Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

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Assoc. Prof. Dr. Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

Zhejiang A&F University | China 

Dr. Tongcun Liu is an Associate Professor at Zhejiang A & F University, specializing in computer science and technology with a strong focus on big data analytics and artificial intelligence. He earned his Ph.D. from the Beijing University of Posts and Telecommunications and later enhanced his academic experience as a Visiting Scholar at the Hong Kong University of Science and Technology. His research primarily revolves around advanced algorithms for graph computing, recommendation systems, and AI4Science, contributing significantly to the intersection of data intelligence and computational innovation. Dr. Liu leads multiple research projects funded by the National Natural Science Foundation of China and the Zhejiang Provincial Natural Science Foundation. His current and completed projects include the development of data-driven models for estimating mangrove soil dissolved organic carbon sequestration potential and the creation of cloud-edge collaborative recommendation systems based on session flow methods. With a robust publication record of more than 30 papers in esteemed international journals and conferences, his scholarly work has had a substantial impact on the field of artificial intelligence and data-driven computing. In addition to his academic achievements, Dr. Liu holds over 10 granted patents from more than 20 applications, reflecting his strong commitment to technological innovation and the advancement of AI-based computational methodologies.

Profile : Google Scholar

Featured Publications 

Feng, H., Qiu, J., Wen, L., Zhang, J., Yang, J., Lyu, Z., Liu, T., & Fang, K. (2025). U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision. Neural Networks, 185, 107207.

Fang, K., Deng, J., Dong, C., Naseem, U., Liu, T., Feng, H., & Wang, W. (2025). MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. Proceedings of the ACM on Web Conference 2025, 5065–5074.

Liu, T., Yu, G., Kwok, H. Y., Xue, R., He, D., & Liang, W. (2025). Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration. Marine Environmental Research, 107170.

Shi, Q., Wang, Y., Liu, T., Zhang, L., & Liao, J. (2024). STRL: Writer-Independent Offline Signature Verification with Transformers and Self-Supervised Representation Learning. 2024 10th International Conference on Computer and Communications (ICCC).

Liu, T., Bao, X., Zhang, J., Fang, K., & Feng, H. (n.d.). Enhancing session-based recommendation with multi-interest hyperbolic representation networks. IEEE Transactions on Neural Networks and Learning Systems.

Ms. Lingxiao Qu – kernel method – Best Researcher Award

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Ms. Lingxiao Qu - kernel method - Best Researcher Award

The University of Aizu - China

Author Profile 

ORCID 

🎓 Early academic pursuits

Lingxiao Qu began her academic journey with a strong foundation in computer science and technology, earning her bachelor’s degree from north china electric power university in china between october 2013 and september 2017. during this time, she developed a deep interest in embedded systems and power electronics, which laid the groundwork for her future academic and research pursuits. her academic dedication and performance paved the way for graduate-level opportunities and advanced research training.

👩‍💻 Professional endeavors

Following her undergraduate studies, lingxiao pursued a master’s degree in software engineering at guangxi normal university, china, from october 2018 to september 2021 under the guidance of professor jiang jing. currently, she is completing her ph.d. at the university of aizu, japan, in the graduate school of computer science and engineering, where she has been working under the supervision of professor pei yan since october 2021. throughout her academic trajectory, she has actively contributed to software development projects and collaborative research in power electronics and intelligent systems.

🔬 Contributions and research focus

Lingxiao’s research primarily centers on integrating software engineering with power electronics applications, aiming to enhance the intelligence and efficiency of electronic systems. she has explored machine learning algorithms, optimization techniques, and real-time control strategies to advance the design and deployment of smart power electronic devices. her work demonstrates a profound understanding of cross-disciplinary systems, particularly in how computational methods can elevate power electronics design and operation.

🌍 Impact and influence

Lingxiao Qu’s research contributions have had a growing influence in the field of embedded systems and intelligent control, particularly in applying advanced software engineering approaches to power electronics. her innovative thinking has inspired her peers and collaborators, fostering academic discussions and new explorations within her research group and beyond. her ability to merge theoretical knowledge with practical implementation makes her a valuable contributor to modern electronic system advancements.

📚 Academic cites

Although in the early stages of her research career, lingxiao qu’s scholarly work is gaining academic recognition, especially within the circles of computer science and power electronics. she has been actively engaged in publishing her research findings in international journals and conferences. her work has contributed to evolving academic conversations and citations in areas intersecting computer engineering and smart electrical systems.

🌱 Legacy and future contributions

Lingxiao Qu is poised to become a prominent researcher at the intersection of software engineering and power electronics. her future endeavors are expected to explore green energy applications, smart grids, and ai-powered control systems. with a robust academic foundation and a commitment to innovation, her ongoing and future contributions will likely shape the next generation of intelligent electronic systems. she aims to mentor future researchers and collaborate globally to advance sustainable and efficient electronic technologies.

Notable Publications 

  • Title: A Comprehensive Review on Discriminant Analysis for Addressing Challenges of Class-Level Limitations, Small Sample Size, and Robustness
    Author(s): Lingxiao Qu, Yan Pei
    Journal: Processes

  • Title: A Data Analysis Method Using Orthogonal Transformation in a Reproducing Kernel Hilbert Space
    Author(s): Lingxiao Qu, Yan Pei, Jianqiang Li
    Conference: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

  • Title: Coded Distributed Computing Schemes via Grouping Method
    Author(s): Jing Jiang, Lingling Zhou, Lingxiao Qu
    Conference: Proceedings of the 8th International Conference on Computing and Artificial Intelligence

  • Title: An Approach to Improve Distributed Computing
    Author(s): Jing Jiang, Yiyun Zhong, Lingxiao Qu
    Conference: 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)

  • Title: Cascaded Coded Distributed Computing Schemes Based on Placement Delivery Arrays
    Author(s): Jing Jiang, Lingxiao Qu
    Journal: IEEE Access

Dr. Xiao Guo – Intelligent Control – Best Researcher Award

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Dr. Xiao Guo - Intelligent Control - Best Researcher Award

Beihang university - China

Author Profile

SCOPUS

ORCID

GOOGLE SCHOLAR

Early academic pursuits 🎓

Xiao Guo's journey in the field of aerospace engineering began at beihang university, where he completed his bachelor's degree in aircraft design in 2009. his passion for advancing aircraft technology led him to pursue a ph.d. in the same field at beihang university, which he successfully completed in 2014. during his academic years, xiao developed a keen interest in unmanned aerial vehicles (uavs) and stratospheric airships, laying the groundwork for his future research innovations.

Professional endeavors 🚀

Following his ph.d., xiao guo continued his academic and research career at beihang university as a postdoctoral fellow from 2014 to 2018. he is now an associate professor at the institute of unmanned systems, where he focuses on cutting-edge technologies such as uav design, stratospheric airship development, and advanced path planning algorithms. his work in these areas not only contributes to academic knowledge but also has practical applications in industry.

Contributions and research focus 🔬

Xiao's research focuses on the design and operation of stratospheric airships and the application of reinforcement learning to complex aerospace problems. he has led significant projects funded by the ministry of education, focusing on enhancing the long-term operation of stratospheric airships. through deep reinforcement Intelligent Control learning, he has developed flight planning algorithms that consider complex environmental conditions and mission constraints. his contributions have been widely recognized in the aerospace field.

Accolades and recognition 🏅

xiao Guo’s research excellence is reflected in his impressive body of work. he has published 47 research papers, with 28 indexed by the science citation index (sci) and 19 by the engineering index (ei). his innovative work on airship design and long-term floating capacity maintenance has also earned him a us patent (us 12,116,102  b2). his research papers have been cited 225 times, Intelligent Control showcasing the impact and relevance of his contributions.

Impact and influence 🌍

Through his research, xiao guo has significantly advanced the understanding of stratospheric airships and unmanned systems. his contributions have practical Intelligent Control implications for industries focused on uav and airship technologies, particularly in the areas of flight planning and navigation in challenging environments. his ongoing projects continue to influence both academic research and real-world applications, bridging the gap between theoretical advancements and industrial needs.

Legacy and future contributions 🔮

Xiao Guo’s legacy in aerospace research is characterized by his innovative approach to solving complex problems in aircraft design and unmanned systems. as a member of professional bodies like ieee and aiaa, he actively contributes to the broader scientific community. with several ongoing projects and collaborations, including partnerships with prominent researchers like zewei zheng and jiace yuan, his future contributions will likely push the boundaries of aerospace innovation even further.

Notable Publications 

  • Title: Path planning of stratospheric airship in dynamic wind field based on deep reinforcement learning
    Authors: Zheng, B., Zhu, M., Guo, X., Ou, J., Yuan, J.
    Journal: Aerospace Science and Technology, 2024, 150, 109173
  • Title: Online Learning-Based Surrogate Modeling of Stratospheric Airship Solar Array Output Power
    Authors: Sun, K., Liu, S., Du, H., Liang, H., Guo, X.
    Journal: Aerospace, 2024, 11(3), 232
  • Title: Soft Actor-Critic Based Multi-drones Pursuit-Evasion Differential Game with Obstacles
    Authors: Zhao, C., Deng, Z., Guo, X.
    Journal: Lecture Notes in Electrical Engineering, 2024, 1203 LNEE, pp. 148–161
  • Title: Unsupervised Real Time and Early Anomalies Detection Method for Sewer Networks Systems
    Authors: Qiu, C., Shao, G., Zhang, Z., Guo, X., Guan, X.
    Journal: IEEE Access, 2024, 12, pp. 21698–21709
  • Title: Prescribed-time error-constrained moving path following control for a stratospheric airship with disturbances
    Authors: Sun, L., Sun, K., Guo, X., Yuan, J., Zhu, M.
    Journal: Acta Astronautica, 2023, 212, pp. 307–315