Assoc Prof Dr. Yifan Shen, Liaoning Technical University, China
Yifan Shen is an Associate Professor at the School of Surveying and Geo-Informatics, Liaoning Technical University. He earned his Ph.D. in Surveying Science and Technology from Liaoning Technical University in June 2022, where he also completed his M.Sc. and B.Sc. degrees in the same field. Since August 2022, he has been serving as a Postdoctoral Researcher at Liaoning Technical University. His research focuses on groundwater storage anomalies, terrestrial water storage, and machine learning applications in environmental monitoring. Shen has published extensively in journals such as Remote Sensing and IEEE Access, and holds several patents related to water storage anomaly analysis and deep learning techniques. He has received notable awards, including the First Prize in Innovation and Entrepreneurship from the China Invention Association and the Second Prize in Innovation and Development from the China Productivity Promotion Center Association, both in 2022.
Professional Profile:
Summary of Suitability for Best Scholar Award:
Yifan Shen’s academic journey and research achievements position him as an exemplary candidate for the Best Scholar Award. With a solid educational foundation from Liaoning Technical University, including a Ph.D. in Surveying Science and Technology, Shen has demonstrated exceptional scholarly rigor and innovation throughout his career.
Education:
- Ph.D. in Surveying Science and Technology, Liaoning Technical University, September 2019 – June 2022
- M.Sc. in Surveying Science and Technology, Liaoning Technical University, September 2016 – July 2019
- B.Sc. in Surveying Engineering, Liaoning Technical University, September 2012 – July 2016
Work Experience:
- Associate Professor, School of Surveying and Geo-Informatics, Liaoning Technical University, August 2022 – Present
- Postdoctoral Researcher, Liaoning Technical University, August 2022 – Present
Publication top Notes:
Improving the Accuracy of Groundwater Storage Estimates Based on Groundwater Weighted Fusion Model
Improving the SSH Retrieval Precision of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural Network Feature Optimization Method
Inverted Algorithm of Groundwater Storage Anomalies by Combining the GNSS, GRACE/GRACE-FO, and GLDAS: A Case Study in the North China Plain
Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China
Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE