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Preprints and Publications

Actually sparse variational Gaussian processes
Jake Cunningham, So Takao, Mark van der Wilk, Marc Deisenroth

NeurIPS workshop on Gaussian Processes, Spatiotemporal Modelling, and Decision-Making Systems (2022)

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Invited Talks

The Euler Equations. A Coincidence or Genius?
SIAM-JAMS (2017)

Networks of Coadjoint Orbits. Bridging the gap between geometric and statistical mechanics.
Imperial SIAM Student Chapter Annual Conference (2018)

Modelling Uncertainty in the Ocean. A Geometric Perspective.
SIAM-JAMS (2019)

Extending the Generalised HMC to Lie Groups and Beyond.
Geometric Science of Information Conference (2019)

Geometric Framework for Stochastic GFD Modelling.
Nonlinear and Stochastic Methods in Climate and GFD, Climate and GFD, Institute Henri Poincare (2019)

Generalised Hamiltonian Monte Carlo on Lie Groups.
SIAM-JAMS (2019)

Stochastic Advection by Lie Transport. The Past, Present and Future.
Second Applied Geometric Mechanics Meeting on “Stochastic Geometric Mechanics, Fluid Models and Uncertainty Quantification” (2019)

Machine-learned 4DVar. A case study with the L63 Model.
STUOD Inaugural Workshop (2020)

Intelligent Weather Prediction. Can A.I. be used to produced better forecasts?
UCL ComputerScience PhD Seminar (2020)

Vector-valued Gaussian Processes on Manifolds.
Met Office Academic Partnership Workshop on Uncertainty Quantification and Parameterization (2021)

Spherical models for data driven weather forecasting.
UCL Statistical Machine Learning Internal Seminar (2021)

A novel framework for data assimilation using message passing.
UCL ML4Climate (2022)

Incorporating physics into spatiotemporal message passing.
UCL Statistical Machine Learning Research Day (2022)

Rethinking Data Assimilation as a Message Passing Problem.
UCL Statistical Machine Learning Internal Seminar (2022)

Improving data-assimilation for weather forecasting. A graph-based Bayesian perspective.
RIKEN, Tokyo (2023)

Data Assimilation. A message passing perspective.
CliMA, Caltech (2024)

Data Assimilation. A message passing perspective.
SIAM talk Caltech (2024)

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Professional Roles

UCL Met Office Academic Partnership sandpit meeting on “Uncertainty quantification and parameterizations” (2021)

UCL Met Office Academic Partnership workshop on Bayesian machine learning for weather and climate (2022)

UCL AI Centre workshop on “AI for Sustainability” (2023)

Machine Learning Seminar course on “Message Passing Algorithms in Machine Learning”, UCL (2023)
Nanxi Zhang
Solar PV Nowcasting with graph neural networks (2021)

with M. Deisenroth and J. Kelly

Rui Li
Learning input conditional invariances using the marginal likelihood (2021)

with M. Deisenroth and M. van der Wilk

Eiki Shimizu
Improving the approximate inference of invariant Gaussian processes (2021)

with M. Deisenroth and M. van der Wilk

Sean Nassimiha
Modelling solar power production with spatio-temporal variational Gaussian processes (2022)

with M. Deisenroth and P. Dudfield

Ronald Maceachern
Sea ice freeboard optimal interpolation (2022)

with M. Deisenroth, M. Tsamados and W. Gregory

Bengt Lofgren
Boundary aware Gaussian processes. treading freely near the edge. (2022)

with M. Deisenroth and J. Cunningham

Weibin Chen
Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification

with M. Tsamados and R. Willatt

Eirik Aalstad Baekkelund
Probabilistic Solar PV Nowcasting (2023)

with M. Deisenroth

Rafael Anderka
Efficient Data Assimilation With Nonlinear Stochastic Partial Differential Equations Through Markov Structures (2023)

with M. Deisenroth

Christian Au
ARISGAN Extreme Super-Resolution of Arctic Surface Imagery using Generative Adversarial Networks (2023)

with M. Tsamados and P. Manescu

Schrödinger Scholarship Scheme for Mathematics (2016-2020)

Imperial SIAM Student Chapter Annual Conference. Best presentation award (2018).

Doris Chen Mobility Award (2018-2019)

Reviewed papers for top tier academic journals and conferences including, Annals of Applied Probability (AAP), International Conference on Machine Learning, Conference on Neural Processing Systems (NeurIPS), International Conference on Learning Representations (ICLR) and Journal of Machine Learning Research (JMLR).

Research lead for the Met Office Academic Partnership (MOAP) work group on “Applications of Data Science to Weather and Climate” (2021-2023)