Yuanyi Chen | Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Yuanyi Chen — Hainan University, China
Yuanyi Chen
Affiliation Hainan University
Country China
Scopus ID 57564366400
Documents 3
Citations 6
h-index 2
Subject Area Artificial Intelligence
Event Technology Scientists Awards

Yuanyi Chen is a researcher affiliated with Hainan University, China, whose academic work is situated within the field of Artificial Intelligence. Chen is listed as a co-author of research on personalized federated learning, privacy-preserving knowledge alignment, and machine learning, providing a basis for recognition in an artificial intelligence research context. [1][2]

Abstract

Yuanyi Chen is affiliated with Hainan University and works within Artificial Intelligence research. Available scholarly records identify Chen as a co-author of work addressing personalized federated learning and privacy-preserving knowledge alignment. The research demonstrates engagement with contemporary machine learning challenges involving heterogeneous data, privacy protection, personalization, and collaborative model development. [1][2]

Keywords

Artificial Intelligence; Federated Learning; Personalized Federated Learning; Privacy-Preserving Machine Learning; Knowledge Alignment; Machine Learning; Data Heterogeneity; Representation Learning; Privacy Protection. [1]

Introduction

Artificial Intelligence increasingly requires collaborative learning approaches that preserve data privacy while accommodating differences among participating clients. Yuanyi Chen’s research includes personalized federated learning, addressing these challenges through privacy-preserving knowledge sharing and dynamic alignment. This area connects machine learning methodology with practical requirements for decentralized, heterogeneous data environments. [1]

Research Profile

Yuanyi Chen is affiliated with Hainan University, China, and is associated with Artificial Intelligence research. Bibliographic information records three documents, six citations, and an h-index of two in the supplied Scopus profile information. Chen’s identified publication activity includes research in personalized federated learning and privacy-preserving machine learning. [1][2]

Research Contributions

Chen contributed to research on FedPKDA, a personalized federated learning framework designed to combine privacy protection with dynamic knowledge alignment. The study applies feature clipping, Laplacian noise, prototype-based knowledge representation, and Mahalanobis-distance guidance to facilitate privacy-aware cross-client information sharing while maintaining client-specific characteristics under heterogeneous learning conditions. [1]

Publications

A documented publication involving Yuanyi Chen is “FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment,” published in the Proceedings of the AAAI Conference on Artificial Intelligence in 2026. Chen is listed among seven authors, and the article appears in volume 40, issue 33, pages 28113–28121, with DOI 10.1609/aaai.v40i33.40037. [1]

Research Impact

Chen’s available bibliographic indicators show an emerging research profile, with three documents, six citations, and an h-index of two in the supplied Scopus information. The identified publication addresses privacy and personalization in federated learning, an important artificial intelligence research area where reliable knowledge sharing must be balanced against data protection requirements. [1][2]

Award Suitability

Chen’s research profile is aligned with the academic scope of a Best Researcher Award in Artificial Intelligence because the documented work addresses current machine learning challenges through a privacy-aware federated learning framework. The publication record demonstrates participation in peer-reviewed AI research and provides an objective basis for considering Chen within this recognition category. [1][2]

Conclusion

Yuanyi Chen represents an emerging researcher in Artificial Intelligence affiliated with Hainan University. The documented work on personalized federated learning contributes to research addressing privacy, personalization, and heterogeneous data. Current publication and citation indicators provide measurable evidence of scholarly activity and support consideration for recognition in an AI-focused researcher award category. [1][2]

References

  1. 1. Zeng, M., Tu, W., Chen, Y., Wang, Y., Yu, M., Tang, X., & Cheng, J. (2026). FedPKDA: Personalized federated learning with privacy-preserving knowledge dynamic alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 40(33), 28113–28121.
    https://ojs.aaai.org/index.php/AAAI/article/view/40037
  2. 2. Elsevier. (n.d.). Scopus author details: Yuanyi Chen, Author ID 57564366400. Scopus.
    https://www.scopus.com/pages/authors/57564366400

 

Sheng Xiang | AI | Research Excellence Award

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Assoc. Prof. Dr. Sheng Xiang | AI | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Sheng Xiang is an Associate Researcher at Chongqing University, China, specializing in intelligent battery management systems, energy storage analytics, and data-driven prognostics for lithium-ion batteries. His research expertise lies at the intersection of artificial intelligence, machine learning, and electrochemical energy systems, with a particular focus on remaining useful life prediction, state-of-charge estimation, and lightweight deep learning models for real-world battery applications. He has authored 30 peer-reviewed publications, which have collectively received 1,655 citations, reflecting strong international recognition and an h-index of 17. His recent contributions in high-impact journals such as Energy and Journal of Energy Storage demonstrate methodological innovation and practical relevance, especially for electric vehicles and smart energy systems. With an active collaboration network involving 57 co-authors, his work supports interdisciplinary research and global knowledge exchange. The societal impact of his research is evident in its potential to enhance battery safety, efficiency, sustainability, and lifecycle management in next-generation energy technologies.

