Interests

Current Research Interests

  • Adversarial and distributed machine learning
  • Networked and distributed system security
  • Deep learning theory
  • Applications of machine/deep learning, optimisation, and game theory to IoT and Cyber-Physical Systems (CPS)
  • Electrical power systems: smart grid, renewables, electricity markets
  • Wireless, Cognitive, and Software-Defined Wireless Networks

Research Questions

The fundamental question I aim to address with my research is:
“How do we create more efficient, incentive-compatible, and secure systems?”

Towards this overarching goal, I work on these specific sub-problems:

  • How can we analyse and design systems where multiple participants (as independent decision makers with personal preferences) interact with each other?
    Applications: IoT/CPS, power systems, wireless communications.
    Methods: game theory, distributed optimisation, mechanism design, machine learning

  • How do we better integrate modern computing and machine learning to engineering systems (control, communication, signal processing)?
    Applications: IoT/CPS, power systems, wireless communications.
    Methods: machine/deep learning, distributed optimisation, control, communications, signal processing.

  • How can we make security decisions and manage risks in complex systems in a principled, quantitative, and analytical manner?
    Applications: IoT/CPS, critical infrastructure, defence.
    Methods: game theory, quantitative risk models, machine learning

  • Can we develop a theoretical understanding of Deep Learning?
    Applications: IoT/CPS, control, communications
    Methods: information theory, optimisation, source coding