Data Economy Lab
The essence of the big data revolution comes from the realisation that data is a new form of natural resource. Markets are emerging in which datasets are sold and traded as commodities. Large public datasets are available through government open-source websites such as the US-based www.data.gov or UK-based www.data.gov.uk. In both the private and public sectors, data science is the catalyst for advances in the physical sciences including: astrophysics, particle physics, biology, climate science and meteorology; the human sciences including medicine and healthcare; and the social sciences such as economics, business, and finance, as well as sociology, and political science. The generation of data in each of these fields offers businesses, governments, and non-profit organisations unparalleled opportunities to divine new insights into human behaviour and social dynamics.
At the Data Science Institute, we are carrying out research into the economic, legal and policy mechanisms required for the emerging Data Economy in the UK and worldwide. Working with our colleagues in economics and social science, we are pioneering in the key areas of data economy such as open data business model, digital money and digital service exchanges.
Head, Professor William Knottenbelt
Professor William Knottenbelt is the Professor of Applied Quantitative Analysis in the Analysis, Engineering, Simulation and Optimization of Performance (AESOP) group in the Department of Computing at Imperial.
Data Science Institute research interests include Blockchain Technology, Open Data Infrastructure, Network/Graph Algorithms, Pattern Recognition, Anomaly Detection through Machine Learning, High Performance Computing/GPU clusters, Financial Technology, Data Economy
Dr David Birch
David leads the Data Visualisation group at Imperial's Data Science Institute. This team which designed, constructed and runs the Data Observatory. This facility provides unique capacity to visualise data in an immersive circular visualisation studio. Our goal is to help researchers and industry to make sense from their data.