Hongchen Wu | Computer Science | Best Researcher Award

Prof. Hongchen Wu | Computer Science | Best Researcher Award

Associate Professor at Shandong Normal University, China.

Dr. Hongchen Wu 🌏💻 is an associate professor in the School of Information Science & Engineering at Shandong Normal University. After earning his Ph.D. in Computer Science & Technology in 2016—supplemented by a two‑year joint Ph.D. stay at the University of California, Irvine—he has built a vibrant career at the crossroads of next‑generation Internet, information security, and AI‑driven data science. Wu leads multiple national and provincial projects on cross‑domain recommendation, privacy management, and online‑payment fraud, publishing widely in Neurocomputing, Information Processing & Management, IEEE Access, and other high‑impact venues. A committee member of the China Computer Federation (CCF) and active reviewer for top IEEE Transactions titles, he blends rigorous theory with real‑world impact—pushing the envelope on fake‑news detection, multimodal content analysis, and privacy‑aware personalization. Outside the lab, Wu mentors students, collaborates globally, and champions ethical AI practices, making him a dynamic force in contemporary computer science. 🚀📈

Professional  Profile:

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Suitability For Best Researcher Award – Prof. Hongchen Wu

Dr. Hongchen Wu exemplifies the qualities of an outstanding researcher whose contributions span both theoretical innovation and real-world application. His research seamlessly integrates AI, information security, and digital ethics to address urgent challenges in privacy, fraud detection, and misinformation. His active leadership in prestigious national and international projects, high-impact publications, and dedication to mentorship make him a highly suitable candidate for the Best Researcher Award.

🎓 Education:

Wu completed his B.Eng. and M.Eng. at Shandong University 🏫 before obtaining his Ph.D. in Computer Science & Technology there in 2016. Thanks to a prestigious exchange program, he spent 2013‑2015 at UC Irvine, USA 🌎, sharpening his expertise in networked systems and machine learning. This bicultural training equipped him with a global view of AI ethics, security, and large‑scale data processing. 🧑‍🎓🔗

🚀 Professional Development :

Since 2017, Wu has served as Principal Investigator on projects funded by the National Natural Science Foundation of China and the Shandong Provincial Key R&D Plan. These initiatives—covering cross‑platform privacy mining, emotional contagion modeling, and payment‑fraud risk analytics—have yielded deployable prototypes and policy recommendations for e‑commerce stakeholders. Within the CCF, he helps steer the Service Computing Technical Committee, organizing workshops that connect academia and industry. As a meticulous peer reviewer for IEEE TCYB, TNNLS, and Information Sciences, he advances scholarly quality while staying abreast of frontier research. Wu also champions open‑source culture, supervising student hackathons and offering guest lectures on reproducible AI. Together, these activities reflect a career trajectory marked by leadership, mentorship, and continuous upskilling. 🛠️📚✨

🔍 Research Focus:

 Wu’s lab explores privacy‑conscious AI and trustworthy media analytics. Key threads include (1) 🤖 Deep‑learning architectures for multimodal fake‑news detection—fusing text, imagery, and voice to flag disinformation early; (2) 🔒 Cross‑domain recommender systems that balance personalization with minimal privacy intrusion through adaptive default settings; (3) 💳 Behavior‑aware fraud prediction for online payments, leveraging temporal event graphs and sentiment drift; (4) 🧠 Behavioral analytics in educational platforms to support adaptive tutoring. By uniting computational linguistics, computer vision, and behavioral science, Wu delivers end‑to‑end frameworks that are both explainable and scalable. The overarching ambition: create a safer, more transparent digital ecosystem without sacrificing user experience. 🌐⚖️

🏆 Awards & Honors:

 Wu’s leadership has been recognized through consecutive NSFC Young Scientists Awards for outstanding PIs 🥇, a Shandong Provincial Science‑and‑Technology Progress Excellence Citation 🌟, and multiple “Outstanding Reviewer” certificates from IEEE and Elsevier journals 📜. His projects on privacy‑aware recommendation earned a Top‑Ten Innovation Achievement nod at the 2022 Shandong Digital Economy Expo 🏅, while his teaching excellence garnered a university‑level Mentor of the Year award 🎖️. Collectively, these accolades highlight his dual impact on scientific discovery and community service. 👏

Publication Top Notes

1. Multimodal Fake News Detection via Progressive Fusion Networks
  • Authors: J. Jing, H. Wu, J. Sun, X. Fang, H. Zhang

  • Journal: Information Processing & Management

  • Volume/Issue: 60 (1)

  • Article Number: 103120

  • Year: 2023

  • Citations: 155

  • Summary: This paper presents a progressive fusion network approach to detect fake news by integrating multimodal data sources (e.g., text, images). The proposed framework captures both fine-grained and high-level correlations across modalities to improve detection accuracy.

