Feng Tian

Prof Feng Tian focuses his research on machine learning, with particular interest in developing advanced approaches on representation learning and data clustering. He is a prolific researcher with more than 120 papers published in peer-reviewed journals and international conferences, including top-tier venues such as International Conference on Machine Learning (ICML), International Joint Conference on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), IEEE Transactions on Cybernetics, IEEE Transactions on Visualization & Computer Graphics and ACM Transactions on Modelling & Computer Simulation. In addition to his research, Prof Tian is deeply committed to teaching and enjoys interacting with students. His teaching interests include programming, data structure, algorithms, machine learning and artificial intelligence. Before joining Duke Kunshan University (DKU), Prof Tian was a faculty member at Bournemouth University in the UK and Nanyang Technological University in Singapore. He holds a B.Eng. and Ph.D. from Xi'an Jiaotong University, China.

Current Duke Appointments & Affiliations

Professor of Data Science at Duke Kunshan University · 2021 - Present DKU Faculty

Chair of the Division of Natural and Applied Sciences of Undergraduate Program at Duke Kunshan University · 2024 - Present DKU Faculty

Recent Scholarly Works

Hypernetwork parameter optimization of QMIX based on differential evolution

Journal article Intelligent Data Analysis · September 1, 2026 Q-value Mixing (QMIX) is a widely used algorithm for multi-agent reinforcement learning. However, multi-agent environments are quite complex and have high-dimensional action and state spaces, which leads to lower exploration efficiency and sparse global re ... Full text Cite

A reinforcement learning-assisted differential evolution with population feature replay

Journal article Engineering Applications of Artificial Intelligence · June 15, 2026 As an effective global optimization method, differential evolution (DE) often faces limitations in search capability due to its differential mutation strategy and control parameters. To effectively address diverse problems, adaptively adjusting the control ... Full text Cite

MGGA: Universal Perturbations against Deepfake via Multiple Model-based Gradient-Guided Feature Layer Attack

Conference Proceedings of the 1st on Deepfake Forensics Workshop Detection Attribution Recognition and Adversarial Challenges in the Era of AI Generated Media Dff 2025 · October 27, 2025 The application of deepfake models for image editing has become increasingly popular, yet their malicious use poses significant risks. Recent studies using active defense mechanisms achieve satisfactory results while maintaining the forgery model and datas ... Full text Cite

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Education

Xi'an Jiaotong University (China) · 1997 Ph.D.