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.


05/16/2023 :pencil: Two papers on Federated Learning are accepted by ACM KDD 2023.
04/18/2023 :pencil: Our paper “Libra: Harvesting Idle Resources Safely and Timely in Serverless Clusters” has been accepted by HPDC 2023!
03/22/2023 :pencil: One paper on Data Privacy Examination is accepted by AsisCCS 2023.
02/28/2023 :trophy: Alexander Randall from KST Charter School, advised by Dr. Wang, won 2nd place in the Louisiana Regional Competition. Check our award-winning project!
02/06/2023 :earth_asia: Dr. Wang will serve as the Publicity Chair of the 23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2023).

Selected Publications

    Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
    Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li
    In IEEE International Conference on Computer Communications (INFOCOM) 2020
  2. WebConf
    Accelerating Serverless Computing by Harvesting Idle Resources
    In Proceedings of the ACM Web Conference (WebConf) 2022
  3. 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
  4. 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
  5. 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