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Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

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Prof. Dr. Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

Professor | American International University | Bangladesh

Prof. Dr. Muhammad Firoz Mridha, a researcher at the American International University–Bangladesh (AIUB), has established a strong scholarly profile in computer science with notable contributions to machine learning, data analytics, cybersecurity, IoT, and applied artificial intelligence. With 319 publications, over 4,629 citations, and an h-index of 33, his work demonstrates sustained academic productivity and global research impact. His studies often address practical and emerging challenges—such as intelligent decision-support systems, secure digital infrastructures, and data-driven solutions for healthcare and smart environments—positioning his contributions at the intersection of theoretical advancement and real-world application. Collaboration is a defining feature of his career, reflected in partnerships with 575 co-authors, enabling multidisciplinary knowledge exchange and strengthening international research networks. His work has supported technological development, digital inclusion, and innovation-oriented problem-solving, particularly in contexts where data-centric technologies can improve societal outcomes.

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Dr. Leila Malihi | Knowledge Distillation | Research Excellence Award

Research Assistant | Osnabrück University | Germany

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Profile: Scopus

Featured Publication

1. Dührkoop, E., Malihi, L., Erfurt-Berge, C., Heidemann, G., Przysucha, M., Busch, D., & Hübner, U. H. (2024). Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration. In R. Rohrig et al. (Eds.), German Medical Data Sciences 2024.
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Chao Li | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Chao Li | Machine Learning | Best Researcher Award

Department Chair | Chengdu University of Technology | China

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Profile: Scopus

Featured Publications

1. Ke, C.-F., Zu, S.-H., Cao, J.-X., Jiang, X.-D., Li, C., & Liu, X.-Y. (2024). A hybrid WUDT‑NAFnet for simultaneous source data deblending. Petroleum Science, 21(3), 1649‑1659.
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2. Low‑amplitude structure recognition method based on non‑subsampled contourlet transform. Petroleum Science.(2024)
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3. Seismic Data Reconstruction via Least‑Squares Generative Adversarial Networks With Inverse Interpolation. IEEE Transactions on Geoscience and Remote Sensing.(2025)
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Ushba Rasool | Generative AI | Best Researcher Award

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Dr. Ushba Rasool | Generative AI | Best Researcher Award

Research Instructor | Zhengzhou University | China

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Profiles: Scopus | Google Scholar

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1. Rasool, U., Qian, J., & Aslam, M. Z. (2023). An investigation of foreign language writing anxiety and its reasons among pre-service EFL teachers in Pakistan. Frontiers in Psychology, 13, 947867. 
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2. Barzani, S. H. H. (2022). The effects of online supervisory feedback on student-supervisor communications during the COVID-19. European Journal of Educational Research, 11(3), 1569–1579. 
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3. Barzani, S. H. H. (2021). Teachers and students’ perceptions towards online ESL classrooms during COVID-19: An empirical study in North Cyprus. The Journal of Asia TEFL, 18(4), 1423–1431. 
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4. Rasool, U., Mahmood, R., Aslam, M. Z., Barzani, S. H. H., & Qian, J. (2023). Perceptions and preferences of senior high school students about written corrective feedback in Pakistan. SAGE Open, 13(3), 21582440231187612. 
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5. Rasool, U., Aslam, M. Z., Mahmood, R., Barzani, S. H. H., & Qian, J. (2023). Pre-service EFL teachers’ perceptions of foreign language writing anxiety and some associated factors. Heliyon, 9(2), e13705. 
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Tianyuan Liu | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Tianyuan Liu | Machine Learning | Best Researcher Award

Master’s Supervisor | Donghua University | China

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Profiles: Scopus | ORCID | Google Scholar

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1. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.
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2. Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.
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4. Zhou, B., Li, X., Liu, T., Xu, K., Liu, W., & Bao, J. (2024). CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics, 59, 102333.
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5. Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018). A hybrid CNN–LSTM algorithm for online defect recognition of CO₂ welding. Sensors, 18(12), 4369.
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Assoc. Prof. Dr. Tianyuan Liu’s research bridges artificial intelligence and industrial engineering, advancing smart, explainable, and human-centric manufacturing solutions that empower global industry transformation.

