Roya Amjadifard | Reinforcement Learning | Best Researcher Award

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Assoc. Prof. Dr. Roya Amjadifard | Reinforcement Learning | Best Researcher Award

Faculty member at Kharazmi University. Iran

Roya Amjadifard is a distinguished researcher and academic whose contributions have significantly advanced her field of study. With a strong background in research and innovation, she has been involved in numerous projects aimed at addressing complex challenges. Through her expertise and commitment, she has established herself as a leading figure in her area of specialization.

Profile

Scopus

Education

Dr. Amjadifard earned her Ph.D. in Control Engineering from Tarbiat Modarres University, Tehran, in 2004, under the guidance of Dr. M. Beheshti. Her dissertation focused on “Robust Control for a Class of Nonlinear Singularly Perturbed Systems.” Prior to this, she obtained her M.S. in Control Engineering from K.N.T. University of Technology in 1995, where she explored the robustness of multivariable digital controllers. She completed her undergraduate studies in Electronic Engineering at Ferdowsi University of Mashhad in 1990, working on the design and implementation of a 12-channel TDM-PCM transmitter and receiver.

Experience

Since 2006, Dr. Amjadifard has been serving as an Associate Professor at Kharazmi University. She has extensive teaching experience, instructing both undergraduate and graduate courses, including linear algebra, electrical circuits, digital control, fuzzy control, and system identification. She has also supervised numerous Ph.D. and M.Sc. students, guiding research in advanced control methodologies and artificial intelligence applications in engineering.

Research Interests

Dr. Amjadifard’s research interests encompass singularly perturbed systems control, robust control, optimal control, fuzzy modeling and control, neural control, and learning-based control. Her interdisciplinary approach has facilitated advancements in automation, robotics, and power system analysis, addressing complex engineering challenges through innovative control strategies.

Awards

Dr. Amjadifard has received several accolades recognizing her contributions to control engineering. Her research in robust control systems has been widely acknowledged in academic and industrial circles. She has been honored for her excellence in teaching and mentorship, shaping the next generation of control engineers.

Publications

Ghasemzadeh, A., Amjadifard, R., Keymasi-Khalaji, A., “Adaptive dynamic programming for trajectory tracking control of a tractor-trailer wheeled mobile robot,” IET Control Theory & Applications, 2025.

Amini, F., Amjadifard, R., Mansouri, A., “Fuzzy Information Granulation Towards Benign and Malignant Lung Nodules Classification,” Journal of Computer Methods and Programs in Biomedicine Update, 2024.

Khedmati, H., Amjadifard, R., “On the global stabilization of a class of nonlinear singularly perturbed systems using nonlinear H∞ control approach,” International Journal of Control, 2021.

Tavakolifar, D., Khaloozadeh, H., Amjadifard, R., “Stabilization Of Switched Systems With All Unstable Modes: Application To The Aircraft Team Problem,” Journal of Systems Engineering and Electronics, 2019.

Beheshtipour Z., Khaloozadeh H., Amjadifard R., “Model-Reference Adaptive Moment Control Of Uncertain Nonlinear Stochastic Systems,” Asian Journal of Control, 2020.

Ebrahimi Boukani, S., Khosrowjerdi, M. J., Amjadifard, R., “Terminal Sliding Mode Control Allocation for Nonlinear Systems,” Canadian Journal of Electrical and Computer Engineering, 2017.

Beheshtipour Z., Khaloozadeh H., Amjadifard R., “On the Solvability of Feedback Complete Linearization of Nonlinear Stochastic Systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017.

Conclusion

Dr. Roya Amjadifard’s contributions to control engineering and automation have significantly influenced the field. Her research in robust and learning-based control strategies has led to innovative solutions in various engineering domains. As an educator, she has mentored numerous students, inspiring future engineers and researchers. Her dedication to academic excellence and groundbreaking research continues to shape advancements in control systems worldwide.

