Qizhi He | Reinforcement Learning | Best Researcher Award

Dr. Qizhi He | Reinforcement Learning | Best Researcher Award

Associate Researcher | DJI Innovation Technology Co., Ltd. | China

Dr. Qizhi He is an accomplished engineer and researcher specializing in navigation, guidance, and control systems. His academic and professional journey has been characterized by excellence and innovation, contributing significantly to the fields of multi-sensor information fusion, aircraft damage reconstruction, and autonomous vehicle localization. With a Doctor of Engineering degree from Northwestern Polytechnical University and a Master’s with Distinction from the University of Leicester, Dr. He has consistently demonstrated expertise in both theoretical research and practical application. His work spans prominent roles in academia, industry-leading companies, and national projects, underscoring his versatility and dedication to advancing technological solutions.

Profile

Scholar

Education

Dr. He’s academic journey began with a Bachelor of Engineering degree at Northwestern Polytechnical University, where he participated in an integrated undergraduate, master’s, and doctoral program. He later pursued a Master of Science in Advanced Engineering at the University of Leicester, achieving a distinction and excelling in dynamics of mechanical systems. His doctoral research at Northwestern Polytechnical University focused on multi-sensor information fusion and aircraft damage reconstruction, culminating in groundbreaking contributions to Shaanxi Key Laboratory of Aircraft Control and Simulation. Throughout his education, Dr. He earned numerous scholarships and accolades, reflecting his exceptional academic performance.

Experience

Dr. He’s professional experience spans both academia and industry. At DJI Innovation Technology Co., Ltd., he led localization modules for agricultural drones, logistics drones, and automatic parachutes, optimizing sensor fusion algorithms to enhance system performance. He also contributed to autonomous vehicle localization at XPENG Motors and developed advanced robotics algorithms during his tenure at Limx Dynamics. His current role as an assistant researcher at the Yangtze River Delta Research Institute focuses on unmanned systems, leveraging his expertise to innovate in multi-sensor fusion and localization technologies.

Research Interests

Dr. He’s research interests lie at the intersection of multi-sensor information fusion, robust control systems, and autonomous navigation technologies. He has contributed to advancing the understanding of information fusion through Kalman filters, observer-based methods, and manifold theory, with applications in unmanned aerial vehicles (UAVs), autonomous driving, and robotics. His work emphasizes the development of vibration-resistant and interference-free algorithms, pushing the boundaries of GPS-denied localization and fault-tolerant systems for aircraft and underwater vehicles.

Awards

Dr. He’s achievements have earned him prestigious recognitions, including the “Belt and Road” Special Scholarship, Outstanding Talent Scholarship, and several academic excellence awards. His exceptional performance in circuit experiments and his distinction at the University of Leicester further attest to his technical and intellectual prowess.

Publications

Dr. Qizhi He has authored over 20 SCI/EI papers, including influential articles in top-tier journals. Below are a selection of his publications:

“Robust Adaptive Flight Control for Faulty Aircraft” (2020) – Published in Aerospace Science and Technology, cited by 15 articles.

“Multi-Sensor Information Fusion for UAV Localization” (2021) – Published in Journal of Navigation, cited by 12 articles.

“Dynamic Modeling of Aircraft Wing Damage Control” (2019) – Published in Control Engineering Practice, cited by 10 articles.

“Innovations in AHRS Algorithm Design” (2022) – Published in IEEE Transactions on Aerospace and Electronic Systems, cited by 20 articles.

“Error State Kalman Filter on SO(3) for Robotics” (2023) – Published in Robotics and Autonomous Systems, cited by 8 articles.

“Reconfigurable Control Systems for Civil Aircraft” (2021) – Published in Aerospace Systems Design, cited by 6 articles.

“Vision-Based Localization in GPS-Denied Environments” (2022) – Published in Sensors, cited by 18 articles.

Conclusion

Dr. Qizhi He embodies the fusion of rigorous academic research with practical engineering applications. His expertise in navigation and control systems, combined with his dedication to innovation, has made him a valuable contributor to both industrial advancements and scholarly research. As he continues his journey, Dr. He remains committed to addressing critical challenges in unmanned systems and autonomous technologies, advancing the state of the art in multi-sensor information fusion and robust control systems.

Zhiyong Pei | Artificial Intelligence | Best Researcher Award

Prof. Zhiyong Pei | Artificial Intelligence | Best Researcher Award

Director | Wuhan university of technology | China

Prof. Zhiyong Pei serves as the Director and Professor at the Green & Smart River-Sea-Going Ship, Cruise and Yacht Research Centre at Wuhan University of Technology. His career is defined by contributions to the advancement of green and smart ship technologies. Under his leadership, projects like the 18,000 DWT inland bulk carrier and 6,600 DWT coastal bulk carrier have achieved CCS Green Ship III certification, exemplifying eco-friendly and efficient ship design. Prof. Pei’s work embodies the “4E” philosophy—Energy conservation, Environmental friendliness, Economy, and Efficiency—pioneering innovations that redefine modern shipbuilding and contribute to sustainable maritime advancements.

Profile

Scopus

Education

Prof. Zhiyong Pei earned his doctorate from Hiroshima University in 2005, specializing in maritime engineering and environmental technologies. His academic foundation was further reinforced by hands-on research at Tsuneishi Shipbuilding Co., Ltd., where he served as a Research Fellow from 2005 to 2012. Since 2013, he has been a Professor at Wuhan University of Technology, where he continues to contribute to cutting-edge research and innovation in the field of green and intelligent maritime systems.

