Privacy, Inference and Communications Laboratory (Π-CoLab) is a diverse group of interdisciplinary researchers bridging the gamut between theory and practice in privacy and security (Project 1), information theory (Project 2), wireless communications (Project 3 and Project 4), and learning theory (Project 5).
We are hiring! Multiple Ph.D., M.Sc., and undergraduate research positions with financial support are available. Prospective students are encouraged to read about ongoing projects on this website and send an email to: fshirani@fiu.edu

    Research Projects:


    Network Privacy: This project which focuses on quantifying internet users' privacy risks is a collaborative effort between FIU's Π-CoLab, 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. Recent significant contributions include [4] and [5].

    Multiterminal Information Theory: This project studies the fundamental limits of communication over networks. This is a collaboration between FIU's Π-CoLab and University of Michigan's EECS Department We consider distributed data storage as well as data transmission over interference and broadcast channels. 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].


    Feature Selection and Function approximation: This project constructs practical feature selection and function approximation algorithms with applications in machine learning and pattern recognition and derives the fundamental limits of feature selection in terms of classification accuracy. The project builds upon prior work in [6] and [7].

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 privacy and security (Project 1), information theory (Project 2), wireless communications (Project 3 and Project 4), and learning theory (Project 5).
We are hiring! Multiple Ph.D., M.Sc., and undergraduate research positions with financial support are available. Prospective students are encouraged to read about ongoing projects on this website and send an email to: fshirani@fiu.edu

Research Projects:


  • Network Privacy: This project which focuses on quantifying internet users' privacy risks is a collaborative effort between FIU's Π-CoLab, 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. Recent significant contributions include [4] and [5].


  • Multiterminal Information Theory: This project studies the fundamental limits of communication over networks. This is a collaboration between FIU's Π-CoLab and University of Michigan's EECS Department We consider distributed data storage as well as data transmission over interference and broadcast channels. 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].



  • Feature Selection and Function approximation: This project constructs practical feature selection and function approximation algorithms with applications in machine learning and pattern recognition and derives the fundamental limits of feature selection in terms of classification accuracy. The project builds upon prior work in [6] and [7].