Yaqin wu | Natural Language Processing | Best Researcher Award

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Ms. Yaqin Wu | Natural Language Processing | Best Researcher Award

Lecturer | Shanxi Agricultural University | China

Ms. Yaqin Wu is a dedicated researcher with specialized expertise in acoustic signal analysis, deep learning, and multimodal information fusion. Yaqin is adept in Python, MATLAB, MySQL, and Linux systems. Their academic and project experience spans both voice signal processing and intelligent animal behavior monitoring. Yaqin has led and participated in several impactful projects, including the development of an automatic pathological voice disorder detection system (2022–2023), a MATLAB-based ideological education initiative, and a master’s thesis on pathological voice restoration algorithms, which involved advanced techniques like multi-tone signal processing and speech synthesis. They contributed to AVS audio codec development and handled multiple modules including G.729 codec optimization. Notably, Yaqin is involved in pioneering multimodal deep learning projects for health and behavior monitoring in livestock, combining audio and video data to detect issues like coughing and feeding irregularities. Their work has also extended to calf diarrhea behavior detection using asynchronous multimodal fusion. Recognized for academic excellence and leadership, Yaqin has received multiple honors, including the “Outstanding Achievement Award” for their master’s thesis, first prizes in science and mathematics competitions, and numerous scholarships and commendations across institutions. To date, Yaqin Wu has published 9 documents, received 80 citations, and holds an h-index of 3, reflecting a growing impact in the fields of signal processing and intelligent monitoring systems.

Profile: Scopus

Featured Publication

1. GBNF-VAE: A pathological voice enhancement model based on gold section for bottleneck feature with variational autoencoder.
Cited by: 2

Minoru Sasaki | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Minoru Sasaki | Artificial Intelligence | Best Researcher Award

Organizing Committee | Gifu University | Japan

Prof. Dr. Minoru Sasaki, a distinguished Emeritus Professor at Gifu University, has made significant contributions to the fields of mechanical engineering, control systems, and mechatronics throughout his academic and professional career. With a Ph.D. in Mechanical Engineering from Tohoku University (1985), he has held various academic positions in Japan and internationally, including visiting professorships at UCLA, Georgia Institute of Technology, and King Mongkut’s University of Technology Thonburi. He has also served in numerous leadership roles at Gifu University, such as Department Chair, Assistant President, and Director of the Career Center. His professional affiliations include IEEE Life Senior Member, ASME, JSME, SICE (Fellow), RSJ, JSASS, and others. He has actively contributed to global academic and research communities through editorial roles in prestigious journals and program committees of international conferences. His involvement extends to advisory roles and leadership positions within key engineering societies in Japan and abroad. A prolific researcher, Dr. Sasaki has authored 202 publications, which have been cited by 966, reflecting a strong academic impact with an h-index of 14. These metrics highlight the depth and relevance of his research in intelligent mechanical systems and applied electromagnetics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Taheri, S. M., Matsushita, K., & Sasaki, M. (2017). Virtual reality driving simulation for measuring driver behavior and characteristics. Journal of Transportation Technologies, 7(02), 123.
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2. Takayama, K., & Sasaki, M. (1983). Effects of radius of curvature and initial angle on the shock transition over concave and convex walls. Report of the Institute of High Speed Mechanics, 46, 1–30.
Cited by 66.

3. Yoshida, T., Sasaki, M., Ikeda, K., Mochizuki, M., Nogami, Y., & Inokuchi, K. (2002). Prediction of coal liquefaction reactivity by solid state 13C NMR spectral data. Fuel, 81(11-12), 1533–1539.
Cited by 64.

4. Endo, T., Sasaki, M., Matsuno, F., & Jia, Y. (2016). Contact-force control of a flexible Timoshenko arm in rigid/soft environment. IEEE Transactions on Automatic Control, 62(5), 2546–2553.
Cited by 61.

5. Takeda, K., Sasaki, M., Kieda, N., Katayama, K., Kako, T., Hashimoto, K., … (2001). Preparation of transparent super-hydrophobic polymer film with brightness enhancement property. Journal of Materials Science Letters, 20(23), 2131–2133.
Cited by 56.

Mohammad Amin | Gradient boosting | Young Scientist Award

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Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.