Research Statement

Uncertainty is a key factor in real-world problems and I am interested in intelligent and adaptive systems that can cope with complex and uncertain environments. My research is centered on topics in machine learning (where uncertainty typically resides in the observations and data), communication theory (where uncertainty is due to the transmission medium), and random combinatorial structures (where uncertainty is in the underlying graphical model). The main theme, which unifies these three fields, is information theory. My research focus is on constructing an information theoretic framework to describe the role of uncertainty in data and models. Within such a framework, I develop algorithms by drawing upon probabilistic reasoning, message passing techniques, stochastic optimisation, and approximation algorithms to study questions such as: How can we build systems that acquire the most important information at the lowest cost? How can we design efficient algorithms, often involving large amounts of variables and data, for inference problems? How can we summarise massive amounts of data into a small number of informative representatives and use the smaller set for processing tasks? How can we design practical coding schemes to transfer data inside networks of possibly many individuals? Moreover, I use concepts and techniques from information theory, probability theory, and optimisation to analyse the performance of algorithms and find fundamental trade-offs. My dissertation research was in the fields of information theory and graphical models. I investigate