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Recent Projects

Below is a collection of open-source personal projects I am working on, as well as some very interesting projects I am lucky enough to be involved with. If you like the work here and would like to support more from me, you can help sponsor contributions here


simurgh

simurgh (pronounced Seymour) is an open source platform that supports developing and evaluating algorithms (AI agents) for automated air traffic control. It provides an easy to use interface for running experiments in an air traffic simulator as well as packages that support agent development.

Air traffic control (ATC) is a complex task requiring real-time safety-critical decision making. In practice, air traffic control operators (ATCOs) monitor a given sector and issue commands to aircraft pilots to ensure safe separation between aircraft. They also have to consider the number and frequency of instructions issued, fuel efficiency and orderly handover between sectors. Optimising for the multiple objectives while accounting for uncertainty (e.g., due to aircraft mass, pilot behaviour or weather conditions) makes this a particularly complex task.

The Simurgh project provides a research-focused user-friendly platform for testing automated approaches to ATC


www.astroinformatics.xyz

astroinformatics.xyz is a online book inspired by the OpenAI book SpinningUp. The idea is to have a resource that outlines the mathematics and technology involved with doing Astroinformatics and the Mathematics and Data Science that underpins the research. It is still very much a work in progress, but the plan is to have 3 main sections: 1. Data Science for the Mathematics and Machine Learning theory. 2. Data Engineering; to showcase the technologies used, like Apache Spark or Apache Kafka for instance. 3. Research Engineering to outline the tools of the trade for reproducible research, model deployment and production system design.


option3

option3 is a proof of concept application that is being used to improve my understanding of Kafka and Spark for developing machine learning data pipelines. Inspired by Stephane Maarek's Kafka for Beginners course, I hope to connect to the Twitter stream of tweets, apply some filtering and transformations, and finally visualise, in real-time, the processed data.



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