About Me

I am a highly motivated and results-driven Machine Learning Scientist with multiple years of experience in applying deep learning techniques to solve problems in the weather and climate domain.

Skilled in data analysis using Python, programming, high-performance computing, working with and processing large datasets from weather and climate models, and problem-solving. I have successfully transitioned from academic research (PhD and postdoc positions) to private industry, where I apply cutting-edge ML techniques to deliver real-world solutions.

My work focuses on developing deep learning models for geospatial applications, with expertise in atmospheric science, climate modeling, and weather forecasting. I'm passionate about bridging the gap between research and industry applications.

Resume

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Experience

Machine Learning Scientist

MetDesk Ltd 2024 — 2025
  • Improved execution speed of in-house model by 76%, resulting in the quickest model to market in the sector and attracting multiple new clients due to this speed up
  • Developed a deep-learning based algorithm for wind energy forecasting
  • Developed plan and leading project for an improved solar and cloud forecasting method using live satellite data
  • Pitched and now leading a project that would allow the company to expand to a worldwide clientbase without the need for any new hires
  • Interacted with company leaders to help in the planning of new offerings in the upcoming years, as well as developing methods to improve efficiency of current company practices
  • Helped improve understanding and usage of company hardware, leading to more efficient processes, including version control and a clean codebase
  • Helping mentor and set reasonable targets for new intern

Postdoctoral Researcher

Lawrence Livermore National Laboratory 2022 — 2024
  • Developed deep learning model to detect atmospheric rivers in meteorological data with world-leading performance
  • Worked with a team to create a model to perform bias correction for rainfall for the in-house climate model
  • Developed a system to employ deep learning to discover new physics to improve rainfall modelling via a new parametrization for deep convection
  • Handled and preprocessed large volumes of climate data (>2TB)
  • Helped out various colleagues to optimize their computational workflows, thus being able to produce results much quicker
  • Organized various networking events for postdocs across different sections, as well as contributing to various decision-making initiatives
  • Presented results in multiple international conferences

Computational Scientist

National Centre for Atmospheric Science 2022 — 2022
  • Carried out data analysis to inform on the implementation of a new data storage method when using the UK Met Office climate model
  • Started work to use physically-informed neural networks to be used for emulation of certain processes in a climate model, to improve the computational cost of the climate model, while making it quicker to execute

JAVA Software Engineer

LOQUS Business Intelligence 2016 — 2017
  • Improved existing algorithms to produce more efficient solutions
  • Carried data analysis on existing algorithms to identify any inefficiencies
  • Helped transition the company from a local MySQL database to a cloud-based MongoDB database
  • Presented team results to company executives and clients

Education

PhD Computer Science

University of Reading 2018 — 2022
  • Developed deep learning model using PyTorch to detect the presence of tropical cyclones in meteorological data
  • Formulated and executed a method to use this model for a data reduction method, which was implemented in the UK Met Office's climate model
  • Performed data analysis to computationally optimize the data reduction method
  • Developed skills in deep learning, working with large datasets, HPC, Linux, version control, Docker and Singularity containers
  • Presented findings at an international conference
  • Participated in the Young Entrepreneur Scheme where various skills, including working to a deadline and working with a team, were developed

MSc Atmosphere, Oceans and Climate

University of Reading 2017 — 2018
  • Developed understanding of major physical processes controlling meteorological activity across the globe
  • Developed physical modelling skills, e.g. using the finite difference method to model dispersion of a trace gas
  • Developed skills in presenting complex data and ideas to a non-expert audience, as well as writing weekly progress reports

BSc Computational Physics

University of Malta 2014 — 2017
  • Developed various skills in computational modelling using a variety of programming languages
  • Learnt various computer science fundamentals including Linux, HPC and object-oriented programming
  • Experienced and developed skills in implementing machine learning techniques, e.g. genetic algorithms
  • Developed skills in mathematical understanding of differential equations and other constructs

Publications

Published

In Review / Preparation

Deep Learning-based Non-Stationary Bias Correction Applied to Future Climate Projection

Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, Daniel Galea

Journal of Geophysical Research - Machine Learning and Computation

2025
Detecting Atmospheric Rivers in the Arctic

Sinéad McGetrick, Hua Lu, Grzegorz Muszynski, Oscar Martínez-Alvarado, Matthew Osman, Kyle Mattingly, and Daniel Galea

2025
Evaluating Probabilistic Deep Learning Methods for Uncertainty Quantification of Temperature Downscaling

Yannic Lops, Gemma J. Anderson, Indrasis Chakraborty, Donald D. Lucas, Daniel Galea, Shuang Yu

Artificial Intelligence for the Earth Systems

2024

Skills

Expert

Data Science Deep Learning Python Problem Solving Linux/Unix

Advanced

Software Development C C++ JAVA Statistics

Proficient

MySQL MongoDB High-Performance Computing AWS GCP

Get In Touch

I'm always open to discussing new opportunities, collaborations, or just having a conversation about machine learning, climate science, or technology.

London, England, UK