Rencheng Song

Associate Professor

Department of Biomedical Engineering

Tel: (86-551) 62901508 
Mail: rcsong@hfut.edu.cn

• 2001.9–2005.7, B.S. Computational Mathematics, College of Mathematics, Jilin University, China.

• 2005.9–2010.7, Ph.D. Computational Mathematics, Department of Mathematics, Zhejiang University, China.

• 2010.7–2012.12, Research Fellow, Department of Electrical and Computer Engineering, National University of Singapore, Singapore.

• 2013.1–2017.4, Principal Scientist, Sensor Physics, Halliburton Far East Pte Ltd, Singapore.

• 2017.5–present, Associate Professor, Department of Biomedical Engineering, Hefei University of Technology, Hefei, China.

Teaching

Medical data analysis

Introduction to Deep Learning

Medical Big Data and Artificial Intelligence

Research

Development of human-centered intelligent perception and natural human-machine interaction methods and systems. The research focuses especially on computer vision-based human vital sign monitoring, electromagnetic inverse scattering, and multi-source human-machine interaction.

Selected Papers/Patentsand Honors

1.R. Song, H. Wang, H. Xia, J. Cheng, C. Li, and X. Chen, Uncertainty quantification for deep learning- based remote photoplethysmography. IEEE Transactions on Instrumentation and Measurement, 2023. 72: p. 5027812.

2.R. Song, H. Chen, J. Cheng, C. Li, Y. Liu, and X. Chen, PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography. IEEE Journal of Biomedical and Health Informatics, 2021. 25(5): p. 1373-1384.

3.R. Song, S. Zhang, C. Li, Y. Zhang, J. Cheng, and X. Chen, Heart rate estimation from facial videos using a spatiotemporal representation with convolutional neural networks. IEEE Transactions on Instrumentation and Measurement, 2020. 69(10): p. 7411-7421.

4.R. Song, M. Li, K. Xu, C. Li, and X. Chen, Electromagnetic inverse scattering with an untrained SOM-net. IEEE Transactions on Microwave Theory and Techniques, 2022. 70(11): p. 4980-4990.

5.R. Song, Y. Huang, X. Ye, K. Xu, C. Li, and X. Chen, Learning-based inversion method for solving electromagnetic inverse scattering with mixed boundary conditions. IEEE Transactions on Antennas and Propagation, 2022. 70(8): p. 6218-6228.