Assoc. Prof. Dr. Shibo Li | Distributed Ledger Technology | Best Researcher Award

Assoc. Prof. Dr. Shibo Li, Yangzhou University, China

Assoc. Prof. Dr. Shibo Li is a seasoned academic and researcher in the field of electrical engineering, with over three decades of experience in power systems and energy efficiency. He currently serves as the Deputy Director of the Department of Electrical Engineering at Yangzhou University. With a strong foundation in both industry and academia, Dr. Li has led and contributed to numerous national and provincial research initiatives, including the prestigious National 863 Program. He has authored more than 20 scientific papers, holds 4 national patents, and published a monograph. His work bridges smart substations, energy-efficient systems, and distributed ledger technology. Dedicated to both innovation and education, he plays a pivotal role in shaping China’s sustainable energy future. ⚡📘🔬

🌍 Professional Profile

Orcid

🏆 Suitability for Best Researcher Award

Assoc. Prof. Dr. Shibo Li is an exemplary candidate for the Best Researcher Award. His outstanding contributions to power system optimization, energy conservation, and smart substations have significantly advanced both academic research and real-world applications. With over 20 publications, 4 national invention patents, and leadership in critical government-funded projects, he has demonstrated consistent research excellence. His recent exploration into Distributed Ledger Technology (DLT) in energy systems showcases his forward-thinking approach and adaptability to emerging tech. As a recognized evaluator for provincial strategic projects and a mentor to graduate students, Dr. Li exhibits leadership, impact, and academic distinction, making him a highly suitable recipient of this prestigious recognition. 🏅💡🔍

🎓 Education

Dr. Shibo Li’s academic journey reflects a progressive specialization in electrical engineering. He earned his Bachelor’s degree in Power System and Its Automation from Northeast Electric Power Institute in June 1992. He further deepened his knowledge with a Master’s degree in Circuits and Systems from Jilin University in June 2002. Most recently, in September 2024, he completed his Ph.D. in Power System and Its Automation from Hohai University. His comprehensive academic training across three top-tier institutions has provided a strong foundation in power systems, control technologies, and applied research—laying the groundwork for his impactful contributions to energy conservation and intelligent infrastructure. 🎓📚⚙️

💼 Experience

Dr. Li’s career began in June 1992 at Jilin City Electric Power Design and Research Institute, where he applied practical knowledge to real-world energy infrastructure challenges until 1999. He then transitioned to the Jilin City Urban Development and Management Office, contributing to municipal energy planning. Since April 2004, he has been a pillar at Yangzhou University, where he teaches, mentors, and conducts research in advanced power systems. Currently serving as Deputy Director of the Electrical Engineering Department, he also evaluates strategic industry projects and serves in advisory roles. This blend of academic, government, and engineering experience equips him with a multifaceted perspective in energy technology. 🏢🧑‍🏫🔧

🏅 Awards and Honors

Assoc. Prof. Dr. Shibo Li has earned recognition for his deep involvement in national and regional research initiatives. His work has been backed by major funding bodies such as the National 863 Program, Jiangsu Province’s Policy Guidance Plan, and the Green Development Research Fund of the Ministry of Education. His invention of intelligent energy-saving transformers and systems has led to 4 national patents. He also plays a strategic role in evaluating key projects in strategic emerging industries in Jiangsu Province. Within Yangzhou University, he is a senior reviewer for electrical engineering research and a respected member of the Yangzhou Electrotechnical Society. 🏅📑⚡

🔬 Research Focus

Dr. Li’s research spans power distribution planning, energy efficiency, and most recently, Distributed Ledger Technology (DLT) for smart grid applications. His work includes the evaluation of smart substations, design of intelligent transformers, and urban power utilization optimization. His focus on integrating blockchain/DLT into power systems aims to enhance data transparency, improve energy transaction security, and optimize distributed energy resource (DER) management. By merging traditional power systems with digital transformation, he contributes to the development of intelligent, decentralized, and green energy ecosystems. His research not only addresses current challenges in energy systems but also aligns with future trends in smart cities and sustainable infrastructure. 🔌🔗🌱📉