Udeme Ukpong | Reinforcement Learning | Best Researcher Award

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Mr. Udeme Ukpong | Reinforcement Learning | Best Researcher Award

Research Assistant at Covenant University, Nigeria

Udeme Christopher Ukpong is a researcher at the Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE) and a PhD candidate in Information and Communications Engineering at Covenant University, Nigeria. He holds a Master of Engineering in Information and Communication Engineering from Covenant University and a Bachelor’s degree in Computer Engineering with First Class Honours from the Kwame Nkrumah University of Science and Technology, Ghana. His primary research interests encompass machine intelligence, wireless communication, cognitive radio, cloud computing, and high-performance computing.

Profile

Scopus

Education

Udeme Ukpong has an impressive academic background, starting with a Bachelor’s degree in Computer Engineering, awarded with First Class Honours in 2015 from the Kwame Nkrumah University of Science and Technology, Ghana. He furthered his education at Covenant University, where he completed his Master of Engineering in Information and Communication Engineering in 2022 and is currently pursuing a PhD in the same field. His education reflects a strong foundation in both theoretical and practical aspects of technology and engineering.

Experience

Udeme Ukpong is currently serving as a Research Assistant at the Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE) at Covenant University, a World Bank Ace-Impact Centre. His responsibilities include conducting extensive literature reviews, identifying research gaps, and assisting in writing and submitting research proposals for funding. Additionally, Ukpong is involved in performing experimental research using laboratory equipment and statistical software such as MATLAB and Python for data analysis. Since May 2023, he has also worked as a Research Intern at the Advanced Signal Processing and Machine Intelligence Research Group at Covenant University. Here, he collaborates with faculty and industry experts to achieve research objectives, focusing on computational modeling, simulations, and coding tasks.

Research Interests

Udeme Ukpong’s research interests lie in several cutting-edge domains, particularly machine intelligence, wireless communication, cognitive radio, cloud computing, and high-performance computing. He is focused on the application of deep reinforcement learning in dynamic spectrum access for cognitive radio networks, exploring new ways to improve wireless communications. His work also involves leveraging cloud computing and high-performance computing techniques to address challenges in these areas.

Awards

Udeme Ukpong’s outstanding contributions to research and technology have earned him several accolades and opportunities for recognition. Notably, he has been nominated for the 2024 CApIC-ACE Innovation Seed Grant and the Google Academic Research Awards. These recognitions highlight his potential as a researcher in the fields of machine intelligence and wireless communication. His innovative contributions to dynamic spectrum access in cognitive radio networks have also placed him at the forefront of his field.

Publications

Udeme Ukpong has contributed to several significant publications in the realm of wireless communication and machine intelligence:

Ukpong, U. C., Idowu-Bismark, O., Adetiba, E., Kala, J. R., Owolabi, E., Oshin, O., & Dare, O. E. (2025). Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks. Scientific African, 27, e02523.

Dare, O. E., Okokpujie, K., Adetiba, E., Idowu-Bismark, O., Abayomi, A., Kala, R. J., … & Ukpong, U. C. (2024). Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping. IEEE Access.

Ukpong, U. C., Idowu-Bismark, O., Adetiba, E., Dare, O. E., Owolabi, E., Kala, R. J. (2024). Deep Reinforcement Learning Applications For Coexistence in Television Whitespace: A Mini-Review. 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), Omu-Aran, Nigeria, pp. 1-9, doi: 10.1109/SEB4SDG60871.2024.10629684.

Ifijeh, A. H., Adetiba, E., Adewale, A., Thakur, S., Moyo, S., Emmanuel, D. O., & Ukpong, U. C. (2023, November). Exploring Television White Space as an Alternative for Wireless Broadband Connectivity. In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) (pp. 1-7). IEEE.

These publications contribute significantly to the knowledge base in the fields of wireless communication and machine learning applications in dynamic spectrum management.

Conclusion

Udeme Ukpong’s academic journey and research experiences reflect a strong commitment to advancing technology and contributing to global knowledge in wireless communication and machine intelligence. His innovative research on deep reinforcement learning for cognitive radio networks and his contributions to the development of new models in television spectrum mapping underscore his potential in these areas. With multiple publications and awards to his name, Ukpong is poised to make significant impacts in his field, demonstrating his capabilities as a leading researcher.

Qizhi He | Reinforcement Learning | Best Researcher Award

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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.