Prof. Chen Juan | Deep learning | Best Researcher Award

Prof. Chen Juan | Deep learning | Best Researcher Award

Shanghai University, China

Dr. Juan Chen is a distinguished researcher and educator in the field of big data analytics, autonomous driving, and computer vision, currently serving as a faculty member at SILC Business School, Shanghai University since 2009. With over two decades of academic and research experience, she specializes in developing cutting-edge AI models, especially for transportation and e-commerce applications. Her expertise in deep learning and intelligent transportation systems has earned her recognition in core academic journals and scientific communities.

Publication Profile

ORCID

🎓 Education Background

Dr. Chen obtained her Ph.D. in Control Science and Engineering from Tongji University, China in 2008. She previously completed her Master’s degree at the School of Automation, Xi’an Jiaotong University in 2003, and earned her Bachelor’s degree in Energy and Power Engineering from Shanghai University of Technology in 1996. Her robust academic background laid the foundation for her interdisciplinary work across AI, engineering, and data science.

🏫 Professional Experience

Dr. Chen began her academic career as a lecturer at the School of Electronic and Information Engineering, Northern University for Nationalities from 1996 to 1998 and returned to the same school from 2001 to 2002. Since 2009, she has been actively contributing to teaching and research at SILC Business School, Shanghai University. Her teaching portfolio includes essential courses such as Python Program Design, Fundamentals of Data Analysis, and Deep Learning Practice in Computer Vision, which bridge theory with real-world AI practices.

🏆 Awards and Honors

Dr. Chen has consistently published in prestigious journals indexed in SCI and ESCI, such as the International Journal of Distributed Sensor Networks, IET Intelligent Transport Systems, and Algorithms. Her research achievements, including core journal recognition by Peking University, reflect her impactful contributions to intelligent systems and optimization in traffic networks.

🔬 Research Focus

Dr. Chen’s research is centered on big data analysis applied to transportation and e-commerce, autonomous vehicle control, computer vision, and deep learning. She has developed advanced models such as graph convolutional networks and spatiotemporal LSTM to address challenges in vehicle trajectory prediction, traffic congestion, and signal optimization. Her work integrates reinforcement learning, fuzzy logic, and multi-objective optimization to improve real-world systems’ efficiency and sustainability.

🔚 Conclusion

With an unwavering commitment to advancing AI applications in intelligent transportation, Dr. Juan Chen exemplifies interdisciplinary excellence. Her blend of academic rigor, research innovation, and practical teaching continues to inspire the next generation of engineers and data scientists. 🚗💡📊

📚 Top Publications :

Urban expressway on-ramp control based on improved NSGA-Ⅱ algorithm of reinforcement learning
Journal of Shanghai University (Natural Science Edition), 2023
Cited by: Search in Google Scholar

Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal-LSTM Model
World Electric Vehicle Journal, 2024
Cited by: Search in Google Scholar

KGCN-LSTM: A graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction
IET Intelligent Transport Systems, 2023
Cited by: Search in Google Scholar

Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment
International Journal of Distributed Sensor Networks, 2022
Cited by: Search in Google Scholar

Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy NSGA
Journal of Shanghai University (Natural Science Edition), 2021
Cited by: Search in Google Scholar

Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
Algorithms, 2019
Cited by: Google Scholar

Traffic congestion prediction based on GPS trajectory data
International Journal of Distributed Sensor Networks, 2019
Cited by: Search in Google Scholar

Sara Tehsin | Deep learning | Best Researcher Award

Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A