Planned Activities

Our future growth is limited only by resources. With sufficient support, we would like to:

  • Locate in a purpose-build physical space, with facilities for hosting staff, students and visitors, and for computing, collaboration, hosting workshops, and visualization;
  • Provide more support for the Director through partial buyout;
  • Appoint a Centre Manager;
  • Appoint a second Data Science Fellow to staff a new programme in LSE Data Science Surgeries and proto-type Incubator model. The innovative data science incubator model pioneered by the University of Washington and New York University’s Centre for Data Sciences enables cross-disciplinary progress by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects, frequently involving a non-trivial software engineering or methodological component presenting practical obstacles for social scientist researchers. The incubator model offers researchers from any field the chance to learn through collaboration with data science experts how to surmount these obstacles, not by outsourcing these tasks but by helping the domain experts actively learn the technical solutions through the project incubation process. The incubator programme will consist of two dedicated data science fellows hosted at the LSE but drawn from the DSI, available full time to assist with the challenges of large-scale data processing, linking data from different sources, and using high-performance computing resources of ICL, including big data processing and analysis. A third social science research fellow with data science experience will be hosted at the DSI, to engage data scientists based at Imperial College;
  • Operate an inter-disciplinary seminar series, drawn from speakers from Imperial, LSE, and externally;
  • Host a set of workshops for researchers, to learn new techniques in data science and computation;
  • Host visiting scholars and those on sabbatical, from partner institutions, other universities, or industry;
  • Issue “challenge calls” for innovative solutions, including “hackathon” events to solve specific problems or contests to come up with innovative solutions for targeted questions – an innovative solution-oriented approach called for in the recent FinTech Futures report;
  • Organise data policy “trials”, for prototyping innovative policy solutions and working with civil society organizations and entrepreneurs involved in innovative data practice;
  • Promote outreach and education. A key activity of the Centre will be outreach to practitioners and industry partners, forming a core part of its rationale in contributing knowledge to policy in the data economy and information society. This outreach will maximize the impact of the Centre’s research, with a multiplier effect of spreading awareness of social big data and data science issues through education, seminars in methodology and data management, continuing education and executive education, along with potential revenue-generating activities for sustainability and fostering partnerships with industry and government. We have two new MSc programmes commencing in 2017 and another one from 2018. See the Study page for further information.

Current priorities:

  • The Department of Statistics is pushing the boundaries in developing new statistical and algorithmic methods in the areas of machine learning, statistical learning for complex and dependent data, social statistics and risk management, and complex time series analysis. Specific areas of research include change point detection, functional regression, computation-statistical trade-offs, and scalable algorithms for processing streaming data, as well as data with a combinatorial structure such as social graph data.
  • The Department of Mathematics is developing new solutions in the areas of combinatorial optimisation, financial mathematics, game theory and operations research. Academics have expertise in: analysing the sample complexity of machine learning, addressing how much data is required for reliable conclusions; the development of novel machine learning methods, including those based on various notions of ‘definitive’ classification; and analysis and development of fast algorithms – including sub-linear algorithms for tasks such as property testing.
  • The Department of Methodology focuses on the application of social research methods, which include innovative qualitative approaches to novel data, rather than being restricted to just applied statistics and computational methods. The department hosts world renowned experts who research and teach on topics as diverse as measurement problems, event forecasting, computational text analysis, the structure and design of causal inference, and experimental and quasi-experimental methods for digital ethnography and surveying. Its world-leading, methodologically innovative research covers applications such as social media, election analysis, criminology, political competition, health policy, and urban studies.
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