Privacy, Inference and Communications Laboratory (Π-CoLab) is a diverse group of interdisciplinary researchers bridging the gamut between theory and practice in stastistical learning theory (Project 1) privacy and security (Project 2), information theory (Project 3), and wireless communications (Project 4 and Project 5).
Prospective students are encouraged to read about ongoing research projects on the Π-CoLab website and send an email to: fshirani@fiu.edu
    Research Projects:


    Explainability and Fariness in Graph Neural Networks: This project develops an information theoretic framework for explainability and fairness in graph neural networks, and involves the co-design of GNN archiectures and explanation mechanisms. This is a collaborative effort between FIU's Π-CoLab, FIU's XAI-Lab, and several external collaborators. The project is partially supported by NSF grant #CCF-2241057, and builds upon our recent work titled `Factorized Explainer for Graph Neural Networks ' [1] and ` ' [2].


    Network Privacy and Security: This project focuses on quantifying internet users' privacy risks, and is a collaborative effort between FIU's Π-CoLab, FIU's Sec-Lab, NDSU's CS Department, and NYU's NYU Wireless and NYU CCS. The project is supported by NSF grant #CCF-1815821 titled `An Information Theoretic Framework for Web Privacy'. Recent significant contributions include [1], [2], and [3].

    Wireless Communications: This project focuses on wireless communications, and consists of two subprojects, i) energy efficient communication over millimeter wave networks, and b) resource allocation in cellular systems. The project is supported by NSF grant #CCF-2242700 titled `Towards Energy-Efficient Millimeter Wave Wireless Networks: A Unified Systems and Circuits Framework'. Recent significant contributions include [4] and [5].

    Multiterminal Information Theory: This project studies the fundamental limits of data compression, communication, and randomness generation over networks. This is a collaboration between FIU's Π-CoLab and University of Michigan's EECS Department. The project is supported by NSF grant #CCF-2132843 titled `A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers'. The contributions are summarized in a recently published book [6]. Other more recent contributions include [7] and [8].

Π-CoLab
 Privacy, Inference,
and Communications Laboratory

Privacy, Inference and Communications Laboratory (Π-CoLab) is a diverse group of interdisciplinary researchers bridging the gamut between theory and practice in stastistical learning theory (Project 1) privacy and security (Project 2), information theory (Project 3), and wireless communications (Project 4 and Project 5).
Prospective students are encouraged to read about ongoing research projects on the Π-CoLab website and send an email to: fshirani@fiu.edu
Research Projects:



  • Explainability and Fariness in Graph Neural Networks: This project develops an information theoretic framework for explainability and fairness in graph neural networks, and involves the co-design of GNN archiectures and explanation mechanisms. This is a collaborative effort between FIU's Π-CoLab, FIU's XAI-Lab, and several external collaborators. The project is partially supported by NSF grant #CCF-2241057, and builds upon our recent work titled `Factorized Explainer for Graph Neural Networks ' [1] and `Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks' [2].


  • Network Privacy and Security: This project focuses on quantifying internet users' privacy risks, and is a collaborative effort between FIU's Π-CoLab, FIU's Sec-Lab, NDSU's CS Department, and NYU's NYU Wireless and NYU CCS. The project is supported by NSF grant #CCF-1815821 titled `An Information Theoretic Framework for Web Privacy'. Recent significant contributions include [1], [2], and [3].


  • Wireless Communications: This project focuses on wireless communications, and consists of two subprojects, i) energy efficient communication over millimeter wave networks, and b) resource allocation in cellular systems. The project is supported by NSF grant #CCF-2242700 titled `Towards Energy-Efficient Millimeter Wave Wireless Networks: A Unified Systems and Circuits Framework'. Recent significant contributions include [4] and [5].


  • Multiterminal Information Theory: This project studies the fundamental limits of data compression, communication, and randomness generation over networks. This is a collaboration between FIU's Π-CoLab and University of Michigan's EECS Department. The project is supported by NSF grant #CCF-2132843 titled `A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers'. The contributions are summarized in a recently published book [6]. Other more recent contributions include [7], [8].