Yonghuang Wu | Computer Science | Research Excellence Award

Research Excellence Award

Yonghuang Wu
Fudan University, China

Researcher Information
Affiliation Fudan University
Country China
Scopus ID 58151097300
Documents 5
Citations 80
h-index 2
Subject Area Computer Science
Event Global Scholar Award

The Research Excellence Award recognition profile for Mr. Yonghuang Wu presents an academic overview of his scholarly activities and research engagement in the field of Computer Science. Affiliated with Fudan University, China, Mr. Wu has contributed to research outputs indexed within internationally recognized academic databases. His publication metrics, citation record, and interdisciplinary involvement reflect continued participation in contemporary computational and scientific investigations.[1][3]

Abstract

This article documents the academic profile and scholarly indicators associated with Mr. Yonghuang Wu of Fudan University. The profile is based on publicly accessible bibliometric information, including indexed publications, citation performance, and subject specialization within Computer Science.[1][2]

Keywords

Computer Science; Scholarly Communication; Citation Analysis; Research Metrics; Academic Recognition; Bibliometrics.[1]

Introduction

Academic recognition systems frequently assess researchers through publication quality, citation performance, research consistency, and scholarly contribution.[2][4]

Fudan University provides a strong academic environment supporting interdisciplinary and computational research initiatives. Mr. Yonghuang Wu has participated in scholarly activities associated with internationally indexed research dissemination.[1][4]

Research Profile

Mr. Yonghuang Wu is affiliated with Fudan University in China and is associated with research activities in Computer Science. According to available bibliometric records, his scholarly profile includes five indexed documents with eighty citations and an h-index of two.[1]

Bibliometric indicators such as citations and h-index are widely used to evaluate research visibility and scholarly engagement within international academic systems.[2]

Research Contributions

The research contributions associated with Mr. Wu demonstrate participation in computational investigations and peer-reviewed academic dissemination relevant to Computer Science.[1]

Collaborative scientific publication contributes to methodological advancement, interdisciplinary communication, and scholarly knowledge exchange.

Publications

The publication portfolio associated with Mr. Yonghuang Wu includes research outputs indexed within internationally recognized academic databases. Such indexed publications contribute to scholarly visibility and scientific dissemination.[1]

Research Impact

Research impact is frequently evaluated through citation metrics, academic engagement, and contribution to scholarly communication networks. Citation activity associated with Mr. Wu reflects measurable research dissemination.[1][2]

Award Suitability

The documented publication record, citation metrics, and indexed scholarly contributions align with common evaluation criteria applied within international research recognition programs such as the Global Scholar Award.[2][4]

Conclusion

This article presents a structured academic overview of Mr. Yonghuang Wu and his measurable scholarly indicators associated with Fudan University and the field of Computer Science. The available bibliometric data reflect continued participation in scholarly communication and indexed academic dissemination.[1][3]

References

  1. Elsevier. (n.d.). Scopus author details: Mr. Yonghuang Wu, Author ID 58151097300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58151097300
  2. Li, Y., Wu, Y., Luo, Y., Sun, L., Qin, Z., Qiu, L., Cao, X., & Cai, X. (2025). Instance-level randomization: Toward more stable LLM evaluations. In Findings of the Association for Computational Linguistics: EMNLP 2025.
    https://doi.org/10.18653/v1/2025.findings-emnlp.182
  3. ORCID. (n.d.). ORCID profile record for Mr. Yonghuang Wu. ORCID Registry.
    https://orcid.org/0000-0002-5804-5573
  4. Global Scholar Awards. (n.d.). International academic recognition and scholarly evaluation framework.
    https://globalscholarawards.com/

Bin Liu | Computer Science | Research Excellence Award

Prof. Bin Liu | Computer Science | Research Excellence Award

Professor | Northwest A&F University | China

Prof. Bin Liu is a researcher at Northwest A&F University, Yangling, China, with expertise in artificial intelligence, computer vision, agricultural informatics, and large-scale model training. He has published 69 Scopus-indexed documents, receiving approximately 2,949 citations and achieving an h-index of 18, reflecting sustained academic impact. His recent work focuses on multi-source data fusion, multimodal learning, remote sensing change detection, and efficient parallel training pipelines for large models, with publications in reputable venues such as IEEE Transactions on Computers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and Applied Sciences. Liu has collaborated with over 140 co-authors, demonstrating strong interdisciplinary and international research engagement. His research contributes to societal needs by advancing intelligent agricultural disease diagnosis, improving crop monitoring, and enhancing the efficiency of large-scale AI systems, supporting sustainable agriculture and data-driven environmental management.

Citation Metrics (Scopus)

2949
2200
1500
700
0

Citations

2,949

Documents

69

h-index

18

Citations

Documents

h-index

View Scopus Profile
View Scopus Profile

Featured Publications


MDS-Net: An image-text enhanced multimodal dual-branch Siamese network for remote sensing change detection


– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025

PRT: An efficient pipeline reuse technology for large models training


– IEEE International Conference on Cluster Computing (CLUSTER), 2025

VMF-SSD: A novel V-space based multi-scale feature fusion SSD for apple leaf disease detection


– IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023

Aleeza Adeel | Computer Science | Research Excellence Award

Mrs. Aleeza Adeel | Computer Science | Research Excellence Award

The University of Waikato | New Zealand

Mrs. Aleeza Adeel is a Ph.D. student at the School of Computing and Mathematical Sciences, University of Waikato, New Zealand, specializing in digital twin frameworks, sustainable energy systems, and user-centered computing solutions. Her research focuses on developing interoperable and scalable digital twin technologies to optimize energy system management, enhance operational efficiency, and support sustainable resource utilization. She has contributed to peer-reviewed publications, including a recent article in Energies on an interoperable user-centered digital twin framework, demonstrating her commitment to integrating advanced computational models with real-world energy systems. Aleeza collaborates with interdisciplinary researchers, including experts in energy management and computational modeling, to ensure her work addresses both technical rigor and societal relevance. Her research contributes to sustainable energy transitions by providing data-driven, user-centric solutions that improve system performance, reduce environmental impact, and support informed decision-making in complex energy infrastructures.

Profile: View ORCID Profile 

Featured Publication


An Interoperable User‑Centred Digital Twin Framework for Sustainable Energy System Management

– Adeel, A., Apperley, M., & Walmsley, T. G., Energies, 2026, 19(2), Article 333

Miroslaw Kozielski | Computer Science | Best Researcher Award

Mr. Miroslaw Kozielski | Computer Science | Best Researcher Award

Kazimierz Wielki University | Poland

Mr. Mirosław Kozielski is a researcher at Kazimierz Wielki University in Bydgoszcz, Poland, specializing in computer science, with a strong focus on natural language processing (NLP), industrial informatics, and Industry 4.0/5.0 technologies. His research addresses the use of intelligent language-based systems for automated industrial documentation, knowledge representation, and digital transformation in modern manufacturing environments. He has authored 7 peer-reviewed publications, which have accumulated 35 citations, and holds an h-index of 3, reflecting a focused and emerging academic impact. Dr. Kozielski collaborates with interdisciplinary teams, contributing to the integration of artificial intelligence with industrial and organizational processes. His work supports the development of efficient, human-centric, and sustainable industrial systems, with societal impact through improved documentation quality, enhanced knowledge accessibility, and the practical adoption of advanced AI-driven solutions in contemporary industrial ecosystems.

Citation Metrics (Scopus)

35
25
15
5
0

Citations

35

Documents

7

h-index

3

Citations

Documents

h-index

View Scopus Profile
View ORCID Profile

Featured Publications