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. 🚀📈

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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.