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We are the Intelligent System (IntelliSys) Lab at the ECE Department of Stevens Institute of Technology. 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

09/26/2024 :pencil: Our paper “Pre-Warming is Not Enough: Accelerating Serverless Inference with Opportunistic Pre-Loading” has been accepted by ACM SoCC 2024. Congratulations to our intern Yifan!
08/29/2024 :pencil: Our paper “Freyr+: Harvesting Idle Resources in Serverless Computing via Deep Reinforcement Learning” has finally been accepted by IEEE TPDS. Our sincere appreciation to our reviewers and editors!
08/19/2024 :trophy: Our project “SaTC: CORE: Small: Advancing Model Forensics with Systematic Parsing, Injection Detection, and Model Provenance Attribution” have received the NSF SaTC CORE grant. Thanks NSF and my collaborator Dr. Ibrahim (Abe) Baggili!
08/15/2024 :pencil: Our paper “Nitro: Boosting Distributed Reinforcement Learning with Serverless Computing” has been accepted by VLDB 2025! This is the last piece of our trilogy on Serverless Computing for Distributed Reinforcement Learning.
08/08/2024 :city_sunrise: We have moved to Stevens Institute of Technology. Thanks LSU. Hello Hoboken!
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Selected Publications

  1. 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
  2. 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 Slides
  3. 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 Poster
  4. SC
    Stellaris: Staleness-Aware Distributed Reinforcement Learning with Serverless Computing
    Hanfei Yu, Hao Wang, Devesh Tiwari, Jian Li, and Seung-Jong Park
    Accepted by the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) – Best Student Paper Finalist 2024
    PDF

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