Hello
I'm Nic Pritchard, a final-year PhD candidate and experienced Software Engineer specialising in Machine Learning and building complex computational systems. My research in neuromorphic computing applied to radio astronomy pushes the boundaries of efficient, time-aware AI, demonstrating my ability to translate cutting-edge concepts into practical, working technology.I excel at bridging rigorous scientific research with robust software engineering practices to deliver data-centric solutions and tangible outcomes. My practical experience includes:
- Developing and implementing novel neural network architectures (Spiking Neural Networks) for efficient, real-time analysis of complex, high-rate data streams. I have achieved state-of-the-art anomaly detection performance in radio astronomy at ~1/100th of the energy of GPU-based approaches (PhD Research).
- Co-founding Cohesion Technologies, where we develop strategic product management software and consult on AI enablement.
- Developing Mondage, a task management web app I created to solve a real-world user need for expressing task dependencies simply.
- Contributing to large-scale scientific software frameworks like the Square Kilometre Array (SKA) execution framework.
- Applying expertise in computer vision, high-performance computing (HPC), computational workflow management, and full-stack web development.
Please feel free to reach out – I'm always happy to connect, discuss potential collaborations, or chat about the latest developments in AI and software engineering.
Research Interests
Spiking Neural Networks & Artificial Intelligence
I investigate how a particular type of neural network (spiking neural networks), which naturally evolve over time could help solve extremely high-rate data processing problems. Radio Astronomy is a great place to find these kinds of problems, but the techniques I create are applicable anywhere there's data changing in time.Data-Intensive Astronomy
Science's data processing needs are growing immensely, and computational workflows aim to address these needs. ICRAR's Data-Intensive Astronomy team is building the computational backend for the SKA, which needs to handle a data-ingest rate up to 50TB/s.Scientific Reproducibility
The scientific method has given humanity some of its greatest successes and our most terrifying inventions. Continuing to do science we can trust is an eternal struggle made harder in a sea of data. Creating technologies to simplify this process is key to solving our most pressing challenges.Quantum Computing
Quantum computing is closer than ever, and we'll need a lot of software. My earliest work involved simulating quantum algorithms (Quantum Approximate Optimisation Algorithm (QAOA)) assuming that this technology is ready to search for problems that would benefit from these exotic machines.Traffic and Urban Planning
Impending autonomous driving technology has ignited questions about the limits of our transport systems. I have helped the Planning and Transport Research Centre PATREC with computer vision and modelling projects to investigate autonomous technologies applied to Perth, Western Australia, my hometown.Writing
You can find my publications on Google Scholar.
Reading
I am always on the hunt for better things to read, and for the last few years, I have kept track of my
favourite book I read that year. If you find something you ultimately enjoy, let me know, and if you have
some suggestions, I would love to hear them.