2021 - 2022

Developed a thorough understanding of machine learning/statistical methodologies and how to apply these to scientific data. For my dissertation, Segmentation of Windthrow in High Resolution Capella SAR Images Using Fully Convolutional Networks, I developed tools to automate the processing of SAR imagery into a format suitable for deep learning and developing novel segmentation algorithms. This work was supervised by Prof Iain Woodhouse and I worked closely with Capella Space. Courses Included:

• Achieved an MSc with Distinction

• Dissertation Mark 87%

2014 - 2017

Gained an understanding of many physical systems and excellent mathematical abilities in areas covering calculus, linear algebra, mathematical reasoning, probability and statistics.

• Achieved a First Class degree

• Developed a Least-Squares Fitting Routine Python application currently in use by the University for data analysis and received formal recognition from the Laboratories Committee for the ‘outstanding’ work done and its usefulness for studies in Undergraduate Laboratory work

2013 - 2014

Progression from foundation year required an average year grade of 80% which was achieved by averaging 93%; amongst the highest in my cohort.

• Recipient of the Gillett Foundation Studies Scholarship, this was awarded in recognition of my achievements on foundation year

Machine Learning Engineer @ Sylvera

April 2023 - present

Machine learning engineer focussed on combining deep learning and remote sensing to bring transparency to the carbon marketand incentivize real investment in climate action.

• Casually doing my best to save the planet 🌳

• Researching and developing novel deep learning algorithms to be applied to satellite data

• Engineering ML pipelines according to software engineering best standard to ensure they are robust, scalable and extensible.

• Liaising with stakeholders to ensure our ML solutions are actually brining value to customers

Data Scientist & Engineer @ Babcock LGE

October 2022 - March 2023

Leading the development of data processing pipelines and methodologies to analyse real time data streamed remotely from Liquid Gas Equipment (LGE) instrumentation.

• Leading the deployment of the infrastructure and practices required to productionise data science solutions into commercial products. This is a new endeavour for Babcock and I am liaising with business wide stakeholders to ensure solutions work across the business and remain robust

• Developing machine learning and artificial intelligence solutions to minimise operational downtime and ensure equipment is running efficiently as possible; aiding the shipping industry in meeting its net zero commitments

• Developing digital twins of instrumentation to aid process (chemical) engineers understand how systems are operating in the field

Data Scientist @ Earth Blox

March 2022 - August 2022

Data scientist at a fast paced startup aiming to make earth observation accessible to everyone, regardless of expertise. I support the development of new earth observation workflows by developing data science pipelines and prototyping new algorithms.

• Collated historical windthrow events from the Copernicus Emergency Management Service to facilitate development of new windthrow algorithms in Sentinel-1 data.

• Developed automated workflows to scrape, encode and create training data from available earth engine functions and image collections IDs. Developed algorithms to recommend useful earth engine collections to users based on previous workflow usages.

Software Engineer @ Kratos Analytical

Dec 2020 - Aug 2021

Responsible for feasibility studies and driving the adoption of new technologies/practices. Worked with C#, Azure, Python, Specflow, Gherkin, Appium and more!

• Drove the adoption of modern practices and held workshops, demonstrations and presentations

• Developed a test framework with the capability to automate at least 50% of the existing manual testing

• Incorporated Azure into the existing infrastructure to improve CI/CD

Software Engineer @ Waters

Nov 2017 - Dec 2020

Responsible for the design and implementation of Mass Spectrometer control software. Working within a microservice architecture with C++, C#, Python, Lua, Docker, Angular2+ and more!

• Created deep simulations of real time data and instrument behaviour

• Developed self-diagnosing and automated instrument setup procedures

• Pioneered the use of python for developing new data analysis techniques

• Investigated how machine learning could be utilised for predictive maintenance

Development Scientist @ Waters

Jun 2016 - Sep 2016

Involved in the development process and testing of a new mass spectrometer.

• Carried out component and sub-system testing on a prototype mass spectrometer

• Gained knowledge in maintaining mass spectrometry systems, specifically with GC-MS systems

• Suggested design improvements in relation to potential faults and liaised with mechanical engineers in Singapore about these issues