I’m a PhD Candidate in Trustworthy Artificial Intelligence at Delft University of Technology working on the intersection of Computer Science and Finance. My current research revolves around Counterfactual Explanations and Probabilistic Machine Learning. Previously, I worked as an Economist for the Bank of England.
I started working with Julia at the beginning of PhD in late 2021 and have since developed and used various packages for my own research, some of which I presented at JuliaCon 2022. To organise these efforts, I have recently created Taija: a GitHub organisation that hosts software geared towards Trustworthy Artificial Intelligence in Julia. Go check it out and should you be interested in collaborating, feel free to reach out. Actually, feel free to do that in any case!
ConformalPrediction.jl: a Julia package for Predictive Uncertainty Quantification in Machine Learning (ML) through Conformal Prediction. It works with supervised models trained in
MLJ.jl, a popular comprehensive ML framework for Julia. Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions.