Research Interests and Areas
Research Interests
My research mainly focuses on applications of distributed decision making, game theory, and control to various security and resource allocation problems in complex networked systems.
- Game, optimization, and control theories.
- Security of networked systems and risk management.
- Mechanism design, pricing, and decision making under limited information.
- Spectrum and resource allocation problems in wired and wireless networks.
- Distributed machine learning and social networking.
Game and Control Theories for Resource Allocation
This research involves analysis, development, and implemention of distributed and scalable pricing, control, and optimization schemes to address various resource allocation and network control problems ranging from congestion and power control to active queue management (AQM) in heterogeneous networks. Game and control theories provide a solid foundation to address a wide variety of research questions in this domain. Specifically, depending on the nature of the problem at hand, stochastic methods (Markov models), game theory, hybrid systems, H-infinity optimal control, and game/mechanism design are among the utilized methods and approaches. Active research directions in this area include decision making under limited information, spectrum sharing and mechanism design, resource allocation in optical and wireless networks, and novel stochastic approaches to control complex nonlinear or chaotic dynamics.
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Security of Complex and Networked Systems
Decision and Game Theoretic Approach
This research encompasses development and analysis of quantitative decision-making for resource allocation problems arising in the context of security. Despite the recent surge of interest in the subject, most of the existing research efforts are heuristic in nature. Utilizing game and -optimal- control theories as a basis, the objective is to develop formal mathematical frameworks to model and solve various security problems. Such mathematical abstractions are useful for generalization of problems, combining the existing ad-hoc schemes under a single umbrella, and facilitating future research. This approach has been successfully applied to topics such as optimal deployment and configuration of malware filters, strategies for (malware) epidemic removal, and allocation of (system administrator) resources for detection and response.
IT and Security Risk Management
Most of the existing approaches to IT and security risk management are ad-hoc and qualitative in nature. Following the decision and game theoretic approach, the objective is to develop quantitative models and decision-making frameworks based on system, game, and optimization theories as well as machine learning. The focus is on large scale organizations and complex relationships between people, business processes, systems, and products.
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Distributed Learning and Social Networks
Adaptive services provided over distributed systems is an increasingly popular research field. Analysis and development of scalable and distributed algorithms for optimal allocation of resources based on pricing, optimization, and game theory constitute one specific direction. Another active research area is the organization and intelligent routing of information utilizing machine learning, information retrieval, and social networking. In a recent project (internal codename Spree), a prototype of a social search system has been developed for information exchange between users of a community. An additional direction is to bring rich information services, similar to those on the Internet, to the physical world through augmented environments and wireless (sensor) networks. By their very nature, these research threads integrate diverse areas including optimization, machine learning, and usability.
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