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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

11/17/2025 :pencil: One papers has been accepted by TPDS. Congratulations to Yifan and Hanfei!
11/07/2025 :pencil: Two papers have been accepted by AAAI’26. Congratulations to Zixun and Hanxi!
10/03/2025 :trophy: Alice Agnoletto, our undergraduate RA, has received the Provost’s Office Undergraduate Research Fund! Kudos to the Provost’s Office.
09/26/2025 :pencil: Our paper “Multi-Agent Reinforcement Learning with Serverless Computing” has been accepted by SoCC 2025. Congratulations to Ricky!
08/23/2025 :pencil: Our paper “Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading” has been accepted by EuroSys 2026. Find our arXiv paper here.
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Selected Publications

  1. 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
  2. ICLR
    Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
    Haomin Zhuang, Mingxian Yu, Hao Wang, Yang Hua, Jian Li, and Xu Yuan
    In Proceedings of the International Conference on Learning Representations (ICLR) 2024
    PDF Code Poster
  3. SC
    Stellaris: Staleness-Aware Distributed Reinforcement Learning with Serverless Computing (Best Student Paper Finalist)
    Hanfei Yu, Hao Wang, Devesh Tiwari, Jian Li, and Seung-Jong Park
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) 2024
    PDF Code Slides
  4. EuroSys
    Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading
    Hanfei Yu, Xingqi Cui, Hong Zhang, Hao Wang@Rutgers, and Hao Wang
    Accepted by ACM European Conference on Computer Systems (EuroSys) 2026
    PDF

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