Intelligent Data Analysis (IDA) fuses Data Analytics, Artificial Intelligence and Statistics to solve challenging real-world problems. The Intelligent Data Analysis (IDA) Group was founded in 1994 under Professor Xiaohui Liu at Birkbeck College, University of London. It moved to Brunel University London in 2000 and hosts around 40 members of academic staff, post doctorate research staff and PhD students. The IDA group is a leading centre of excellence for multidisciplinary work involving Artificial Intelligence, Data Science, and Statistics. The work in the IDA group has led not only to novel research results published in many leading journals in the field, but also to effective implementation of applications that have been successfully used in practical settings, especially in biology and medicine. Our research areas of interest Artificial intelligence, Machine learning, Data analysis, Data pre-processing, Text mining, Image Analysis, Deep Learning tools and applications Read our latest news IDA 2022Call For Papers for IDA 2022 is on the new websiteNew PublicationsCongratulations to the following for their recent paper acceptance! Biraja Ghoshal and Allan Tucker, “On Cost-Sensitive Calibrated Uncertainty in Deep Learning: An application on COVID-19 detection” to IEEE CBMS 2021 Seyed Erfan Sajjadi and Allan Tucker, “Exploiting Clinical Staging Data to Constrain Pseudo-Time Modelling of Disease Progression” to IEEE CBMS 2021 Ben Evans and Allan […] Contact us Allan Tucker Email: allan.tucker@brunel.ac.uk Telephone: +44 (0)1895 266 933 Stephen Swift Email: stephen.swift@brunel.ac.uk Telephone: +44 (0)1895 266 934 Sample Research Projects Total number of results: 9 Maintenance models for zero-unexpected-breakdowns Completed Detecting change in medical data to identify bias Ongoing Intelligent data-driven pipeline for the manufacturing of certified metal parts Ongoing Synthetic data generation for primary care data Ongoing A data-oriented predictive ecology approach to modelling fish communities Completed Emotion AI for trading on financial markets Ongoing Personalised disease modelling Ongoing Retinal image analysis Ongoing A deep learning model for global camera trap labelling Ongoing In addition to the above we also conduct research as below: Topological Data Analysis for patient-specific diagnosis (EPSRC) Latent variable modelling of disease progression Joint Baltic Sea Research and Development Programme BONUS (EU) Pseudo time-series trajectory modelling for integrating longitudinal and cross-sectional data Biodiversity informatics for heterogeneous data (Royal Botanical Gardens, Kew) Extreme events in a changing climate: a Big Data perspective (NERC) Deep learning techniques with applications to healthcare data (Royal Society, Royal Academy of Engineering, European Union) A multi-dimensional environment-health risk analysis system for Kazakhstan (British Council) A global canonical image data set for automatic species classification (NERC & Google) Estimating Uncertainty in Deep Learning Classification Advanced algorithm development for big data analysis in social networks (Royal Society, European Union) Optimisation and Drug Manufacturing