Associate Professor, Southwestern University of Finance and Economics, China
Dr. Chen Xingyan is an esteemed Associate Professor at the School of Computer and Artificial Intelligence, Southwestern University of Finance and Economics. He holds a Ph.D. in Engineering from Beijing University of Posts and Telecommunications 🎓. His research spans generative AI applications, large language models, distributed computing networks, multimedia communication, and reinforcement learning 🤖. With over 30 publications in top-tier journals and conferences, including IEEE INFOCOM, IEEE TMC, IEEE TMM, and IEEE TCSVT, Dr. Chen has made significant contributions to advancing AI-driven networked systems. He has led multiple national and provincial research projects, making him a distinguished figure in AI and computing research.
Publication Profile
🎓 Education
Dr. Chen Xingyan earned his Ph.D. in Engineering from Beijing University of Posts and Telecommunications, where he specialized in AI-driven multimedia communication and networked computing. His academic journey has been marked by excellence in research, leading to impactful contributions in distributed AI and reinforcement learning applications 📡.
💼 Experience
Dr. Chen is currently an Associate Professor at Southwestern University of Finance and Economics, where he actively engages in cutting-edge research and mentorship. He has been a principal investigator for several prestigious projects, including the NSFC Youth Fund and Sichuan Provincial Natural Science Fund. His consultancy work with leading tech firms like Huawei and China Electronics Technology Group further highlights his industry influence. Additionally, Dr. Chen has played a pivotal role in research projects related to 5G streaming, blockchain-based cloud computing, and immersive video transmission 🎥.
🏆 Awards and Honors
Dr. Chen has been recognized for his groundbreaking research with several prestigious grants and awards. He has received funding from the National Natural Science Foundation of China and the Sichuan Provincial Science and Technology Department. His expertise in AI and multimedia systems has earned him notable accolades in academia and industry 🏅.
🔬 Research Focus
Dr. Chen’s research is centered on generative AI, multimedia communication, federated learning, and reinforcement learning. His work on immersive video transmission, cloud-edge computing, and blockchain-enhanced computing frameworks has been widely cited and influential. He continues to innovate in the field, developing AI-driven methodologies for large-scale distributed networks and next-generation communication systems 🌐.
🔚 Conclusion
Dr. Chen Xingyan stands at the forefront of AI-driven computing and multimedia systems, making substantial contributions through innovative research and industry collaborations. His work in AI, distributed computing, and multimedia communication has not only advanced theoretical knowledge but also influenced practical applications in 5G, blockchain, and federated learning. With a strong research portfolio, prestigious awards, and impactful industry partnerships, Dr. Chen continues to shape the future of AI-powered networked systems 🚀.
🔗 Publications
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling. IEEE Transactions on Computers. [Cited by 15] 🔗
A Novel Adaptive 360° Livestreaming with Graph Representation Learning-based FoV Prediction. IEEE Transactions on Emerging Topics in Computing. [Cited by 12] 🔗
A Federated Transmission Framework for Panoramic Livecast with Reinforced Variational Inference. IEEE Transactions on Multimedia. [Cited by 20] 🔗
A Multi-user Cost-efficient Crowd-assisted VR Content Delivery Solution in 5G-and-beyond Heterogeneous Networks. IEEE Transactions on Mobile Computing. [Cited by 18] 🔗
A Universal Transcoding and Transmission Method for Livecast with Networked Multi-Agent Reinforcement Learning. IEEE INFOCOM. [Cited by 25] 🔗
Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration. IEEE Transactions on Circuits and Systems for Video Technology. [Cited by 22] 🔗
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks. IEEE Transactions on Knowledge and Data Engineering. [Cited by 30] 🔗
BC-Mobile Device Cloud: A Blockchain-Based Decentralized Truthful Framework for Mobile Device Cloud. IEEE Transactions on Industrial Informatics. [Cited by 17] 🔗
Differential Privacy Oriented Distributed Online Learning for Mobile Social Video Prefetching. IEEE Internet of Things Journal. [Cited by 19] 🔗
Optimal Information Centric Caching in 5G Device-to-Device Communications. IEEE Transactions on Circuits and Systems for Video Technology. [Cited by 23] 🔗
BC-MetaCast: A Blockchain-enhanced Intelligent Computing Framework for Metaverse Livecast. IEEE Network. [Cited by 14] 🔗