Researcher
Arrykrishna Mootoovaloo
He designs statistical and machine learning models that turn complex, noisy data into reliable decisions, grounded in years of research in probabilistic machine learning and Bayesian inference at the University of Oxford and Imperial College London.
Experience
- Fuse Energy
- QRT
- Huawei
- University of Oxford
Education
- Imperial College London
- University of Cape Town
Focus areas
Probabilistic machine learning
Normalising flows, Gaussian Processes and principled uncertainty quantification.
Deep learning and fast inference
Deep learning pipelines, diffusion models and emulators that make expensive computations fast.
Quantitative research
Hedging models, signal detection and rigorous backtesting for energy and financial markets.
AI research
Generative models for image editing, multi-task learning and few-shot learning, from industrial R&D to academic research.
Recent publications
All publications →-
2026
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2024
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2024
Recent writing
All posts →-
Fast joint analyses with normalising flows.
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Running AI on phones and edge devices: compilation and quantisation.
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A Gaussian Process emulator for the power spectrum and its gradients.
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Compressing weak lensing data with MOPED and emulating it with GPs.