User:Mcsharry
Patrick McSharry | |
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| Born | 4 April, 1972 |
| Nationality | Irish  |
| Alma mater | University of Dublin |
| Scientific career | |
| Fields | Science |
| Institutions | University of Oxford |
| Website | www.mcsharry.net |
Patrick E. McSharry (born 4 April, 1972) is a Royal Academy of Engineering/EPSRC Research Fellow at the University of Oxford, UK and is most noted for his work on nonlinear time series analysis and forecasting. He is the author of Everyday Numbers and Advanced Methods And Tools for ECG Data Analysis and teaches Nonlinear Dynamics and Chaos theory at the University of Oxford.
He employs a multidisciplinary approach to modelling a wide range of complex dynamical systems. He is a Royal Academy of Engineering/EPSRC Research Fellow at the Department of Engineering Science, University of Oxford, a Research Associate at St Catherine’s College, a Research Associate at the Said Business School, a Visiting Research Fellow at the Department of Statistics, London School of Economics and a Senior Member of the Institute of Electrical and Electronics Engineers.
He is a co-investigator on the BBSRC/EPSRC grant for the recently founded Oxford Integrative Systems Biology Centre (OCISB) and is an active member of the Complex Agent-Based Dynamic Networks (CABDyN) cluster. His current projects include biomedical applications of nonlinear signal processing (Royal Academy of Engineering and EPSRC), evaluation of probabilistic forecasts (6th Framework of the European Union), flood risk prediction (NERC award) and data-mining for decision-making (Spanish Ministry for Science). He regularly presents his research at international conferences and has published over 50 peer-reviewed articles and two books. He founded the Systems Analysis, Modelling and Prediction (SAMP) group, which provides an online forum for multidisciplinary research and gives a cross-referenced account of the overlap between techniques and applications.
His research takes a multidisciplinary approach, drawing on techniques from engineering, mathematics, physics and statistics, to modelling complex dynamical systems. Typical characteristics include non-normal distributions, nonlinear interactions and spatio-temporal dynamics. The general aim is to construct parsimonious data-driven models from empirical observations. Information about the underlying dynamics, such as existing first-principles' models or conservation laws, are employed to constrain the model class. This approach uses a diverse range of techniques from areas including time series analysis, signal processing, forecasting, operations research, data-mining and machine learning.
This research has many practical applications in quantifying risk and uncertainty for decision-making. Applications include biomedical engineering (prediction and classification of risk from disorders such as epilepsy, heart disease, asthma and diabetes); systems biology (gene networks, [[chemical reactions|biochemical reaction networks); decision science (automated financial trading strategies, energy and weather forecasting, risk management, healthcare resource planning); speech modelling (signal processing, pathology classification); and the social sciences (optimised policy-making and the analysis of human development indicators).