Computational Statistics and Machine Learning Section

Background

The Computational Statistics and Machine Learning Section (formerly known as the Statistical Computing Section) supports interest in all aspects of the use of computational science and technology in modern statistical analysis. We are interested in the fundamental design of software and systems for statistical computing and in the computational issues that are an essential part of modern statistics and data science. Competent statisticians and practitioners must understand the principles on which statistical methods work and be able to implement them in ways that are safe, transparent, reproducible and scale well with large and complex data sets.

Computational issues are an essential part of modern statistics, data analysis and data mining. Statisticians and practitioners must not just understand the principles on which statistical methods work but also be able to implement them on computers in ways that are transparent, require minimal user intervention and scale well with large data sets. The section is also interested in the fundamental design of software and systems for statistical computing.

Remit

  • Act as a national focus for statisticians involved in theoretical and practical aspects of computational statistics and machine learning
  • To foster interaction with related data-intensive scientific communities, e.g. information engineering, computer science, data science and machine learning
  • Promote a statistically rigorous approach to the development of models and algorithms within computational statistics and machine learning
  • Organise meetings for the promotion and dissemination of research and developments in statistical computing, computational statistics, and machine learning
  • Promote best practice and standards in the design and development of computing systems that support the work of statisticians and machine learners, both in terms of systems and software, for statistical processing, documentation and analysis of data.

Officers

Chair: Theo Kypraios (Discussion meetings representative)
Vice chair: Chris Nemeth
Secretary: Rebecca Killick
Meetings secretary: Ben Powell

Committee members

Louis Aslett
Adam Johansen
Alex Lewin
Chris Oates
Ben Powell
Trevor Ringrose (co-opted)
Camille Szmaragd
Richard Tomsett
Catalina Vallejos
Christopher Yau (co-opted)

Future meetings

View our events calendar: statslife.org.uk/computational-statistics-and-machine-learning-section

CSML Network

The Computational Statistics and Machine Learning (CSML) network is part of the Statistical Computing Section of the RSS, with a focus on supporting and developing the UK's significant strength in CSML. The group organises training and development opportunities for researchers working in the joint area of CSML, as well as highlighting internationally recognised research activity for exploitation within industry, policy making and other scientific research domains.

Forum: CSML Network

Additional information

Annual Report 2018