Joe Davies
Research Associate
King’s College London
Joe is a research assistant at the Maurice Wohl Institute of King’s College London. He obtained his PhD in Particle Physics at Queen Mary University, which focused on the development of machine learning algorithms to search for Dark Matter at the Large Hadron Collider. He currently works on the development of algorithms to search for and identify Post-Ictal Generalized EEG Suppression from data collected using an ultra long-term subcutaneous EEG device in people with epilepsy.
Qualifications:
- Msci in Theoretical Physics, First Class with Honours.
- PhD in Particle Physics.
Previous Experience:
- September 2018 – March 2024: PhD Candidate. Worked on using machine learning to constrain the search for Dark Matter at the Large Hadron Collider.
- July 2022 – October 2022: Data Science Intern for the Royal National Lifeboat Institute. Worked on forecasting models for when rescues and assists were most likely to be needed. Created a suite of algorithms to classify incident reports. Taught python, data science and data engineering.
- October 2021 – March 2022: NLP Engineer at Rewire Online. Worked on hate-speech detection using NLP. Compared our model to competitors. Testing model robustness by augmenting data and using adversarial techniques.
- July 2019 – September 2019: Data Science Intern at Receipt Bank. Worked on user segmentation to provide insights on targeted advertisement of services.
Core expertise:
- Machine learning,
- Data science,
- Python,
- C++,
- Physics.
Profile links:
Publications and Posters:
- Aarrestad, T., van Beekveld, M., Bona, M., Boveia, A., Caron, S., Davies, J., … & Zhang, Z. (2022). The dark machines anomaly score challenge: benchmark data and model independent event classification for the large hadron collider. SciPost Physics, 12(1), 043.
