PhD Candidate • AI Researcher

Minh K. Quan Privacy-Preserving AI

Investigating intelligent systems that learn collaboratively while protecting privacy. Specializing in Federated Learning, Quantum-Inspired Algorithms, and Privacy-Preserving Technologies.

Minh K. Quan
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About Me

Bridging cutting-edge AI research with real-world privacy protection

I'm a passionate AI researcher pursuing my PhD at Deakin University, specializing in privacy-preserving machine learning systems. My work focuses on enabling AI models to learn collaboratively across distributed environments while maintaining strict privacy guarantees.

With a background spanning from Vietnam's dynamic tech ecosystem to Australia's world-class research institutions, I bring a unique perspective to solving complex challenges in AI ethics and security.

Key Achievements

  • ARC Industrial Transformation Postgraduate Research Scholarship recipient
  • Exemplary Reviewer Award from IEEE Open Journal of Communications Society in 2023
  • Research featured in IEEE publications and international venues
  • A dedicated IEEE Communications Society reviewer of over 25 papers

Research Focus

Advancing the frontier of privacy-preserving artificial intelligence

Federated Learning

Developing distributed learning systems that enable multiple parties to collaboratively train machine learning models without sharing raw data, ensuring privacy while maintaining model performance.

Distributed ML Privacy Collaboration

Privacy-Preserving AI

Creating AI systems that maintain high performance while protecting user privacy through advanced cryptographic techniques, differential privacy, and secure multi-party computation protocols.

Cryptography Differential Privacy Ethics

Quantum-Inspired Algorithms

Leveraging quantum computing principles to enhance classical machine learning algorithms, improving optimization efficiency and enabling new approaches to complex computational problems.

Quantum Computing Optimization Innovation

Recent Publications

Contributing to the scientific community through peer-reviewed research

Year
Title
2025

Federated Learning for Cyber Physical Systems: A Comprehensive Survey

Minh K Quan, Pubudu N Pathirana, Mayuri Wijayasundara, Sujeeva Setunge, Dinh C Nguyen, Christopher G Brinton, David J Love, H Vincent Poor

IEEE Communications Surveys & Tutorials

2025

Quantum-Inspired Genetic Algorithm for Robust Source Separation in Smart City Acoustics

Minh K Quan, Mayuri Wijayasundara, Sujeeva Setunge, Pubudu N Pathirana

IEEE International Conference on Communications 2025 (ICC2025)

2025

Privacy and Fairness in Machine Learning: A Survey

Sina Shaham, Arash Hajisafi, Minh K Quan, Dinh C Nguyen, Bhaskar Krishnamachari, Charith Peris, Gabriel Ghinita, Cyrus Shahabi, Pubudu N Pathirana

IEEE Transactions on Artificial Intelligence

2025

Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments

Minh K Quan, Mayuri Wijayasundara, Sujeeva Setunge, Pubudu N Pathirana

International Conference on Computing, Networking and Communications (ICNC)

2024

Towards privacy-preserving waste classification in the Internet of Things

Minh K Quan, Dinh C Nguyen, Van-Dinh Nguyen, Mayuri Wijayasundara, Sujeeva Setunge, Pubudu N Pathirana

IEEE Internet of Things Journal

2023

HierSFL: Local differential privacy-aided split federated learning in mobile edge computing

Minh K Quan, Dinh C Nguyen, Van-Dinh Nguyen, Mayuri Wijayasundara, Sujeeva Setunge, Pubudu N Pathirana

2023 IEEE Virtual Conference on Communications (VCC)

2023

Holistic survey of privacy and fairness in machine learning

Sina Shaham, Arash Hajisafi, Minh K Quan, Dinh C Nguyen, Bhaskar Krishnamachari, Charith Peris, Gabriel Ghinita, Cyrus Shahabi, Pubudu N Pathirana

IEEE Transactions on Artificial Intelligence

Let's Collaborate

Open to research collaborations, speaking opportunities, and meaningful conversations

I'm always excited to connect with fellow researchers, industry professionals, and anyone passionate about the ethical development of AI technologies. Whether you're interested in collaboration, have questions about my research, or simply want to discuss the future of privacy-preserving AI.

Ready to Connect?

Let's discuss how we can advance privacy-preserving AI together.

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