A retro-revolutionary mapping app for the Ski Industry.I've built a scalable data transformation process to transform public open-source data from OpenStreetMap into a tool for locating a user on a skiing piste map interface. With a domain-appropriate data processing algorithm, I’ve been able to plug the gaps in the open-source data to identify skiing pistes from GPS data with only a minimal amount of front-loaded user-input. Working with React-Native (Expo), and an Express + PostgreSQL server hosted on AWS - I’ve built a complete tech stack to deliver a mobile app that can replicate this process across hundreds of different ski resorts.

I worked as primary developer on two hospital-wide projects.** UHS Lifelines - a major revitalisation of the patient medical record interface. See: https://uhsdigital.co.uk/article/uhs-lifelines/I took an existing tool and updated it, building a scalable solution for data aggregation and standardisation from sources across the hospital's technical ecosystem.** SCRPlus -an end-to-end integrated system for data collection, clinical data access, and analytical filtering. We expanded on existing efforts with one of the world’s largest breast cancer treatment datasets to empower clinicians to use this for research, and to scale this entire process to help other cancer specialty areas build parallel datasets. I built an in-memory C# data aggregation and data filtration pipeline. This enabled medical experts (without SQL skills) to extract and analyse data using a graphical user interface. A key feature is that changes and decisions in our data collection schemas automatically cascade into an updated data filtering process and UI. This streamlined design allows our tool to support a large number of cancer areas without scaling our technical input.

A tell-all app on the expected Environmental Impacts of everyday supermarket products.The challenge here was primarily in identifying and understanding available Life Cycle Analysis (LCA) data. I spent time reading widely through research and opinion articles, talking with world-leading scientists, and building small prototype systems to understand the scientific and human problems at play.While many studies have sought links between products and their environmental impacts, their widely varying methodologies and coarse-grained groupings of products make the role of a transparent, trustworthy data ‘revealer’ challenging, and irresponsible.Key issues at this stage:- Unavailability of private data relating to company's products- High methodological uncertainty in estimates- Uncontrollably high uncertainty on all accepted scientific measures of environmental impact outside of the GHG emissions category,