Xiang Chen

Xiang Chen

Intelligence Fusion Laboratory

Xiang (Shawn) Chen

计算机学院 副教授

陈翔，师从陈怡然教授，2016年8月获得美国匹兹堡大学的计算机工程专业博士学位。博士毕业后，即在美国乔治梅森大学的计算学部任教并获得终身教职。他的研究工作主要聚焦于高能效移动边缘系统和人工智能计算，并在软硬件协同设计以及全栈平台性能优化等方面取得了杰出的学术成就。在美国任职期间，他获得了美国国家科学基金会颁发的青年职业奖。作为首席研究员，他领导了共计6项美国 NSF 项目，并联合参与了1项NSF和1项空军研究实验室项目。陈翔教授作为第一作者或通讯作者发表论文40余篇，合作发表论文20余篇，均发表在计算系统设计自动化、人工智能系统等领域的国际顶级会议与期刊。其中关于边缘分布式大规模学习的工作获得DATE 2017最佳论文奖，DATE 2022、ICCAD 2016最佳论文提名；关于高能效边缘计算优化的工作获得MLSys 2023最佳论文奖，ASPDAC 2024、DATE 2020最佳论文提名。2023年陈翔教授回国全职加入北京大学计算机学院，入选国家级青年项目计划并获得博雅青年学者称号，在网络与高能效研究所继续相关领域的研究和教学工作。

目前，陈翔教授的主要研究方向为计算系统设计自动化（包括软硬件结合优化、AI 加速系统和体系结构设计、边缘与移动计算等）和人工智能（包括深度学习算法、可信 AI 计算等）。

Associate Professor of Computer Science

Xiang Chen received his Ph.D. degree with the major of computer engineering in 2016 from the University of Pittsburgh, under the guidance of Dr. Yiran Chen. After that, Dr. Chen joined the George Mason University and founded the Intelligence Fusion Lab. His research works focus on mobile computing system, high-performance systems, and artificial intelligence. He has published more than 60 papers, and received the Best Paper Award in MLSys 2023, DATE 2017, and several other competition awards. He has also led multiple research projects funded by NSF, AFRL, Comcast, and Microsoft, and received the NSF CAREER Award in 2022. In 2023, Dr. Chen joined Peking University, Computer Science School.

His research interests include:

  • Computing Systems Design Automation (DES)
    • Hardware Software Co-Design and Co-Optimization
    • Specialized AI System and Architecture Design
    • Edge and Mobile Computing
  • Artificial Intelligence (AI)
    • Deep Learning Algorithms, Systems, and Middleware Designs
    • Trustworthy AI and Reliable AI Systems

WE ARE HIRING

We are looking for self-motivated students to work on Machine Learning System, including efficient AI computing, domain specific architecture, mobile systems, and distributed machine learning.

We now have openings for PhD (to start in Fall 2026) and undergraduate interns.
If you are interested in our research, please contact Prof. Chen via mail.

We prefer students with hands-on system building skills (e.g. C++/CUDA/Python programming, hardware design, etc.) or mathematics background.

Selected Recent Publications

See Full List HERE

  • [ICML ‘24] † C. Xu, F. Yu, Z. Xu, N. Inkawhich, and X. Chen. “Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble,” in Proceedings of the International Conference on Machine Learning, to appear, 2024.
  • [ICASSP ‘24] * P. Xu, Y. Wang, X. Chen, and Z. Tian. “Communication-Efficient Decentralized Dynamic Kernel Learning,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 7135~7139, 2024.
  • [ASP-DAC ‘24] † C. Xu, F. Yu, Z. Xu, C. Liu, J. Xiong, and X. Chen. “QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks,” in Proceedings of the Asia and South Pacific Design Automation Conference, pp. 19~25, 2024.
  • [ICML ‘23] J. Zhang, A. Li, M. Tang, J. Sun, X. Chen, F. Zhang, C. Chen, Y. Chen, and H. Li. “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction,” in Proceedings of the International Conference on Machine Learning, pp. 41354~41381, 2023.
  • [DAC ‘23] † Y. Yu, F. Yu, C. Liu, X. Sheng, and X. Chen. “EagleRec: Edge-Scale Recommendation System Acceleration with Inter-Stage Parallelism Optimization on GPUs,” in Proceedings of the Design Automation Conference, pp. 1~6, 2023.
  • [IEEE-TCAD ‘24] * F. Yu, Z. Xu, L. Shangguan, D. Wang, D. Stamoulis, R. Madhok, N. Karianakis, A. Li, C. Liu, Y. Chen, and X. Chen. “Rethinking Latency-Aware DNN Design With GPU Tail Effect Analysis,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, May 2024.
  • [IEEE-TCCN ‘24] * W. Zhang, Y. Wang, X. Chen, L. Liu, and Z. Tian. “Collaborative Learning based Spectrum Sensing under Partial Observations,” IEEE Transactions on Cognitive Communications and Networking, Apr. 2024.
  • [IEEE-TNNLS ‘23] * P. Xu, Y. Wang, X. Chen, and Z. Tian. “QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM,” IEEE Transactions on Neural Networks and Learning Systems, Sep. 2023.

Awards and Honors

  • Best Paper Award — MLSys (2022)
    The 5th Conference on Machine Learning and Systems
    “QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration”
  • Best Paper Award — DATE (2017)
    The 17th Design Automation and Test in Europe Conference
    “MoDNN: Local Distributed Mobile Computing System for Deep Neural Network”
  • Best Poster Award — MLSys-CrossFL (2022)
    The 5th Conference on Machine Learning and Systems (MLSys) Workshop on Cross Community Federated Learning, 2022.
  • Best Poster Award — ACM-SIGDA-SRF (2015)
    ACM Special Interest Group on Design Automation (SIGDA) Student Research Forum (SRF) Competition, associated with
    The 20th Asia and South Pacific Design Automation Conference (ASP-DAC), 2015.
  • Best Paper Award Nomination — ASP-DAC (2024)
    The 29th Asia and South Pacific Design Automation Conference
    “QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks”
  • Best Paper Award Nomination — DATE (2020)
    The 20th Design Automation and Test in Europe Conference
    “Attention-based Dynamic Optimization for Neural Network Runtime Efficiency”
  • Best Paper Award Nomination — ICCAD (2016)
    The 35th International Conference on Computer-Aided Design
    “Scope: Quality Retaining Display Rendering Workload Scaling based on User-Smartphone Distance”