Probabilistic Reasoning (Seminar)

I recently had the opportunity to give a seminar as part of the Oxford ML and Physics seminar series. I talked about probabilistic reasoning, and why it is not just about uncertainty but can be a superpower enabling us to tackle complex problems without training data.

Do give it a watch! I would also highly recommend browsing the Oxford ML and Physics Seminars YouTube channel, which is a treasure trove of fascinating talks on a huge variety of topics, from generative modelling of quantum states to seasonal sea ice forecasting.

The full abstract for my talk:

In both atmospheric science and machine learning, it is important to capture the uncertainty of predictions. This information can avoid the dangers of relying on over-confident predictions which may be incorrect, and help to understand the potential for high-impact rare events. Nonetheless, to focus only on capturing uncertainty risks giving an incomplete picture of the strengths of probabilistic modelling. At their heart, probabilistic models are about information. How does a new observation influence our mental model of the world? In this talk, I will discuss the use of probabilistic models in atmospheric science and how they can aid scientists in interpreting incomplete observations. I will also present some early work in this direction for spatial reasoning and time-series analysis.