📍Current Position

Since April 2024, he has been serving as a Research Fellow at the University of Milan. In this role, she focuses on developing explainable knowledge-based solutions to address critical issues related to privacy and information security. Her research aims to empower users to manage their personal data and access reliable content while safeguarding freedom of expression. She is actively involved in the KURAMi Project under the guidance of Prof. Giovanni Livraga, working on innovative solutions in privacy protection and data security.

📝Publication Achievements 

“Trustworthy Machine Learning Approaches for Cyberattack Detection: A Review” Computational Science and Its Applications – ICCSA 2022 Workshops, doi:10.1007/978-3-031-10548-7_20. “The Emerging Threat of AI-Driven Cyber Attacks: A Review” Applied Artificial Intelligence, 36(1) doi:10.1080/08839514.2022.2037254 “Cloud Applications Management – Issues and Developments. Computational Science and Its Applications – ICCSA 2018 doi:10.1007/978-3-319-951713_54

🔍Ongoing Research 

Explainable AI: Developing methods to make AI systems more interpretable and understandable to users. Federated Learning: Enhancing privacy and security in distributed learning environments. Misinformation: Tackling the spread of false information on social media platforms. Privacy: Ensuring that users’ personal data is protected and managed effectively.

🔬Research Interests 

Explainable AI: Techniques that make machine learning models transparent and understandable. Federated Learning: Collaborative learning methods that maintain data privacy and security. Misinformation: Strategies to detect and mitigate false information in digital media. Privacy and Security: Protecting personal data and ensuring compliance with privacy regulations.

🎓Academic Background 

Doctor of Philosophy (PhD) in Computer Science Covenant University, 2023 . Master of Science (MSc) in Computer Science
Covenant University, 2019.  Bachelor of Science (BSc) in Computer Science Niger Delta University, 2012

🏆Scholarships and Awards 

Implemented Federated Learning Models: Developed and deployed federated learning models for healthcare applications. Applied Homomorphic Encryption: Secured research datasets through advanced encryption techniques. Conducted Privacy Risk Assessments: Evaluated privacy risks associated with social media platforms and proposed protective measures.

🚀Success Factors 

His success can be attributed to her dedication to research, her ability to stay abreast of technological advancements, and her collaborative approach to problem-solving..

🧪Publications & Laboratory Experience

His publications highlight her contributions to the fields of machine learning and cybersecurity. Her laboratory experience includes working on innovative projects related to data security, federated learning, and privacy-preserving techniques.