Binghao OuYang | Optimization | Best Researcher Award

Dr. Binghao OuYang | Optimization | Best Researcher Award

Research assistant at City University of Hong Kong, China

Dr. OuYang Binghao (欧阳炳濠) is a promising early-career researcher specializing in game theory and distributed Nash equilibrium seeking algorithms ⚙️📊. Currently pursuing a joint PhD at City University of Hong Kong and University of Science and Technology of China, he holds a strong academic record with a GPA of 3.78/4.3 🎓. His research focuses on fixed-time convergence algorithms for complex control systems, with practical applications in non-cooperative games and Euler-Lagrange systems 🔍🤖. Dr. OuYang has earned several awards, including a gold medal at the National College Student Innovation Competition 🥇 and recognition as an outstanding undergraduate graduate. Skilled in Python, C++, and Matlab, he also applies reinforcement and deep learning techniques to advance his research 💻🧠. With a solid mathematical foundation and innovative approach, Dr. OuYang is a rising talent in control science and engineering, poised to make significant contributions to his field.

Professional Profile 

🎓 Education

Dr. OuYang Binghao (欧阳炳濠) has pursued a rigorous academic path in engineering and control science. He earned his Bachelor’s degree in Automation from the University of Science and Technology of China (USTC) (2016–2020) 🎯, followed by PhD studies in Control Science and Engineering (2020–2022) with a GPA of 3.50/4.3 📚. Currently, he is completing a Joint PhD in Biomedical Engineering and Control Science at City University of Hong Kong and USTC, maintaining an impressive GPA of 3.78/4.3 🏅. Guided by Professors Feng Gang and Wang Yong, his education blends theoretical depth with applied research, equipping him to tackle complex engineering challenges across interdisciplinary domains 🎓🔬.

💼 Professional Experience

Dr. OuYang has demonstrated practical innovation from early in his academic career. In 2020, his excellent undergraduate graduation project involved the development of a fingerprint attendance system 🛠️. Later, in 2021, he participated in the China International College Students Innovation Competition, helping design a motion perception and high-precision positioning system, which won a gold award 🥇. From 2022–2023, he conducted research at City University of Hong Kong focused on distributed Nash equilibrium seeking in non-cooperative games 🤝📈. His hands-on experience bridges theoretical modeling and real-world applications, showcasing a promising blend of technical creativity and research diligence 💡🧪.

🔬 Research Interest

Dr. OuYang’s core research areas include game theory, distributed Nash equilibrium algorithms, and fixed-time convergence strategies for control systems 📐⚙️. He is particularly focused on solving equilibrium-seeking problems in constrained and dynamic non-cooperative games, as well as Euler-Lagrange systems 🔄🧠. With expertise in reinforcement learning, deep learning, and optimization, his work integrates modern computational tools to address long-standing challenges in control science 🚀. His interest in convergence algorithms extends to real-time systems, offering broad applications in autonomous systems, multi-agent robotics, and decentralized networks 🤖🌐. His work is driven by a strong mathematical foundation and a vision for robust and scalable algorithm design 📊🧮.

🏆 Awards and Honors

Dr. OuYang has received multiple honors reflecting both academic excellence and innovation 🏅. He earned the Outstanding Undergraduate Graduate award from USTC in 2020, recognizing his stellar academic performance 🎓. His graduation project was named an Excellent Graduation Project, and he secured First-Class Graduate Scholarships in both 2020 and 2021 💸. In 2021, he won a Gold Award in the National College Student Innovation Competition for his contributions to a motion perception and positioning system 🥇. Earlier, in 2019, he received the Second Prize in the NXP Cup Intelligent Car Competition 🚗. These recognitions affirm his technical competence, creativity, and dedication to impactful research and development.

💼📘Conclusion

Dr. OuYang Binghao stands out as a dynamic and skilled researcher, combining academic excellence with practical innovation 💼📘. With a strong educational background, impactful project work, and research in high-impact areas such as game theory and fixed-time algorithms, he exhibits the qualities of a future leader in engineering and control systems 🧭. His proficiency in programming, AI integration, and control dynamics, along with international research exposure, positions him as a valuable contributor to the global research community 🌍. Dr. OuYang’s trajectory reflects not only promise but also a clear commitment to solving critical real-world problems through mathematics, computation, and collaborative research 🤝🔬..

Publication Top Notes

Title: Vertically Oriented Micron-Thick Perovskite Film Enables Efficient and Stable Inverted Perovskite Solar Cells

Authors: Bing-Hao Lv, Yong-Chun Ye, Jun-Gan Wang, Liu-Jiang Zhang, Yu-Hang Zhang, Ming-Li Zheng, Xian-Min Chen, Hui-Wei Du, Jie Yang, Xin-Yu Zhang, Meng-Lei Xu, Qiu-Feng Ye, Xingyu Gao, Jian-Xin Tang, Yongbing Tang

Year: 2025

Source: Chemical Engineering Journal, Volume 511, Article 161966CoLab

DOI: 10.1016/j.cej.2025.161966

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Zohaib Khan | Optimization | Best Researcher Award

