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| Name: |
Professor M Girolami |
| Organisation: |
Imperial College London |
| Department: |
Dept of Mathematics |
| Current EPSRC-Supported Research
Topics: |
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Analytical Science
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Biophysics
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Digital Signal Processing
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Energy - Nuclear
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Energy Efficiency
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Ground Engineering
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Image & Vision Computing
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Information & Knowledge Mgmt
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Non-linear Systems Mathematics
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Numerical Analysis
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Statistics & Appl. Probability
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| Current EPSRC Support |
| EP/R034710/1 | CoSInES (COmputational Statistical INference for Engineering and Security) | (C) |
| EP/R018413/1 | Semantic Information Pursuit for Multimodal Data Analysis | (P) |
| EP/R004889/1 | Delivering Enhanced Through-Life Nuclear Asset Management | (C) |
| EP/P020720/1 | Inference, COmputation and Numerics for Insights into Cities (ICONIC) | (P) |
| EP/J016934/3 | Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications | (P) |
| EP/L014165/1 | In Situ Nanoparticle Assemblies for Healthcare Diagnostics and Therapy | (C) |
| EP/K034154/1 | Enabling Quantification of Uncertainty for Large-Scale Inverse Problems (EQUIP) | (C) |
| EP/K011839/1 | RCUK CENTRE for ENERGY EPIDEMIOLOGY (CEE): the study of energy demand in a population. | (C) |
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| Previous EPSRC Support |
| EP/J016934/2 | Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications | (P) |
| EP/K015664/2 | ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research | (P) |
| EP/K009788/2 | Network on Computational Statistics and Machine Learning | (P) |
| EP/K009788/1 | Network on Computational Statistics and Machine Learning | (P) |
| EP/J016934/1 | Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications | (P) |
| EP/K015664/1 | ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research | (P) |
| EP/J007617/1 | A Population Approach to Ubicomp System Design | (C) |
| EP/F009429/2 | Advancing Machine Learning Methodology for New Classes of Prediction Problems | (P) |
| EP/H024875/2 | Cross-Disciplinary Feasibility Account : Computational Statistics and Cognitive Neuroscience | (P) |
| EP/E032745/2 | The Molecular Nose | (C) |
| EP/E052029/2 | The Synthesis of Probabilistic Prediction & Mechanistic Modelling within a Computational & Systems Biology Context | (P) |
| EP/H024875/1 | Cross-Disciplinary Feasibility Account : Computational Statistics and Cognitive Neuroscience | (P) |
| EP/F009429/1 | Advancing Machine Learning Methodology for New Classes of Prediction Problems | (P) |
| EP/E052029/1 | The Synthesis of Probabilistic Prediction & Mechanistic Modelling within a Computational & Systems Biology Context | (P) |
| EP/E032745/1 | The Molecular Nose | (C) |
| EP/C010620/1 | Stochastic Modelling and Statistical Inference of Gene Regulatory Pathways: Integrating Multiple Sources of Data | (C) |
| GR/R55184/02 | Data mining Tools for Fraud Detection in M-Commerce - DETECTOR | (P) |
| GR/R55184/01 | Data mining Tools for Fraud Detection in M-Commerce - DETECTOR | (P) |
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Key: (P)=Principal Investigator, (C)=Co-Investigator, (R)=Researcher Co-Investigator
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