Abstract:
Data from populations of systems are increasingly prevalent. Infrastructure continues to be instrumented with sensing systems, emitting streams of telemetry data with complex interdependencies. Data-centric monitoring procedures tend to consider these assets/datasets as distinct – operating in isolation and associated with independent data. In contrast, this work looks to capture the statistical correlations and interdependencies between models that represent groups of systems. Utilizing a Bayesian multilevel approach, the value of data can be extended, since population data can be considered as a whole, rather than constituent parts. In particular, domain expertise and knowledge of the underlying physics have the potential to be encoded within the multilevel structure: at a system, subgroup, or population level – as well as between systems.
Lawrence A. Bull is a research associate in the Engineering Dept. at the University of Cambridge, within the Computational Statistics and Machine Learning group. He researches statistical methods for monitoring telemetry data from systems and infrastructure, working closely with the Cambridge Centre for Smart Infrastructure and Construction (CSIC).
The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: http://www.jlengineer.eu/ml-seminar/