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Researcher Details
 
Name: Professor M Girolami
Organisation: University of Cambridge
Department: Engineering
Current EPSRC-Supported Research Topics:
Artificial Intelligence Control Engineering
Digital Signal Processing Energy - Nuclear
Fluid Dynamics Human-Computer Interactions
Image & Vision Computing Information & Knowledge Mgmt
Multiphase Flow Numerical Analysis
Statistics & Appl. Probability

Current EPSRC Support
EP/V056522/1 Advancing Probabilistic Machine Learning to Deliver Safer, More Efficient, and Predictable Air Traffic Control(C)
EP/T000414/1 PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE)(C)
EP/R018413/2 Semantic Information Pursuit for Multimodal Data Analysis(P)
EP/P020720/2 Inference, COmputation and Numerics for Insights into Cities (ICONIC)(P)
EP/R034710/1 CoSInES (COmputational Statistical INference for Engineering and Security)(C)
EP/R004889/1 Delivering Enhanced Through-Life Nuclear Asset Management(C)
Previous EPSRC Support
EP/R018413/1 Semantic Information Pursuit for Multimodal Data Analysis(P)
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/J016934/2 Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications(P)
EP/K009788/2 Network on Computational Statistics and Machine Learning(P)
EP/K015664/2 ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research(P)
EP/K009788/1 Network on Computational Statistics and Machine Learning(P)
EP/K034154/1 Enabling Quantification of Uncertainty for Large-Scale Inverse Problems (EQUIP)(C)
EP/J016934/1 Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications(P)
EP/K011839/1 RCUK CENTRE for ENERGY EPIDEMIOLOGY (CEE): the study of energy demand in a population.(C)
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/H024875/2 Cross-Disciplinary Feasibility Account : Computational Statistics and Cognitive Neuroscience(P)
EP/F009429/2 Advancing Machine Learning Methodology for New Classes of Prediction Problems(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)
Key: (P)=Principal Investigator, (C)=Co-Investigator, (R)=Researcher Co-Investigator