Cambridge Big Data, University of Cambridge
The Cambridge Big Data Strategic Research Initiative brings together researchers from across the University to address challenges presented by our access to unprecedented volumes of data.
|Address:||Cavendish Laboratory, J.J. Thomson Avenue, Cambridge|
|Membership type:||Cambridge University Department|
Our research spans all six Schools of the University, from the underlying fundamentals in mathematics and computer science, to applications ranging from astronomy and bioinformatics, to medicine, social science and the humanities
In parallel, our research addresses important issues around law, ethics and economics, in order to apply Big Data to solve challenging problems for society.
Cambridge Big Data supports collaboration and knowledge transfer in this growing field.
Staff and Students at the University of Cambridge can sign up to the Strategic Research Initiative here.
If you do not work at the University and wish to be kept informed of relevant external events and news, please sign up here.
Modern technology allows for the collection of immense volumes of data. The challenge of converting these data into useful and actionable information is an activity known as data science, or “Big Data”.
Big data has captured the world’s attention, with talk of a new Industrial Revolution based on information, and of data being one of the 21st century’s most valuable commodities. The University of Cambridge has begun a month-long focus on research that uses, produces and interrogates huge datasets.
2 June 2015Read in full
On 3rd December 2014 CSaP ran the first of its Data Science and Policy workshops, entitled "Big Data and Policy".
22 December 2014Read in full
20 May 2019 - 21 May 2019Centre for Mathematical Sciences, Wilberforce Road, Cambridge
The development of machine learning programming languages is critical to support the research and deployment of ML solutions as data-size and model-complexity grow. This workshop aims to bring together researchers from both academia and industry, to discuss recent advances and challenges in machine learning languages development and research.