Applied Data Analytics Research and Enterprise Group

Organization profile

Profile Information

Applied Data Analytics REG image credit Clint Adair on UnsplashVast amounts of data are generated every day: from the text and visual content placed online by users and providers, to innumerable IoT data streams and the click trails and other collateral data emanating from users’ interaction with the online world, we need better methods to make sense of, manage, organise, visualise, utilise and monetise data.

The common aim in all data analytics is to turn data into information, and ultimately knowledge, that can be used to add value to businesses, help people lead healthier lives at all ages, optimise manufacturing processes, and enrich user experiences.

The Applied Data Analytics Research Group’s expertise is grounded in new and established methods and tools from computer science, statistics and mathematics including a wide range of machine learning and statistical modelling techniques. Building on these are more specialised approaches for the analysis of textual, image and structured numerical data. We have worked with a wide range of specific data types, including social media posts, free-text user comments, medical images, user-generated photographs, geolocation data, corporate and financial data, product lifecycle and PPS data, food labels, genomic data and medical data. This expertise in data methods and data types is complemented by specialisation in three application domains: (1) Health (BSMS: Ford, Cassell, Bremner, van Marwijk; PABS: Smith; CEM: Belz, Touloumis); (2) Business intelligence (UoB SPPO: Mullick; CEM: Chernov, Touloumis); and (3) Manufacturing processes (CEM: Song, Belz, Wang).

Group leader: Professor Anya Belz, Professor of Computer Science in the School of Computing, Engineering and Mathematics at the University of Brighton

Group members at the University of Brighton:

Dr Alexey Chernov, Senior Lecturer

Professor Colin Smith, Professor of Functional Genomics

Mr Ran Song, Senior Lecturer

Dr Anestis Toulmoumis, Senior Lecturer

Dr Yan Wang, Principal Lecturer

Group members at Brighton and Sussex Medical School:

Dr Stephen Bremner, Senior Lecturer in Medical Statistics 

Professor Jackie Cassell, Head of the Department of Primary Care and Public Health & Director of Knowledge Exchange

Dr Elizabeth Ford, Lecturer in Research Methodology

Professor Harm Van Marwijk, Professor in General Practice

Fingerprint Dive into the research topics where Applied Data Analytics Research and Enterprise Group is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

Milling (machining) Engineering & Materials Science
Lead screws Engineering & Materials Science
Remanufacturing Mathematics
Process planning Engineering & Materials Science
Energy utilization Engineering & Materials Science
Machine tools Engineering & Materials Science
Energy Consumption Mathematics
Machine Tool Mathematics

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Projects 2017 2021

Research Output 2015 2019

  • 18 Article
  • 7 Conference contribution with ISSN or ISBN
  • 1 Anthology
  • 1 Editorial

A dynamic information transfer and feedback model for reuse-oriented redesign of used mechanical equipment

Wang, H., Jiang, Z., Zhang, H. & Wang, Y., 4 May 2019, Procedia CIRP. Elsevier, Vol. 80. p. 15-20 6 p. (Procedia CIRP).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Open Access
Machine tools
Energy utilization

Analytical modeling of temperature distribution in lead-screw whirling milling considering the transient un-deformed chip geometry

He, Y., Liu, C., Wang, Y., Li, Y., Wang, S., Wang, L. & Wang, Y., 6 May 2019, In : International Journal of Mechanical Sciences. 157-158, p. 619-632 14 p.

Research output: Contribution to journalArticle

Lead screws
Milling (machining)
Temperature distribution
temperature distribution

An analytical model for predicting specific cutting energy in whirling milling process

He, Y., Wang, L., Wang, Y., Li, Y., Wang, S., Wang, Y. & Liu, C., 28 Aug 2019, In : Journal of Cleaner Production. 240, 118181.

Research output: Contribution to journalArticle

Milling (machining)
Analytical models
Ball screws
Sustainable development