Networks, Inference and Complex Systems

Our understanding of complex systems remains primitive. Whether we are seeking insights into artificial neural networks or deciphering brain functionality, a cohesive grasp of these systems is essential now more than ever. Unlike the late 20th century, when methods were first motivated to address these convoluted problems, many previously intractable tasks are becoming feasible because of modern computational power and the volume of available data.

Network theory has proven to be one of the most advantageous and successful methods for understanding complex systems. In this project, we seek to develop models and inference techniques for the analysis of these underlying structures. Data itself may be drawn from a variety of domains, ranging from epidemiology to neuroscience.

In turn, methods will be drawn from statistics, machine learning, physics and applied mathematics, with the ambition to develop algorithms that analyse disparate systems, whilst understanding their theoretical limits.

Relevant areas of research: Mathematical Physics, Statistics and Applied Probability, Artificial Intelligence Technologies, Non-Linear Systems, Digital Signal Processing.