PhD Supervision:
Stephen Jacobs - A Robust Examination Question Classifier; using NLP and classification to assess GCSE Physics questions
MSc Project Supervision:
Anthony Bwanali - Predicting Deterioration in Paediatric Time Series; Using Gaussian Process to predict trends in child health signals (Completed)
James Cashmore - Simulating the Impact of Opening a New Store as a Business Decision using Probabilistic Graphical Models; Implementation of PGM and Monte Carlo Simulation to identify optimal business decisions. (Completed; Distinction)
Mitchell Walker - Probabilistic Graphical Modelling of Manufacturing and the Impact of Government Interventions; Implementation of PGM and Monte Carlo Simulation to assess the impact of international lockdown strategies on the car industry. (Completed; Distinction)
Stellah Chonzi - Predicting Deterioration in Paediatric patients: A critical analysis of machine learning methods in forecasting and predicting vital sign deterioration; Implementation of LSTM and Time Series forecasting in predictive healthcare informatics.
Diako Saed - Anomaly Detection of COVID-19 using classification models and comparing lockdown approaches; Analysing multivariate time series to identify anomalies in COVID transmission and death rates. (Completed)
Machine Learning
Deep Learning
Time Series
AI in Healthcare
PhD Mathematics
BSc Mathematics and Business Administration
Machine Learning
Time Series
Learner Analytics
Predictive Healthcare Informatics
Data-driven Visualisation and Understanding
Selected Journal Papers and Letters
I. Rice. Improved data visualisation through multiple dissimilarity modelling. Information Sciences, Vol. 370-371, pp. 288-302, 2016
R. Rice and I. Rice. A different use of Visual Analytic Techniques in Anaesthetics, British Journal of Anasthesia, Vol 118 (5), pp. 801-802, 2017
I. Rice. Γ-SNE for feed-forward data visualisation. Information Visualization, In Press. 2018
I. Rice and D. Lowe. A Decision Support System to Ease Operator Overload in Multibeam Sonar, IEEE Journal of Oceanic Engineering, Vol. PP 99, pp. 1-11, 2018
I. Rice. Improved data visualisation through nonlinear dissimilarity modelling. Pattern Recognition, Vol. 73, pp. 76-88, 2018
A. Chattopadhyay, T. K. Kumar and I. Rice. A social engineering model for poverty alleviation, Nature Communications (11: 6345), pp. 1-9,2020
H. Duncan, B. Fule, I. Rice, A. Stich, D. Lowe. Wireless monitoring and Real-time Adaptive Predictive Indicator of Deterioration, Nature (10), 2020
Selected Conference Papers and Talks
I. Rice and D. Lowe, “A robust, realistic noise model for visualisation and anomaly detection of multi-beam sonar targets,” in 10th IMA Mathematics in Signal Processing, 2014.
I. Rice and D. Lowe, “Deep layer radial basis function networks for classification,” in 10th IMA Mathematics in Signal Processing, 2014.
I. Rice, “Patient Monitoring Through Topographic Information Visualisation,” in The PERCAT Gallery 2015 (prize for best oral presentation).
Patents
RAPID project system for prediction of deteriorations in children, Patent Application Number 1700873.1, under review, 2018.
Knowledge Transfer Network Partnership with Thales (UK) Ltd with PhD focusing on Sonar Data Analysis through Visual Informatics.
Data Scientist for ‘The RAPID Project’ in conjunction with Birmingham Children’s Hospital aiming to predict deterioration of paediatric patients in real time. https://bwc.nhs.uk/news/birmingham-childrens-hospitals-revolutionary-study-aiming-to-improve-monitoring-and-save-lives-reaches-milestone-666/