Muratulla Utenov | Data Visualization | Best Researcher Award

Posted on by AI Data Scientist

Prof. Dr. Muratulla Utenov | Data Visualization | Best Researcher Award

Professor at Al-Farabi Kazakh National University, Kazakhstan

Muratulla Utenov is a distinguished academic in the field of mechanics and engineering, currently serving as a Professor in the Department of Mechanics at al-Farabi Kazakh National University. With over four decades of experience in teaching, research, and academic leadership, he has significantly contributed to the advancement of analytical methods in robotics, mechanism theory, and computational modeling. His innovative research has earned national and international recognition, particularly in the design and analysis of robotic manipulators and mechanical systems.

Profile

Scopus

Education

Professor Utenov’s academic journey began with a specialization in mechanics from S.M. Kirov Kazakh State University in 1975. He continued at the same university to earn his Candidate of Technical Sciences degree in 1989, focusing on advanced mechanical systems. In 2007, he was awarded a Doctor of Technical Sciences degree by al-Farabi Kazakh National University, where he deepened his research in analytical modeling, mechanics of manipulators, and robotic system dynamics. His academic training established a robust foundation for his long-standing career in mechanical engineering and applied mechanics.

Experience

Since 2012, Muratulla Utenov has been a full professor in the Department of Mechanics at al-Farabi KazNU. Prior to this, he held various teaching and research positions where he led academic initiatives in mechanical sciences and supervised numerous students at graduate and doctoral levels. His professional journey also includes collaborative research efforts with international scholars, resulting in influential conference presentations and high-quality journal publications. He has also led key research grants, including his principal investigator role for a project under the Research Institute of Mathematics and Mechanics focused on robotic system strength and stiffness from 2015 to 2017.

Research Interest

Professor Utenov’s research interests span a wide array of topics in mechanics and robotics. He specializes in analytical modeling of mechanical systems, computational determination of internal forces, kinematic and dynamic analysis of manipulators, and visualization of distributed loads in robotic structures. His work emphasizes precision modeling of parallel and serial manipulators using computational tools, with applications in automation, industrial robotics, and advanced mechanical systems. He also actively explores Maple and other simulation platforms to animate and visualize mechanical motions, further enhancing the theoretical understanding of robotic mechanisms.

Award

Throughout his career, Professor Utenov has been recognized for his excellence in research and academic leadership. His project on predicting the strength and stiffness of robotic mechanisms, funded by the Research Institute of Mathematics and Mechanics, stands as a testament to his role as a thought leader in applied mechanics. Additionally, his contributions to international conferences and his partnerships with researchers from institutions worldwide underscore the recognition of his expertise on a global stage.

Publication

Professor Utenov has authored numerous impactful publications in both journals and international conference proceedings. Some of his significant journal works include:

Utenov, M., et al. “Analytical Method for Determination of Internal Forces of Mechanisms and Manipulators,” Robotics (MDPI), vol. 7, no. 3, p. 53, 2018 — cited by 25 articles.

Baigunchekov, Z., et al., “A Robomech Class Parallel Manipulator with Three Degrees of Freedom,” Eastern-European Journal of Enterprise Technologies, vol. 7, no. 105, pp. 44-56, 2020 — cited by 13 articles.

Utenov, M., et al., “Definition and Visualization of Distributed Dynamic Loads of Manipulators,” IFToMM Asian MMS 2024, pp. 405-413 — presented in 2024.

Utenov, M., et al., “3D Modeling Manipulator Movement and Direct Positional Kinematic Analysis,” IFToMM Asian MMS 2024, pp. 398-404 — presented in 2024.

Utenov, M., et al., “Animation of Motion of Mechanisms and Robot Manipulators in the Maple system,” ACM ICRCA 2017, pp. 30-34 — cited by 6 articles.

Baigunchekov, Z., Kalimoldaev, M., Utenov, M., et al., “Geometry and Direct Kinematics of Six-DOF Three-Limbed Parallel Manipulator,” ROMANSY 2016, pp. 39-46 — cited by 15 articles.

Baigunchekov, Z., Kalimoldaev, M., Utenov, M., et al., “Inverse Kinematics of Six-DOF Three-Limbed Parallel Manipulator,” RAAD 2016, pp. 171-178 — cited by 17 articles.

