About Me
I am a doctoral researcher at Helmholtz AI and the Technical University of Munich in Dr. Vincent Fortuin’s lab. I am broadly motivated by the need to develop machine intelligence systems that operate with an accurate sense of uncertainty over their predictions. Ensuring high quality predictions and uncertainty estimates boils down to encoding the right inductive biases, and in my Bayesian view this amounts to selecting the right prior for our model.
My research area is that of amortised inference. While I am very excited by ideas in simulation-based inference, prior-data fitted networks (including the in-context tabular revolution), and the posing of Bayesian inference as an in-context learning problem in general, the area that I am especially interested in and have devoted most of my research efforts towards thus far is that of neural processes. Neural processes enable us to meta-learn the “right” prior and to amortise predictive inference, allowing for the rapid procurement of well-calibrated probabilistic predictions. To paint a more detailed picture of my research interests, three papers that I particularly enjoyed reading recently are:
- Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation,
- Flow Matching Neural Processes,
- Amortized Probabilistic Conditioning for Optimization, Simulation and Inference.
As for application areas, as an engineer by training, I am mostly interested in using probabilistic machine learning to solve scientific and engineering problems, and I am becoming increasingly interested in weather and climate forecasting.
Prior to my PhD, I took a year out of education to focus on expanding my knowledge of probabilistic machine learning. During this time, I wrote a single-author research paper about parameter symmetry breaking in BNNs that was accepted at AABI 2024, and I spent five months as a Machine Learning Researcher at Motorway in London where we developed and deployed Bayesian machine learning systems for a variety of use cases within vehicle pricing.
Before my year out, I studied engineering at the University of Cambridge where my specialism was computer and information engineering, though my module choice made the integrated masters year indistinguishable from a typical masters in Machine Learning, albeit with a heavy dose of Bayesianism.
For more information about my background, feel free to look at my CV. To see my publications, you can scroll to the bottom of my CV or find my Google Scholar page here. My CV is accurate as of June 2026.