Zakawat Liaqat
AI Researcher in Explainable and Trustworthy Machine Learning | Cybersecurity | Edge & Federated AI Systems
I am an AI researcher specializing in explainable and trustworthy machine learning, with research interests in cybersecurity, edge AI, federated learning, and intelligent healthcare systems. My work focuses on developing secure, transparent, and privacy-preserving AI solutions for real-world and low-resource environments by integrating explainable AI, anomaly detection, and distributed intelligent systems. I have contributed to Elsevier, IEEE, and Springer-published research in areas including privacy-preserving federated fine-tuning of large language models, Edge-AI for healthcare, explainable financial anomaly detection, and blockchain-enabled federated learning for cyber threat detection. I am particularly interested in advancing trustworthy AI systems that combine innovation, security, and human-centered decision-making, and I am actively seeking PhD and research collaboration opportunities in Europe.
About Zakawat Liaqat
I am an AI researcher and computer science professional with a strong interest in explainable and trustworthy machine learning, cybersecurity, edge AI, and federated intelligent systems. My research focuses on developing secure, transparent, and privacy-preserving AI solutions for real-world applications, particularly in healthcare, financial systems, and critical digital infrastructure. I have contributed to Elsevier, IEEE and Springer-published research in areas including Edge-AI for remote healthcare, SHAP-based explainable anomaly detection, and blockchain-enabled federated learning for cyber threat detection in smart healthcare environments. Alongside my research activities, I also possess practical experience in full-stack development, digital transformation, and scalable software engineering, allowing me to bridge advanced AI research with real-world technological implementation. My long-term goal is to contribute to the advancement of trustworthy and human-centered AI systems through impactful research, innovation, and international academic collaboration.
Research Interests & Academic Expertise
I’m Zakawat Liaqat, an AI researcher and computer science professional specializing in Explainable AI, Trustworthy Machine Learning, Cybersecurity, Edge AI, and Federated Intelligent Systems. My research focuses on developing secure, transparent, and privacy-preserving AI solutions for healthcare, financial systems, and critical digital infrastructure. I aim to bridge advanced AI research with practical real-world implementation through innovative and human-centered intelligent systems.
Advancing trustworthy and human-centered AI research for secure and intelligent real-world systems.
Research Experience in Explainable AI, Cybersecurity, and Distributed Intelligent Systems
Focused on developing trustworthy and scalable AI frameworks for anomaly detection, intelligent healthcare, privacy-preserving learning, and secure digital environments.
Contributed to Elsevier, IEEE and Springer-published research in Edge-AI, Explainable Machine Learning, Federated Learning, and Cyber Threat Detection with a strong focus on real-world impact and interdisciplinary innovation.
Zakawat Liaqat | Research & Academic Expertise
My research focuses on Explainable Artificial Intelligence (XAI), Trustworthy Machine Learning, Cybersecurity, Edge AI, and Federated Intelligent Systems. I am particularly interested in developing secure, transparent, and privacy-preserving AI solutions for healthcare, financial technologies, and critical digital infrastructure. My work combines machine learning, distributed intelligence, anomaly detection, and cybersecurity to build scalable and human-centered intelligent systems with real-world impact. Through Elsevier, IEEE and Springer-published research, I aim to contribute to the advancement of trustworthy AI technologies that bridge innovation, security, and practical implementation.
Explainable & Trustworthy AI
Developing transparent and interpretable machine learning systems that improve reliability, fairness, and trust in AI-driven decision-making.
Cybersecurity & Intelligent Threat Detection
Researching AI-powered cybersecurity frameworks, anomaly detection models, and privacy-preserving systems for secure digital environments.
Edge & Federated AI Systems
Designing distributed intelligent systems using Edge AI and Federated Learning for scalable, secure, and low-resource real-world applications.
Research Methodologies & Technical Expertise
Work Experience | Zakawat Liaqat
My professional experience combines software development, IT operations, digital transformation, and AI-driven system development. I have worked on scalable applications, IT service optimization, process improvement, and intelligent technology solutions while integrating modern AI and cybersecurity concepts into practical real-world environments.
Teaching computer science subjects while conducting research in Artificial Intelligence, Explainable AI, cybersecurity, and intelligent systems. Supervising technical projects and contributing to academic research and innovation.
Led IT service operations using ITIL-based processes, including Change, Incident, and Service Level Management. Improved service performance, managed stakeholder expectations, and ensured smooth service transition from development to operations within complex environments.
Developed and maintained secure, scalable applications while supporting IT operations and system reliability. Coordinated with technical teams to ensure service continuity, process compliance, and efficient system performance.
Designed and implemented automation solutions to streamline IT service workflows, reduce manual effort, and improve operational efficiency across systems and processes.
Monitored system performance, identified service bottlenecks, and implemented improvements to enhance reliability, security, and overall service quality.
Research Vision
My research vision is centered on developing trustworthy, explainable, and privacy-preserving AI systems that address real-world challenges in healthcare, cybersecurity, and intelligent digital infrastructure. I aim to contribute to human-centered AI research that combines innovation, transparency, and security for future intelligent systems.
Research Interests
My research vision is centered on developing trustworthy, explainable, and privacy-preserving AI systems that address real-world challenges in healthcare, cybersecurity, and intelligent digital infrastructure. I aim to contribute to human-centered AI research that combines innovation, transparency, and security for future intelligent systems.
Explainable AI ◆ Trustworthy ML ◆ Cybersecurity ◆ Federated Learning ◆ Edge AI ◆ Intelligent Healthcare Systems
Zakawat Liaqat | Publications
My publications focus on Explainable Artificial Intelligence (XAI), Trustworthy Machine Learning, Cybersecurity, Edge AI, and Federated Intelligent Systems, with applications in healthcare, anomaly detection, and secure digital infrastructures. My research aims to develop transparent, scalable, and privacy-preserving AI solutions that bridge advanced machine learning innovation with real-world challenges in intelligent and critical systems.
Academic Collaboration & PhD Opportunities
I am actively seeking PhD opportunities and international research collaborations in Explainable AI, Cybersecurity, Federated Learning, Edge AI, and Intelligent Healthcare Systems.