We are the Intelligent System (IntelliSys) Lab at the ECE Department of Stevens Institute of Technology. Our research focuses on bridging artificial intelligence (AI) and emerging system architectures by developing efficient, robust algorithms and advancing intelligent, high-performance computing systems. Supported by multiple NSF grants, as well as funding and credits from AWS, NIH AIM-AHEAD, and the Computing Research Association (CRA), our work spans theory, algorithms, and real-world systems. Explore our projects.


News

11/05/2026 :flags: Our proposal for the IEEE INFOCOM 2027 Workshop on Embodied Intelligence Networks (EIN 2027) has been accepted for INFOCOM 2027. We look forward to a successful workshop in Honolulu, Hawaii!
11/05/2026 :mortar_board: Congratulations to Yifan Sui, who successfully passed his Ph.D. defense at Shanghai Jiao Tong University (SJTU)!
10/22/2026 :bookmark: Dr. Wang will serve as the Area Chair of ICML 2027.
10/21/2026 :trophy: Congratulations to Qingyang Yu on receiving up to $1,000 from the Stevens Graduate Conference Funding to present “Bayes-pFCL: Bayesian Personalized Federated Continual Learning” at NeurIPS 2026!
10/21/2026 :trophy: Our project “Enabling RL-based Agentic LLM Post-training with Serverless Computing” (NAIRR260186) has been funded by the NAIRR Pilot program, with $5,000 in CloudBank cloud credits and 2,500 GPU hours on NCSA Delta!
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Selected Publications

  1. EuroSys
    Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading
    In Proceedings of ACM European Conference on Computer Systems (EuroSys) 2026
  2. 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
  3. AAAI
    iSeal: Encrypted Fingerprinting for Reliable LLM Ownership Verification
    In Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) 2026
  4. ICML
    GFedCL: Graph-Based Federated Continual Learning with Spatial and Temporal Awareness
    Accepted by International Conference on Machine Learning (ICML) 2026

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