• MEng: 1) Electronic Information

• MS：1)Intelligent Science and Technology

Qi Wen, who holds a doctorate from the Polytechnic University of Milan in Italy. The research fields include human-computer interaction and smart wearables. Nearly 50 papers have been published in international journals and top international conferences, with a total of 1,678 citations on Google Scholar. Received several Best Paper/nomination awards in IEEE journals or conferences, and received the IEEE SMC Andrew P. Sage Best Transactions Paper Award in 2021. Organized workshops and special sessions for multiple IEEE international academic conferences and served multiple international journals and academic organizations, including IEEE TASE, RA-L, EAAI, NCAA, TII, TCDS, ICRA, IROS, etc.

• Human-computer interaction, machine learning, artificial intelligence, robotics, smart wearables, human body data analysis, adaptive control

• Qi, Wen, Haoyu Fan, Hamid Reza Karimi, and Hang Su. An adaptive reinforcement learning-based multimodal data fusion framework for human–robot confrontation gaming. Neural Networks 164 (2023): 489-496.

• Qi, Wen, Haoyu Fan, Hamid Reza Karimi, and Hang Su. A Novel Bio-inspired Teleoperation Control of Anthropomorphic Dual Robotic Arm. ISA Transactions, 2023 in press

• Qi, Wen, and Hang Su. A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Transactions on Industrial Informatics 18, no. 10 (2022): 6663-6670.

• Qi, Wen, and Andrea Aliverti. A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity. IEEE journal of biomedical and health informatics 24, no. 8 (2019): 2199-2207.

• Qi, Wen, Salih Ertug Ovur, Zhijun Li, Aldo Marzullo, and Rong Song. Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network. IEEE Robotics and Automation Letters 6, no. 3 (2021): 6039-6045.

• Qi, Wen, Hang Su, and Andrea Aliverti. A smartphone-based adaptive recognition and real-time monitoring system for human activities. IEEE Transactions on Human-Machine Systems 50, no. 5 (2020): 414-423.