011: Merit Scholarship, ICT R&D Fund, Pakistan for B.Sc. in Computer System Engineering.

🌐 LEGACY AND FUTURE CONTRIBUTIONS

As a visionary researcher, Hussain’s contributions lay the groundwork for secure, scalable solutions in distributed systems. His innovative ideas are poised to impact industry practices and academic discourse for years to come.

🌠 FINAL NOTE

Hussain Ahmad Madni exemplifies dedication to advancing technology and solving complex problems in machine learning, cybersecurity, and cloud computing. His achievements reflect his commitment to shaping a secure, innovative future.

📑 NOTABLE PUBLICATIONS 

Exploiting Data Diversity in Multi-Domain Federated Learning
  • Authors: Ahmad Madni, H., Muhammad Umer, R.
  • Journal: International Journal of Neural Systems
  • Year: 2023
Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients
  • Authors: Madni, H.A., Umer, R.M., Foresti, G.L.
  • Journal: International Journal of Neural Systems
  • Year: 2023
Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning
  • Authors: Madni, H.A., Umer, R.M., Foresti, G.L.
  • Journal: IEEE Access
  • Year: 2023
DAVS-NET: Dense Aggregation Vessel Segmentation Network for Retinal Vasculature Detection in Fundus Images
  • Authors: Raza, M., Naveed, K., Akram, A., Khan, M.A.U., Mui-Zzud-din
  • Journal: PLoS ONE
  • Year: 2021
Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter
  • Authors: Naveed, K., Abdullah, F., Madni, H.A., Khan, T.M., Naqvi, S.S.
  • Journal: Diagnostics
  • Year: 2021
Machine Learning: Science and Technology
  • Authors: Luca Foresti, G.
  • Journal: Machine Learning: Science and Technology
  • Year: 2024
Robust Federated Learning for Heterogeneous Model and Data
  • Authors: Madni, H.A., Umer, R.M., Foresti, G.L.
  • Journal: International Journal of Neural Systems
  • Year: 2024

 

Hussain Ahmad Madni | Hematology | Best Researcher Award

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