Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

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Prof. Dr. Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

Professor at Tabriz university, Iran

Dr. Jafar Keighobadi is a distinguished professor in the Faculty of Mechanical Engineering at the University of Tabriz, Iran. With a career spanning over two decades, he has made significant contributions to the fields of mechatronics, control systems, signal processing, and artificial intelligence. His expertise extends to the programming and implementation of microcontroller and microprocessor boards, reflecting a profound integration of theoretical knowledge with practical applications. Throughout his tenure, Dr. Keighobadi has been instrumental in advancing research and education, mentoring numerous students, and collaborating on projects that bridge the gap between academia and industry.

Profile

Scopus

Education

Dr. Keighobadi’s academic journey commenced with a Bachelor of Science in Mechanical Engineering, specializing in Applied Design Mechanics, from the University of Tabriz. He furthered his studies at the Amirkabir University of Technology (Tehran Polytechnic), where he earned both his Master of Science and Ph.D. in Mechanical Engineering. His doctoral research focused on “Robust Estimator Design for Stochastic Attitude-Heading Reference System in Accelerated Maneuvers,” a comprehensive study that entailed the development and extensive testing of a low-cost Attitude-Heading Reference System. This academic foundation has been pivotal in shaping his research trajectory and teaching philosophy.

Experience

Dr. Keighobadi’s professional experience is marked by a progressive academic career at the University of Tabriz, where he has served as an Assistant Professor (2008–2013), Associate Professor (2014–2020), and has held the position of full Professor since 2020. In addition to his teaching and research responsibilities, he has been a Patent Examiner at the university since 2009, overseeing the evaluation of innovative technologies and inventions. His commitment to education is further demonstrated through his roles as a lecturer at various institutions, including the Islamic Azad University branches in Khoy, Qazvin, and Maragheh, as well as Zanjan University. These roles have enabled him to disseminate knowledge across a broad spectrum of students and professionals.

Research Interests

Dr. Keighobadi’s research interests are diverse and interdisciplinary, encompassing MEMS sensors and actuators, GNSS, control systems, and Kalman filtering. He has a profound interest in autonomous robots and the design and implementation of intelligent systems. His work delves into robust filtering and control, stochastic nonlinear estimation and control, and the mathematical algorithms of chaos. A significant portion of his research is dedicated to artificial intelligence, including fuzzy logic, artificial neural networks, and deep learning. Moreover, he is adept in FPGA, DSP, and ARM programming, which underscores his commitment to integrating advanced computational techniques with mechanical engineering applications.

Awards

Throughout his illustrious career, Dr. Keighobadi has been the recipient of several accolades that recognize his contributions to research and academia. Notably, he was honored as the Best Young Researcher across all technical departments at the University of Tabriz on November 27, 2011. This award reflects his dedication to advancing engineering knowledge and his impact on the academic community. Additionally, his academic excellence was evident early in his career when he secured the second rank out of 120 candidates in the Ph.D. entrance exam at Amirkabir University of Technology on June 18, 2001. These honors underscore his commitment to excellence and innovation in his field.

Publications

Dr. Keighobadi’s scholarly output includes numerous publications in esteemed journals. A selection of his notable works includes:

“Immersion and Invariance-Based Extended State Observer Design for a Class of Nonlinear Systems,” published in the International Journal of Robust and Nonlinear Control on May 21, 2021.

“Adaptive Neural Dynamic Surface Control of Mechanical Systems Using Integral Terminal Sliding Mode,” featured in Neurocomputing on December 21, 2019.

“Adaptive Inverse Deep Reinforcement Lyapunov Learning Control for a Floating Wind Turbine,” published in Scientia Iranica on January 15, 2023.

“Decentralized INS/GPS System with MEMS-Grade Inertial Sensors Using QR-Factorized CKF,” featured in the IEEE Sensors Journal on June 1, 2017.

“INS/GNSS Integration Using Recurrent Fuzzy Wavelet Neural Networks,” published in GPS Solutions on May 21, 2020.

“Passivity-Based Hierarchical Sliding Mode Control/Observer of Underactuated Mechanical Systems,” featured in the Journal of Vibration and Control on May 19, 2022.

“Extended State Observer-Based Robust Non-Linear Integral Dynamic Surface Control for Triaxial MEMS Gyroscope,” published in Robotica on January 15, 2019.

These publications highlight Dr. Keighobadi’s extensive research in control systems, artificial intelligence, and their applications in mechanical engineering.

Conclusion

Dr. Jafar Keighobadi stands as a prominent figure in mechanical engineering, with a career dedicated to advancing research, education, and practical applications in mechatronics and control systems. His interdisciplinary approach, combining robust theoretical frameworks with hands-on implementation, has significantly impacted both academic circles and industry practices. As a mentor, researcher, and educator, Dr. Keighobadi continues to inspire and lead in the ever-evolving landscape of engineering and technology.

Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

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Ms. Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

Student at Guangxi University of Science and Technology, China

Juanling Liang is a graduate student specializing in robotics engineering at Guangxi University of Science and Technology. Currently engaged in research focusing on robotic arm path planning and dynamic obstacle avoidance, Juanling has developed a strong foundation in algorithms such as RRT* and APF. The primary aim of the research is to optimize robotic arm movement in complex environments, with an emphasis on improving the operational efficiency of industrial tasks. Despite being early in his academic career, he has already contributed significantly to the field through his academic paper on robotic arm optimization.

Profile

Orcid

Education

Juanling Liang is pursuing a graduate degree in robotics engineering at Guangxi University of Science and Technology. His academic journey has been centered on understanding the intricate mechanisms of robotic motion and artificial intelligence, with a particular focus on dynamic obstacle avoidance and path planning for robotic arms. His educational background equips him with a solid grasp of both the theoretical and practical applications of robotics in real-world environments, positioning him well for future advancements in the field.

Experience

Although still a student, Juanling Liang has already demonstrated notable progress in the field of robotics. His primary research revolves around the optimization of algorithms such as RRT* and APF, which are essential for improving robotic arm navigation in environments with obstacles. This research not only strengthens his expertise but also shows his commitment to bridging the gap between theoretical models and practical applications, especially in the industrial sector.

Research Interest

Juanling’s research interests are primarily focused on path planning and dynamic obstacle avoidance for robotic arms. He aims to improve the performance of robotic arms in complex environments, where the efficient navigation of obstacles is crucial for productivity and safety. His work involves enhancing existing algorithms to optimize robotic movements, ensuring that robotic arms can operate more effectively in dynamic and cluttered spaces. The ultimate goal is to improve the efficiency of industrial tasks, such as assembly lines, where precision and speed are critical.

Award

Juanling Liang is a nominee for the prestigious Young Scientist Award, recognizing his outstanding contribution to robotics research. His work on optimizing robotic arm path planning has the potential to make significant strides in the efficiency of industrial processes. The award would serve as a recognition of his academic dedication and research contributions, highlighting his potential for future innovations in the field.

Publication

  1. Liang, J. (2024). “Optimization of the RRT* Algorithm for Robotic Arm Path Planning.” Journal of Robotics and Automation, Vol. 1, No. 1.
    Cited by: 12 articles

Conclusion

Juanling Liang is an emerging talent in the field of robotics engineering, with a strong focus on robotic arm path planning and dynamic obstacle avoidance. His work on optimizing algorithms such as RRT* and APF showcases his ability to address complex challenges in robotics, contributing to advancements that have significant real-world applications, especially in industrial settings. With his dedication to research and innovation, Juanling is poised to become a leading figure in robotics, making valuable contributions to the scientific community and the industries relying on robotics technology.