📊 Publication Top Note

Research on Optimal Allocation of Renewable Energy and Energy Storage in Large‐Scale Parks Considering Grid‐Connected Fluctuations

Dr. Teng Huang | Blockchain | Best Researcher Award

Dr. Teng Huang, Guangzhou University, China

Dr. Teng Huang is a distinguished researcher at Guangzhou University, China, specializing in Blockchain, Smart Contracts, and Medical Image Analysis. His contributions span diverse areas, including Comprehensive Transformer Integration Networks (CTIN), endoscopic disease segmentation, and intelligent 3D tumor segmentation. His expertise extends to remote sensing image change detection, privacy-preserving AI, and recommender systems. Dr. Huang has authored numerous high-impact IEEE and Springer publications, advancing cutting-edge AI applications. His research focuses on developing efficient and scalable AI solutions for medical imaging, security, and remote sensing, positioning him as a leading innovator in computational intelligence.

Suitability for Best Researcher Award 🏆

Dr. Teng Huang is an exceptional candidate for the Best Researcher Award, given his groundbreaking contributions in blockchain technology, smart contracts, and AI-driven medical imaging. His highly cited research in medical segmentation, secure AI architectures, and remote sensing innovations underscores his impact on academia and industry. Dr. Huang’s work in privacy-preserving AI and adversarial learning is transforming cybersecurity and healthcare analytics. With an extensive publication record in IEEE Transactions and Springer, he has significantly advanced computational efficiency, security, and AI-powered medical diagnostics, making him a standout nominee for this prestigious recognition.

Professional Experience 👨‍🏫

Dr. Teng Huang is a senior researcher and faculty member at Guangzhou University, where he leads projects on medical AI, blockchain security, and computational intelligence. He has collaborated on multinational research initiatives, developing advanced AI frameworks for ultrasound and MRI analysis, tumor segmentation, and privacy-preserving recommender systems. Dr. Huang has served as a principal investigator for high-profile studies in remote sensing, adversarial AI, and federated learning. His work has been instrumental in advancing medical diagnostics, cybersecurity protocols, and AI-driven automation, making him a sought-after expert in intelligent computing and blockchain research.

Awards & Honors 🏅

Dr. Teng Huang has received multiple accolades for his contributions to artificial intelligence, medical imaging, and cybersecurity. He has been honored with the Best Paper Award at IEEE conferences for his work on efficient breast lesion segmentation and smart contract security. He was recognized among the Top AI Researchers in China for his pioneering work on transformer-based medical diagnostics. Dr. Huang also received the Outstanding Researcher Award from Guangzhou University for his breakthroughs in blockchain and AI-driven healthcare solutions. His contributions to privacy-preserving AI and cybersecurity have earned him international recognition.

Research Focus 🔬

Dr. Teng Huang’s research is centered on Blockchain, Smart Contracts, Medical AI, and Privacy-Preserving AI. His expertise includes 3D tumor segmentation, ultrasound imaging, federated learning, adversarial AI, and remote sensing. He specializes in transformer-based architectures for medical diagnostics, lightweight AI models for resource-limited platforms, and privacy-enhanced encryption techniques for IoT security. His work on self-sovereign identity management and subgraph matching algorithms has significantly advanced blockchain security and data protection. Dr. Huang’s interdisciplinary approach integrates deep learning, AI-driven medical analysis, and secure computing, positioning him at the forefront of intelligent healthcare innovations.

Publication Top Notes 📖

Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture

Efficient Breast Lesion Segmentation From Ultrasound Videos Across Multiple Source-Limited Platforms

IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel Refiner

Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

Optimized Breast Lesion Segmentation in Ultrasound Videos Across Varied Resource-Scant Environments

SFFAFormer: A Semantic Fusion and Feature Accumulation Approach for Remote Sensing Image Change Detection