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We are the Intelligent System (IntelliSys) Lab in the Computer Science Engineering Division. Our research bridges artificial intelligence (AI) and novel system architectures by designing efficient and robust AI algorithms and developing intelligent and high-performance computing systems. Check our roadmap.


News

04/02/2024 :calendar: We will host TigerAI: LSU/IBM AI security summer boot camp. Sign up before 4/19th 5pm!
03/14/2024 :trophy: We have received another round of AWS Public Sector Cloud Credit for Research program. Kudos to AWS!
02/02/2024 :tada: Dr. Wang received the Distinguished Member of IEEE INFOCOM 2024 TPC recognition.
02/01/2024 :school_satchel: Hanfei has accepted the summer internship from Azure Research @ Microsoft.
01/16/2024 :pencil: Our paper “Backdoor Federated Learning by Poisoning Backdoor-Critical Layers” has been accepted by ICLR 2024. Congratulations to our two remote interns Haomin and Mingxian! Check this project here.
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Selected Publications

  1. HPDC
    Libra: Harvesting Idle Resources Safely and Timely in Serverless Clusters
    Hanfei Yu, Christian Fontenot, Hao Wang, Jian Li, Xu Yuan, and Seung-Jong Park
    In Proceedings of the International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2023
    PDF Code Slides
  2. KDD
    CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning
    Gang Yan, Hao Wang, Xu Yuan, and Jian Li
    In Proceedings of the ACM Special Interest Group on Knowledge Discovery and Data Mining (KDD) 2023
    PDF Code
  3. ASPLOS
    RainbowCake: Mitigating Cold-starts in Serverless with Layer-wise Container Caching and Sharing
    Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, and Seung-Jong Park
    In Proceedings of the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2024
    PDF Code
  4. ICLR
    Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
    Haomin Zhuang, Mingxian Yu, Hao Wang, Yang Hua, Jian Li, and Xu Yuan
    Accepted by the International Conference on Learning Representations (ICLR) 2024
    PDF Code

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