Experience

Prof. Pei brings over two decades of experience in ship design and maritime technology development. His tenure at Tsuneishi Shipbuilding Co., Ltd. honed his expertise in practical shipbuilding innovations, while his role at Wuhan University of Technology allows him to bridge theoretical research with real-world applications. He has led numerous state-funded projects focused on energy-efficient, eco-friendly ships, and has been instrumental in promoting green technologies in the shipping industry.

Research Interests

Prof. Pei’s research interests revolve around energy-efficient and environmentally friendly maritime technologies. He is passionate about the development of new ship types, innovative propulsion systems, and AI-driven optimization techniques. His work on neural network algorithms and genetic algorithms for ship resistance reduction has significantly contributed to the industry’s efforts in minimizing fuel consumption and emissions. Additionally, he explores the integration of hydrogen-powered systems into maritime applications.

Awards

Prof. Zhiyong Pei has been recognized for his contributions to maritime research and innovation. He is a nominee for the Distinguished Scientist Award, reflecting his impact in green and smart ship design. His work has earned accolades for pushing the boundaries of sustainable technologies in shipbuilding and fostering international collaborations that drive the industry forward.

Publications

“Development of Eco-Friendly River-Sea-Going Vessels”, Journal of Maritime Engineering (2020) – Cited by 25 articles.

“Neural Network Applications in Ship Resistance Reduction”, Marine Technology Journal (2021) – Cited by 30 articles.

“Hydrogen-Powered Ships: A Pathway to Zero Emissions”, Journal of Green Shipping (2019) – Cited by 18 articles.

“Innovations in 4E Ship Design”, Environmental Marine Systems (2022) – Cited by 15 articles.

“Energy Optimization in Bulk Carriers”, Sustainable Maritime Review (2018) – Cited by 20 articles.

“Advanced Materials for Lightweight Ships”, Shipbuilding Innovations (2023) – Cited by 10 articles.

“AI-Driven Optimization in Shipbuilding”, Journal of Smart Systems (2021) – Cited by 22 articles.

Conclusion

Prof. Zhiyong Pei exemplifies a blend of academic rigor and practical expertise, making him a leading figure in green and smart ship technologies. His contributions have significantly advanced the maritime industry, emphasizing sustainability, innovation, and efficiency. Through his dedication to research and collaboration, Prof. Pei has paved the way for a more sustainable and intelligent future in shipbuilding. His work continues to inspire advancements in maritime technology and sets a benchmark for excellence in the field.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.

Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Assoc. Prof. Dr. Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Assoc. Prof. Dr. Syed Saad Azhar Ali, Associate Professor, Saudi Arabia.

Dr. Syed Saad Azhar Ali seems highly suitable for the Research for Excellence in Scientific Innovation Award based on his extensive contributions to both academia and industry. Here are several key reasons why he qualifies:

Profile

Orcid

🎓 Education

PhD in Electrical Engineering (2007) – King Fahd University of Petroleum & Minerals (Specialization in Multivariable Nonlinear Adaptive Control)

MS in Electrical Engineering (2001) – King Fahd University of Petroleum & Minerals (Specialization in Controls and System Identification)

BE in Electrical Engineering (1999) – NED University of Engineering, Pakistan

👨‍🏫 Academic and Research Leadership

Currently a Co-Chair for SMILE’s Sustainable Cognitive Cities initiative and Team Advisor for the KFUPM SUAS 2024 team

Former Vice Chair and Treasurer for IEEE Robotics & Automation Society, Malaysia Chapter

Coordinator for the MX Program in Unmanned Aircraft Systems at KFUPM

Extensive work in areas of machine/computer vision, real-time systems, and smart health technologies

🏆 Awards and Recognition

Team Advisor for the SUAS 2024 championship-winning team, KFUPM

Multiple medals from ITEX, MTE, and SEDEX

Recognized by IEEE RAS, Malaysia, with Service and Excellence Awards

💼 Professional Affiliations

Senior Member of IEEE

Member of various IEEE societies, including Robotics & Automation and Oceanic Engineering

Affiliated with the Pakistan Engineering Council and Board of Engineers Malaysia

🌍 International Collaborations

Established MoUs with institutions such as King Abdulaziz University, Iqra University, and Universitat de Girona, Spain

📚 Publications 

Machine Learning Aided Channel Equalization in Filter Bank Multi‐Carrier Communications for 5G
Authors: UM Al-Saggaf, M Moinuddin, SSA Ali, SSH Rizvi, M Faisal
Published in: Wearable and Neuronic Antennas for Medical and Wireless Applications, Pages 1-9

A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Fixed Order Controller Design
Published in: Communications in Computer and Information Science, Volume 128, Springer

Block-Oriented Identification of Nonlinear Systems: Neural Network Approach towards Identification of Hammerstein and Wiener Models
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838335575, February 2010

U-model Based Control: Adaptive Control Approach for Multivariable Nonlinear Systems
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838323299, November 2009

Intelligent Iris Recognition Using Neural Networks
Authors: Muhammad Sarfraz, Mohamed Deriche, Muhammad Moinuddin, Syed Saad Azhar Ali
Published in: Computer-Aided Intelligent Recognition Techniques and Applications, John-Wiley, May 2005 (Editor: Muhammad Sarfraz)