## Career Profile

So Takao is a postdoctoral scholar at the California Institute of Technology, working on the intersection of machine learning and data assimilation. His interests involve (1) developing efficient inference techniques for probabilistic models such as Gaussian processes and stochastic partial differential equations, (2) developing machine learning models with physical, geometric or topological inductive biases, and (3) applying machine learning techniques to problems in weather or climate science. During 2021-2023, he was a Senior Research Fellow in Machine Learning for Climate Science at the UCL Sustainability and Machine Learning group. There, he lead the Met Office Academic Partnership workgroup on “Data science methodology for weather and climate” and intiated various collaborations across departments to work on problems related to climate change. He received his PhD in 2020 from the Department of Mathematics at Imperial College London, where he wrote his thesis on structure-preserving fluid models and MCMC techniques on Lie groups, advised by Prof. Darryl Holm.

## Experiences

- Principle investigator: Prof. Andrew Stuart
- Working on various projects involving Gaussian processes, data assimilation, uncertainty quantification and operator learning.

- Principle investigator: Prof. Marc Deisenroth
- Worked on developing novel data assimilation algorithms using ideas from message passing, Gaussian markov random fields, stochastic (partial) differential equations, differential geometry and algebraic topology.
- Collaborated with the UCL Earth Science Department on various projects on sea-ice modelling.
- Supervised several BSc and MSc students.
- Lead the Met Office Academic Partnership workgroup on “Data science methodology for weather and climate”.

## Preprints and Publications

**Preprints**

** Publications **

** Workshop papers **

## OSS Contributions

Below is a list of open source softwares that I have been involved in developing: