Yujuan Tan | AI System | Best Researcher Award

Published on by China Scientist

Prof. Dr. Yujuan Tan | AI System | Best Researcher Award 

Professor Yujuan Tan is a full professor at Chongqing University in the College of Computer Science. She received her bachelor’s degree from Hunan Normal University and both her master’s and doctoral degrees from Huazhong University of Science and Technology. Her research focuses on memory and storage systems, including file systems, data deduplication, hybrid memory architectures, and data management for artificial intelligence. She has received multiple awards for her work, including provincial science and technology progress prizes and best paper recognitions at leading international conferences. With extensive experience in academia and industry collaboration, Professor Tan has made significant contributions to advancing efficient, reliable, and intelligent computing systems.

Prof. Dr. Yujuan Tan | Chongqing University | China

Profiles

SCOPUS

GOOGLE SCHOLAR

Education

  • Yujuan Tan pursued her academic journey in the field of computer science. She earned her bachelor’s degree in computer science from Hunan Normal University in Changsha, where she laid the foundation for her expertise in computing. She later advanced to Huazhong University of Science and Technology in Wuhan, where she obtained both her master’s degree and doctoral degree in computer science under the guidance of Professor Dan Feng. These formative years shaped her strong research orientation and set the stage for her contributions to memory and storage systems

Experience

  • Professor Tan has built an extensive career at Chongqing University, starting as an assistant professor, progressing to associate professor, and later rising to the position of full professor in the College of Computer Science. She has also served as a research scholar at the University of Texas at Arlington in the Department of Computer Engineering, where she gained valuable international research experience. Beyond academia, she has collaborated with leading industries such as Huawei Technologies Co., Ltd. as a research consultant, bridging the gap between theoretical advancements and real-world technological applications.

Awards and Recognition

  • Throughout her career, Professor Tan has received several prestigious recognitions for her impactful research. She has been honored with provincial science and technology progress awards for her contributions to non-volatile storage systems and cloud platform technologies. At international conferences, her research has been acknowledged with best paper finalist and best paper nomination awards, showcasing her innovative approaches in file systems and storage optimization. She has also received recognition from esteemed journals, including a popular paper award for her work in federated learning and mobile systems.

Skills and Expertise

  • Professor Tan’s expertise in computer systems, with specialized skills in file system design, storage management, data deduplication, and hybrid memory technologies. She combines deep theoretical understanding with practical application, evident from her collaborations with industry leaders. Her ability to design system-level solutions, optimize performance in complex environments, and apply storage technologies to artificial intelligence systems highlights her technical leadership. She also demonstrates strong skills in interdisciplinary collaboration, project management, and guiding large-scale research initiatives.

Research Focus 

  • Professor Tan’s research interests center on advancing memory and storage systems. She has made significant contributions to the design and optimization of file systems across persistent memory, flash memory, and multi-tiered architectures. Her work in data deduplication has improved efficiency in backup systems and flash caches, while her studies in hybrid memory architectures have focused on enhancing performance through innovative page management and translation lookaside buffer redesign. More recently, her research has extended to data management in artificial intelligence systems, where she develops methods to leverage data sparsity for efficient large language model cache management and neural network acceleration

Publication

  • LAShards: Low-overhead and Self-adaptive MRC Construction for Non-stack Algorithms
    Authors: S. Zhao, Y. Tan, Z. Zeng, J. Yu, Z. Bai, A. Re, X. Chen, Duo Liu
    Journal: IEEE Transactions on Computers

  • CMCache: An Adaptive Cross-Level Data Placement Method for Multilevel Cache
    Authors: Z. Zeng, Y. Tan, Z. Ma, J. Li, S. Zhao, D. Liu, X. Chen, A. Ren
    Journal: IEEE Transactions on Computer–Aided Design of Integrated Circuits and Systems

  • DSAV: A Deep Sparse Acceleration Framework for Voxel-Based 3-D Object Detection
    Authors: H. Fang, Y. Tan, A. Ren, W. Zhuang, Y. Hua, Z. Qin, D. Liu
    Journal: IEEE Transactions on Computer–Aided Design of Integrated Circuits and Systems

  • GNNBoost: Accelerating sampling-based GNN training on large scale graph by optimizing data preparation
    Authors: Y. Tan, Y. Gan, Z. Zeng, Z. Bai, L. Qiao, D. Liu, K. Zhong, A. Ren
    Journal: Journal of Systems Architecture

  • BGS: Accelerate GNN training on multiple GPUs
    Authors: Y. Tan, Z. Bai, D. Liu, Z. Zeng, Y. Gan, A. Ren, X. Chen, K. Zhong
    Journal: Journal of Systems Architecture

Conclusion

  • Professor Yujuan Tan is a distinguished scholar whose career reflects a balance of academic excellence, industrial collaboration, and technological innovation. Her research in memory and storage systems has significantly influenced both academic communities and industrial practices. By addressing challenges in file systems, hybrid memory, and data-driven artificial intelligence applications, she continues to shape the future of computer science. With her strong leadership, award-winning research, and innovative vision, Professor Tan remains a key figure in advancing the frontiers of computing and data management.