Conclusion

Professor Muratulla Utenov stands out as a pioneering researcher and educator in the field of mechanics and robotics. His deep-rooted expertise in mechanical analysis, combined with his dedication to advancing theoretical and practical knowledge in robotic systems, has left an enduring mark on the academic community. Through his extensive research, scholarly publications, and collaborative projects, he continues to shape the future of applied mechanics and inspire a new generation of mechanical engineers and researchers globally.

Daojun Liang | Time Series Analysis | Best Researcher Award

Posted on by AI Data Scientist

Mr. Daojun Liang | Time Series Analysis | Best Researcher Award

PhD student | Shandong University | China

Mr. Daojun Liang is a dedicated PhD student at Shandong University with a solid academic background in computer science. He earned his BS from Taishan University in 2016 and his MS from Shandong Normal University in 2019. Currently pursuing his doctoral studies, Daojun has established himself as a researcher with expertise in uncertainty quantification, time series analysis, and large language models (LLM). Recognized for his independent research skills, Daojun has published several high-level papers in prestigious journals and serves as a reviewer for reputable organizations like IEEE, ACM, Elsevier, and Springer.

Profile

Scholar

Education

Daojun Liang began his academic journey with a Bachelor’s degree in Computer Science from Taishan University in 2016. Driven by a passion for innovation, he pursued a Master’s degree in Information Science and Engineering at Shandong Normal University, which he completed in 2019. His commitment to academic excellence led him to Shandong University, where he is currently advancing his research as a PhD candidate. His educational foundation has equipped him with the skills necessary for cutting-edge research and practical problem-solving in the fields of artificial intelligence and computational sciences.

Experience

Daojun’s research and professional experience demonstrate his versatility and expertise. He has contributed to several impactful projects, such as the development of intelligent vehicle networking technologies and the creation of advanced forecasting methods for 6G communication systems. His work with data-driven analysis and artificial intelligence for industrial applications highlights his ability to address complex challenges. Additionally, his role as an SCI reviewer for leading journals and collaborations with esteemed institutions like Fortiss GmbH and Shanghai Jiao Tong University reflect his strong academic and professional network.

Research Interests

Daojun’s research interests encompass long-term time series forecasting, uncertainty quantification, and the development of probabilistic inference methods. He focuses on analyzing intrinsic patterns in data to propose efficient and lightweight solutions. His work has implications for a variety of industries, including energy, manufacturing, and telecommunications. Daojun is also exploring the intersection of deep learning, natural language processing, and computer vision, ensuring his research remains at the forefront of innovation.

Awards and Recognitions

Daojun has been nominated for the Best Researcher Award in recognition of his outstanding contributions to academia and industry. His innovative methods for time series analysis and uncertainty quantification have not only been published in high-impact journals but have also been widely adopted in industrial applications. He has been honored as a reviewer for leading journals and conferences, which underscores his influence in the research community.

Publications

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Progressive Supervision via Label Decomposition: A Long-Term and Large-Scale Wireless Traffic Forecasting Method. Knowledge-Based Systems, 305, p.112622. (SCI Q1, IF = 7.2). Cited by 10.

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Periodformer: An Efficient Long-Term Time Series Forecasting Method Based on Periodic Attention. Knowledge-Based Systems, 304, p.112556. (SCI Q1, IF = 7.2). Cited by 8.

D. Liang, H. Zhang, D. Yuan, M. Zhang. (2024). Multi-Head Encoding for Extreme Label Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. (SCI Q1, IF = 20.8). Cited by 15.

Liang, D., Yang, F., Wang, X., et al. (2019). Multi-Sample Inference Network. IET Computer Vision, 13(6), 605-613. (SCI Q1, IF = 1.7). Cited by 12.

Liang, D., Zhang, H., Yuan, D., et al. (2025). DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting. ACM SigKDD 2025. Cited by 5.

Conclusion

Daojun Liang exemplifies the qualities of a modern researcher: innovative, dedicated, and collaborative. His contributions to uncertainty quantification, time series analysis, and large language models are reshaping academic and industrial practices. With numerous publications, collaborative projects, and a commitment to advancing knowledge, Daojun stands as a promising figure in his field.