I am Louis Chislett, a PhD student based at the university of Edinburgh part of the HDRUK/Alan Turing Institute PhD programme in health data science. My research interests are broad, covering areas in machine learning including Deep Reinforcement Learning, classification and neural networks. My academic research currently exists in private repositories pending publications (where it will be made public), while mini-projects which I have undertaken can be perused at your leisure.

Stay tuned for my upcoming PhD work AMUSE (Adaptive Model Updating using a Simulated Environment), in which I have come up with a new approach to updating models in the presence of concept drift using reinforcement learning.

In my public profile you can find the following:

• Image Classification App: A convolutional neural network (CNN) approach to image classification with an accompanying demonstration app
• Wellcome Trust Ideathon App: My competition entry to the Wellcome Trust Ideathon 2023, where I lead a team to the semi-finals. This R Shiny app uses NLP techniques to build a dashboard which tracks trends in COVID-19 misinformation on twitter for the duration of the pandemic
• Bayesian linear regression model from scratch: An example of a bayesian linear regression model, built from scratch (i.e. no libraries used) using a metropolis-hastings sampler.
• Skin cancer Detection: The ISIC 2024 Kaggle competition. This example of multi-modal learning builds a neural network which takes as input image and tabular data, eventually concatenating layers and predicting skin cancer incidence.

• 🔭 I’m currently working on: AMUSE (Adaptive Model Updating using a Simulated Environment), in which I have come up with a new approach to updating models in the presence of concept drift using reinforcement learning.
• 🌱 I’m currently learning: SQL
• 👯 I’m looking to collaborate on any projects relating to concept drift adaption in classification models
• 💬 Ask me about any internship positions you are looking to fill
• 📫 How to reach me: