My current research covers four main related areas: complex systems, data interpretation, sensor networks, and blended approaches to learning. I’m interested both in the core computer science of these areas, but also increasingly in how they work across the traditional disciplinary boundaries.
Complex systems. I’m especially interested using complex networks to model real-world phenomena like disease epidemiology and flooding. In particular I’m interested in adaptive and coupled networks, where several independent networks are coupled together somehow so that they affect each others’ behaviours, and whose connections can change in response to events. As part of this work I’m writing a textbook to make the subject more accessible to computer scientists.
Sensor analytics and interpretation. Sensor data provides a picture of the real world, but one that’s limited and error-prone. Interpreting the data is very challenging, and I work extensively on situation and activity recognition techniques that fuse multi-model data together to draw conclusions about what’s happening in the area under observation.
Sensor networks. Sensors collect data, and sensor networks allow very precise and long-lived observations to be made of phenomena that are otherwise hard to observe. I work on error management to reduce noise and other problems in the data stream. I also look at sensor network software design, especially building scientific-standard systems using off-the-shelf components so that experimental groups can deploy more and better sensing capabilities.
Blended learning. As a university we currently serve the a cohort of “digital natives” who’ve grown up with the internet and digital technology. How does this change teaching? How do we use technology to improve learning, to make students more capable of being engaged and creative citizens? I’m experimenting with blended approaches to my own teaching, with some slightly surprising results.
All this research is supported by publications of various kinds. I’m also Director of St Andrews’ Institute for Data-Intensive Research (IDIR) that brings together all the university’s research in data science, digital humanities, and related areas.
You can find a discussion of previous/less current research interests here.