Fang Yang - Transportation Engineering - Best Researcher Award

Kunming University of Science and Technology - China

AUTHOR PROFILE

SCOPUS

EXPERT IN ELECTRIC VEHICLE CHARGING SAFETY

Fang Yang is a leading researcher in the field of electric vehicle technology, with a focus on enhancing the safety and efficiency of electric bike charging systems. His work explores innovative methods for detecting charging anomalies and promoting safe charging practices through advanced data analysis and machine learning techniques.

PROLIFIC AUTHOR IN ENGINEERING AND TRANSPORTATION

Fang has contributed significantly to academic literature with several high-impact publications. Notably, his paper on electric bike charging anomaly detection was published in Engineering Applications of Artificial Intelligence, highlighting his expertise in big data applications for transportation systems.

MAJOR PROJECT CONTRIBUTOR

Fang has played a pivotal role in various major projects, including evaluating traffic impacts and organizing traffic during the construction of Guiyang Rail Transit Line S2. His contributions extend to optimizing safety operations for new energy vehicle charging piles and researching big data public services for Kunming mobile signaling.

ADVANCING MACHINE LEARNING IN TRANSPORTATION

His research also includes leveraging machine learning to enhance the safety of electric bicycle charging systems. His work in this area has been featured in iScience, reflecting his commitment to applying cutting-edge technology to real-world transportation challenges.

RESEARCH IN URBAN RAIL TRANSIT DEMANDS

Fang's research extends to the predictability of passenger demands in urban rail transit. His study, published in Transportation, delves into short-term predictions for passenger origins and destinations, showcasing his expertise in optimizing urban transit systems.

FOCUS ON DATA-DRIVEN FORECASTING

His paper on battery swapping demands for electric bicycles, published in the Journal of Transportation Systems Engineering and Information Technology, underscores his proficiency in data-driven forecasting and its applications in improving transportation infrastructure.

DIVERSE RESEARCH EXPERIENCE

With extensive experience across multiple research projects, Fang Yang's work spans from safety analysis of new energy vehicle infrastructure to public service optimization using big data. His diverse expertise reflects a broad commitment to advancing transportation systems through innovative research.

NOTABLE PUBLICATION

Predictability of Short-Term Passengers’ Origin and Destination Demands in Urban Rail Transit.
Authors: F. Yang, C. Shuai, Q. Qian, M. He, J. Lee
Year: 2023
Journal: Transportation, 50(6), pp. 2375–2401

Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network.
Authors: C. Shuai, X. Zhang, Y. Wang, F. Yang, G. Xu
Year: 2023
Journal: IEEE Intelligent Transportation Systems Magazine, 15(4), pp. 121–136

Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping.
Authors: C. Shuai, Y. Sun, X. Zhang, X. Ouyang, Z. Chen
Year: 2023
Journal: IEEE Transactions on Industrial Electronics, 70(7), pp. 7280–7289

Promoting Charging Safety of Electric Bicycles via Machine Learning.
Authors: C. Shuai, F. Yang, W. Wang, Z. Chen, X. Ouyang
Year: 2023
Journal: iScience, 26(1), 105786

Battery Swapping Demands Forecast for Electric Bicycles Based on Data-Driven.
Authors: C.-Y. Shuai, F. Yang, X. Ouyang, G. Xu
Year: 2021
Journal: Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 21(2), pp. 173–179

EARLY ACADEMIC PURSUITS

Mohamed Almansour began his academic journey at Alexandria University, where he earned his Bachelor's degree in Computer and Systems Engineering with a thesis focused on predicting sub-populations using Single-Nucleotide Polymorphisms (SNPs). He continued his education at the University of Missouri, obtaining a Master's degree in Computer Science with a specialization in AI and Machine Learning.

PROFESSIONAL ENDEAVORS

Mohamed's professional experience spans various domains, including academia and industry. He served as an Instructor at Alexandria University, where he taught courses in programming, data structures, statistics for computing, and digital logic design. Additionally, he worked as a Graduate Fellow at the University of Missouri, conducting research in computer vision, deep learning, and medical imaging. He also gained industry experience as a Software Engineer at Bloomberg LP and Siemens Healthcare.

CONTRIBUTIONS AND RESEARCH FOCUS

Mohamed's research primarily focuses on computer vision, deep learning, and medical image analysis. He has contributed to several publications and conferences, addressing topics such as high-resolution MRI brain inpainting, transportation mode choice models, and neonatal HIE segmentation. His work demonstrates a commitment to advancing knowledge and solving real-world problems in these fields.

IMPACT AND INFLUENCE

Through his research and professional endeavors, Mohamed has made a significant impact on the academic and industrial communities. His contributions to the BONBID-HIE Lesion Segmentation Challenge, where he ranked first, highlight his expertise and influence in the field of medical imaging analysis.

LEGACY AND FUTURE CONTRIBUTIONS

As Mohamed continues his academic and professional journey, his legacy in computer science and engineering is poised to grow. His future contributions are expected to further advance the fields of computer vision, deep learning, and medical imaging analysis, addressing critical challenges and driving innovation in these domains.