2. Matrix Factorization for Personalized Recommendation with Implicit Feedback and Temporal Information in Social E-Commerce Networks
  • Authors: M. Li, H. Wu, H. Zhang

  • Journal: IEEE Access

  • Volume: 7

  • Pages: 141268–141276

  • Year: 2019

  • Citations: 31

  • Summary: This work enhances traditional matrix factorization techniques for recommendation systems by integrating users’ implicit feedback and temporal behaviors within social e-commerce platforms.

3. NSEP: Early Fake News Detection via News Semantic Environment Perception
  • Authors: X. Fang, H. Wu, J. Jing, Y. Meng, B. Yu, H. Yu, H. Zhang

  • Journal: Information Processing & Management

  • Volume/Issue: 61 (2)

  • Article Number: 103594

  • Year: 2024

  • Citations: 27

  • Summary: The paper introduces NSEP, a model designed for early fake news detection by perceiving the semantic environment surrounding the news content. The framework captures contextual cues from related articles to support early-stage detection.

4. Div-Clustering: Exploring Active Users for Social Collaborative Recommendation
  • Authors: H. Wu, X. Wang, Z. Peng, Q. Li

  • Journal: Journal of Network and Computer Applications

  • Volume/Issue: 36 (6)

  • Pages: 1642–1650

  • Year: 2013

  • Citations: 20

  • Summary: This study proposes Div-Clustering, a method that leverages active users’ social influence and clustering behavior to enhance collaborative filtering in recommendation systems.

5. Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services
  • Authors: H. Wu, M. Li, H. Zhang

  • Journal: IEEE Access

  • Volume: 7

  • Pages: 50031–50040

  • Year: 2019

  • Citations: 17

  • Summary: The paper presents a smart anonymity scheme to enhance security and privacy in location-based services, allowing secure collaboration among users without revealing sensitive information.

Conclusion

Dr. Hongchen Wu is an exemplar of 21st-century research excellence. His interdisciplinary innovation, societal relevance, global collaboration, and academic integrity firmly position him as a deserving recipient of the Best Researcher Award. His work not only advances the frontiers of computer science but also addresses some of the most pressing technological challenges facing society today.

Fei Li | Next-generation informatic | Leading Research Award

prof. Fei Li | Next-generation informatic | Leading Research Award

Research scientist Institute of Grassland Research, Chinese Academy of Agricultural Sciences China

Dr. Fei Li is a prominent research professor at the Institute of Grassland Research, Chinese Academy of Agricultural Sciences (CAAS). Specializing in the remote sensing of grassland ecology and big data, Dr. Li leverages satellite and UAV remote sensing along with AI algorithms to advance ecological research. With over 40 academic papers published in top-tier journals, Dr. Li’s contributions have significantly impacted the field of ecological and biological sciences.

 

Profile

Scopus

Education 🎓

Dr. Li earned his Ph.D. in Cartography and Geographic Information Systems from the University of Chinese Academy of Sciences in 2014. Prior to this, he completed his M.S. in Cartography and Geographic Information Systems and his B.S. in Geographic Information Systems from Northwest Normal University in 2009 and 2006, respectively.

Experience 🏆

Dr. Li’s extensive experience includes his current role as a research professor at CAAS since 2020. He previously served as a research assistant at the University of Tennessee, Michigan State University, and the Chinese Academy of Sciences. His diverse experience in both academic and research institutions has equipped him with a robust understanding of ecological processes and remote sensing technologies.

Research Interests 🔬

Dr. Li’s research interests lie in integrating ecological process models with remote sensing observations and machine learning approaches. He focuses on simulating global-regional carbon-water cycles and investigating their response mechanisms. Additionally, he is dedicated to utilizing big data from remote sensing for effective grassland resource monitoring and management.

Awards 🏅

Dr. Li has been recognized with numerous awards and grants from prestigious organizations such as NSF, NASA, DOE, NSA, and ESA. His groundbreaking work in remote sensing and ecological modeling has earned him accolades and funding for various high-impact projects.

Publications Top Notes 📚

Dr. Li has an impressive portfolio of publications, including:

Li, H., Li, F.*, Xiao, J., Chen, J., Lin, K., Bao, G., … & Wei, G. (2024). A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of Environment, 311, 114317. Cited by 10 articles.

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Li, F., Xiao, J., Chen, J., Ballantyne, A., Jin, K., Li, B., … & John, R. (2023). Global water use efficiency saturation due to increased vapor pressure deficit. Science, 381(6658), 672-677. Cited by 25 articles.

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Yan, H., Li, F.*, & Liu, G. (2023). Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland α-diversity metrics. Frontiers in Ecology and Evolution, 11, 154. Cited by 15 articles.

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Ouyang, Z., Sciusco, P., Jiao, T., Feron, S., Lei, C., Li, F., … & Chen, J. (2022). Albedo changes caused by future urbanization contribute to global warming. Nature Communications, 13(1), 3800. Cited by 30 articles.

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Brabazon, H., DeBruyn, J. M., Lenaghan, S. C., Li, F., Mundorff, A. Z., Steadman, D. W., & Stewart Jr, C. N. (2020). Plants to Remotely Detect Human Decomposition?. Trends in Plant Science, 25(10), 947-949. Cited by 20 articles.

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