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We are the Intelligent System (IntelliSys) Lab in the Computer Science Engineering Division of Louisiana State University (LSU). Our research bridges machine learning and distributed computing systems by applying innovative machine learning techniques to understand the dynamic performance of distributed computing systems and building new systems with intelligent scheduling algorithms.


News

11/06/2023 :trophy: Our Federated Analytics System project has been funded by the NSF EPSCoR Research Infrastructure Improvement (RII) Track-4: EPSCoR Research Fellows program. This funding will support us to start a two-year collaborative project with IBM Research. Thanks NSF EPSCoR!
10/27/2023 :pencil: Our paper “Self-supervised Crack Detection in X-ray Computed Tomography Data of Additive Manufacturing Parts” has been accepted by NeurIPS 2023 Workshop AI4Mat! Congratulations to Saber Nemati and Seyedeh Shaghayegh Rabbanian!
09/12/2023 :pencil: Our paper “RainbowCake: Mitigating Cold-starts in Serverless with Layer-wise Container Caching and Sharing” has been accepted by ACM ASPLOS 2024! Congratulations to Hanfei, and kudos to our collaborators!
08/21/2023 :trophy: Our Robust Federated Learning project has been funded by the NSF SaTC CORE program. Thanks NSF! —LSU CoE news.
08/14/2023 :trophy: We received the LAMDA Seed Track 1B Collaborative Partnership Award. Thanks Louisiana Board of Regents!

Selected Publications

  1. AAAI
    DeFL: Defending Against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness
    Gang Yan, Hao Wang, Xu Yuan, and Jian Li
    In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI) 2023
    PDF Code
  2. 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
  3. 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
  4. 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

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