My broad research interest lies in the understanding and management of Complex Networked Systems (CNS) that naturally arise in a variety of real-world settings, such as efficient resource allocation in communication networks, distributed control of cyber-physical systems (CPS), the intelligent operation of social networks, the design of autonomous medical telemetry systems, dynamic navigation in vehicular networks, etc.

The ideal that drives my research efforts is the well-founded development of efficient, robust, adaptable, and scalable algorithms that achieve provably good performance despite the stochastic and highly complex dynamics of the underlying networks. To that end, I take a theoretically well- founded and inter-disciplinary approach geared towards the development of generic methods and principles that are applicable to the aforementioned wide range of application areas.


A significant portion of my research in the pursuit of my broad objectives is aimed at the modeling, analysis, and design of large scale wireless & backbone communication networks. My research activities in this direction can be organized into four topics:

  • Foundations (e.g. see [J1, J2, J5, J6, J9, J20, J27, J31, J32]), which aim at establishing an increasingly more comprehensive framework for stochastic network optimization and architecture design for communication networks;

  • Algorithm Design (e.g. see [J3, J8, J11, J13, J14, J17-19 J26, J30]), which aims at developing low-complexity, low-overhead, scalable, and provably efficient algorithms that carry out the principles and strategies emanating from the theoretical foundations;

  • Performance Analysis (e.g. see [J2, J4, J5, J7, J9, J15, J16, J28, J29]), which aims at the rigorous mathematical analysis of proposed algorithms to establish performance guarantees and to reveal the limits of their applicability;

  • Expanding the Frontiers (e.g. see [J10, J12, J21, J22]), which aim at expanding the framework by incorporating innovative network coding techniques at the physical-level and by exploiting predictability of behavior at the user-level.