Dr. Nianyun Song | Crowdsensing – Student at Beijing Normal University, China
Nianyun Song is an emerging scholar in the fields of artificial intelligence, privacy-preserving computation, and collaborative sensing technologies. Despite currently pursuing academic training, she has made remarkable contributions to the intersection of AI security and trust-centric systems. Her interdisciplinary foundation in applied mathematics and computer science equips her with both theoretical depth and practical insight. With a growing portfolio of scholarly publications, patents, and leadership roles in high-impact projects, she is rapidly establishing herself as a promising researcher dedicated to ethical and secure AI development.
Profile:
Education:
Nianyun has cultivated a strong academic background rooted in quantitative and computational disciplines. Her studies integrate applied mathematics with core areas of computer science, enabling her to address contemporary challenges in AI and data security with rigor and precision. This multidisciplinary approach has supported her innovative research in model obfuscation, privacy-preserving inference, and secure collaborative sensing.
Experience:
Nianyun’s research experience spans multiple national, provincial, and municipal research projects, where she has served in leadership and technical roles. Notably, she is a core member and student lead of the national-level “New Generation AI 2030” initiative, focusing on cloud-edge-end collaborative multimodal sensing. She has also contributed to a provincial-level project on intelligent network scheduling and led a city-level innovation initiative involving voice-interactive systems. Her industry engagement is exemplified by her leadership in the “Yi Lu Tong Xing” project, where theoretical models were successfully implemented in real-world delivery optimization platforms, resulting in a granted patent. She has presented her work at major conferences and participated in collaborative research teams under the Ministry of Science and Technology.
Research Interests:
Nianyun’s research interests focus on enhancing trust and security in AI ecosystems. Her core areas include AI security, privacy-preserving inference using secure multi-party computation (MPC), model watermarking and verification, and crowdsensing under trust and willingness constraints. Her work on intellectual property protection in neural networks addresses pressing concerns in model integrity and ownership. She also explores real-time, trust-aware team recruitment algorithms to optimize collaborative sensing, contributing significantly to both theoretical advancement and practical applications in AI deployment.
Awards:
While formal awards are still forthcoming, Nianyun’s achievements are reflected in her invitations to present at prestigious forums such as IWQoS 2024 and her selection as a reviewer for prominent journals. Her leadership in awarded research grants and her early success in patent innovation demonstrate the recognition her work has already begun to receive in academic and professional circles.
Publications 📚:
Nianyun has published a total of nine research papers, with four currently under review and five already published in respected journals and conferences. Notable publications include:
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🧠 “Multi-view Trust based Team Recruitment for Collaborative Crowdsensing”, Information Sciences (2025) – cited by several applied AI systems in trust modeling.
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🎯 “Cheating Recognition in Examination Halls Based on Improved YOLOv8”, AIoTSys 2024 Conference Proceedings – integrates deep learning in surveillance contexts.
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🚸 “Deep Reinforcement Learning-based Panic Crowd Evacuation Simulation”, CSCWD 2023 – cited in crowd management and emergency response systems.
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🤝 “Team Recruitment of Collaborative Crowdsensing under Joint Constraints of Willingness and Trust”, International Journal of Intelligent Systems (2023) – widely referenced in trust-aware computing literature.
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📡 “Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing” – influential in the development of team optimization algorithms in distributed sensing.
Conclusion:
Nianyun Song exemplifies the rare combination of academic excellence, innovation, and early leadership that makes her an ideal candidate for the Best Researcher Award. Her work pushes the boundaries of what secure, ethical, and collaborative AI can achieve, particularly in open and distributed environments. With her growing publication record, patent success, and participation in national AI missions, she demonstrates the potential for transformative contributions to future AI systems. As she continues to grow as a scholar and innovator, she is well on the path to becoming a leading voice in trustworthy artificial intelligence research.