Dr. Zohaib Khan | Optimization | Best Researcher Award

Jiangsu University, China

Zohaib Khan is a dedicated researcher specializing in machine learning, object detection, and control science engineering, with a strong focus on precision agriculture and AI-driven automation. Currently pursuing a PhD at Jiangsu University, China, he has made significant contributions to deep learning-based agricultural robotics, publishing multiple first-author papers in high-impact SCI Q1, Q2, and EI journals. His work emphasizes real-time detection, optimization algorithms, and AI-driven sustainability solutions. With extensive mentoring experience (50+ Bachelor’s and 10 Master’s students), he has played a key role in academic development. Zohaib has received numerous national and international awards, including first prizes in elite research and innovation competitions. His technical expertise spans Python, MATLAB, LaTeX, and AI-driven modeling, complementing his ability to lead interdisciplinary research. With a passion for advancing AI applications in agriculture, he continues to drive innovation in sustainable and automated farming solutions.

Professional Profile 

Google Scholar
Scopus Profile
ORCID Profile

Education

Zohaib Khan is currently pursuing a PhD in Control Science Engineering at Jiangsu University, China (2022–2026), specializing in machine learning and object detection. He previously earned an MSc in Electrical Engineering (2019–2022) from the same institution, focusing on power systems and renewable energy. His Bachelor’s degree in Electrical Power Engineering (2013–2017) from Swedish College of Engineering and Technology, Pakistan, laid the foundation for his technical expertise. His early academic years were marked by excellence, having completed Pre-Engineering at Fazaia Degree College (2011–2013) and his Secondary School Certificate (2009–2011) at Agricultural University Public School. Zohaib’s academic journey is distinguished by his strong analytical skills and passion for integrating AI and automation in engineering solutions. His education reflects a deep commitment to advanced research, innovation, and interdisciplinary problem-solving, positioning him as a future leader in AI-driven technologies and precision agriculture.

Professional Experience

Zohaib Khan has gained substantial experience in both academic research and engineering practice. As an intern at WAPDA, Pakistan, he developed hands-on expertise in power distribution and transmission lines, strengthening his understanding of grid operations and maintenance. Later, as an Electrical Engineer at LIMAK (JV) ZKB – CPEC Project (2017–2018), he contributed to electrical system design, installation, and maintenance, gaining valuable project management experience. His role involved troubleshooting, safety compliance, and interdisciplinary collaboration, enhancing his problem-solving capabilities. In academia, Zohaib has mentored over 50 Bachelor’s and 10 Master’s students, guiding them through research projects in machine learning, object detection, and automation. His strong writing, teaching, and IT skills have been instrumental in fostering innovation. His diverse experience, spanning applied research and engineering implementation, makes him a well-rounded professional capable of driving breakthroughs in AI-powered automation and precision agriculture.

Research Interest

Zohaib Khan’s research focuses on machine learning, deep learning, object detection, and AI-driven automation, with applications in precision agriculture and robotics. His studies revolve around real-time detection, optimization algorithms, and advanced control systems for agricultural sustainability and industrial automation. He has pioneered AI-driven precision farming techniques, developing deep learning-enhanced YOLOv7 and YOLOv8 algorithms for real-time crop health assessment and robotic spraying systems. Additionally, his work explores autonomous navigation in unstructured farmlands, energy-efficient control systems, and reinforcement learning for AI-based decision-making. His research extends to risk assessment in renewable energy systems, contributing to more efficient and resilient smart grids. Through interdisciplinary collaborations, Zohaib continues to push the boundaries of AI in sustainable agriculture, robotics, and industrial automation, aiming to develop intelligent, scalable, and high-impact solutions for modern technological challenges.

Awards and Honors

Zohaib Khan has received multiple prestigious awards recognizing his contributions to research, innovation, and academic excellence. He has won First Prizes in National Competitions, including the China University Business Elite Challenge (2024) and the Brand Planning Competition (2024). His research excellence was acknowledged with the Excellent Paper Award at the Sino-award (2021) and special recognition in Jiangsu Province Graduate Energy-saving and Low-Carbon Research Competition (2023). Additionally, he was honored as an Outstanding Student in the 17th “Yale School of Jiangsu University” program and received a Certificate of Excellence for Teaching Assistance. His leadership and public speaking skills earned him first place in an English debate at Jiangsu University. These accolades reflect his dedication to research, leadership in innovation, and commitment to advancing AI applications in engineering and agriculture, solidifying his reputation as a promising researcher in his field.

Conclusion

Zohaib Khan’s academic, professional, and research journey showcases his exceptional talent in AI-driven automation, machine learning, and precision agriculture. His extensive experience in research, mentoring, and engineering practice positions him as a leading scholar in intelligent agricultural robotics and sustainable AI applications. With a strong publication record in high-impact journals (SCI Q1, Q2, and EI) and multiple national and international awards, he has demonstrated his ability to drive innovation and solve real-world problems. His work in deep learning-based automation and AI-driven optimization techniques continues to push the boundaries of technology for sustainability and efficiency. As he progresses in his career, Zohaib remains committed to advancing cutting-edge research, fostering academic collaborations, and contributing transformative solutions in AI, robotics, and smart energy systems. His dedication and achievements make him a strong candidate for prestigious research awards and a key contributor to the future of AI in engineering and agriculture.

Publications Top Noted