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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

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!
08/12/2023 :trophy: Our summer intern, William Wei, from Baton Rouge High, won 2nd place in the LSU HSSR Poster Competition. Check our award-winning project!
05/30/2023 :triangular_ruler: Join our summer tutorial series on Machine Learning: Applications and Practices, and Introduction to Meteorology!

Selected Publications

  1. AAAI
    DeFL: Defending Against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness
    In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI) 2023
  2. HPDC
    Libra: Harvesting Idle Resources Safely and Timely in Serverless Clusters
    In Proceedings of the International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2023
  3. KDD
    CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning
    In Proceedings of the ACM Special Interest Group on Knowledge Discovery and Data Mining (KDD) 2023
  4. ASPLOS
    RainbowCake: Mitigating Cold-starts in Serverless with Layer-wise Container Caching and Sharing
    In Proceedings of the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2024

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