
Assistant Professor in Machine Learning @EdinburghUni. PhD in Mathematics. Ex @ETH_en. See https://xitter.cf/t.co/ZjGKiCBhyF
Edinburgh, UK
Joined February 2023
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My group at the University of Edinburgh is recruiting #PhD students for 2027! ๐
We are GLUEd: Geometric Learning & Uncertainty in Edinburgh.
Group: vab.im/group/
How to apply: vab.im/vacancies/
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Viacheslav Borovitskiy retweeted
Announcing: my final Cornell-era academic project - launching today!
It's called "Bayesian Decision-making Algorithms" - a book which tries to write down everything important I've learned in ten years of research.
Chapters 1 and 2 are now online!
๐ Link and more below! ๐
URL: bayesianalgorithms.com
Decision-making is ubiquitous in life - taking actions, seeing what happens, learning, doing a better job next time. In doing so, we plan using our mind's model of reality.
Bayesian decision-making casts this paradigm into math - by using a Bayesian model to quantify uncertainty about reality. The question becomes: how do we use this uncertainty to take better actions?
This is not as easy as it looks. Due to the presence of explore-exploit tradeoffs, maximizing expected value is often bad: strategies that do so can get stuck in bad actions because they don't try enough new things to correctly learn about the world.
The fix is to consider more sophisticated strategies for exploration. In this book, I'll show you the major known algorithm classes which balance tradeoffs like the above, and will teach you how they work.
This book represents the conclusion of an intellectual journey that began at the start of my twenties.
Its primary goal is to inspire the next generation to learn about Bayesian thinking, to enable others to do bigger and more creative things with it.
Especially in realistic situations where we do not assume the Bayesian model exactly matches reality.
In particular, I think there is a chance Bayesian decision-making is one of the "right abstractions" for understanding how large language models actually work, including how they handle exploration. You should consider working on this!
I'm writing the book build-in-public style - I'll be posting other chapters when they're done, which is hopefully soon.
I know exactly what I want to write, the remaining work is to do so. Stay tuned for that.
I don't know how long it will take me to finish. Among my ambitions, the book is number two on the priority list - nonetheless, writing it is possible, because priority one is bottlenecked by the number of days in a year, not the number of hours in a day. More on that another time.
In the meantime, I'd love your comments! Please have a look, and tell me what you think!
My first paper with my first PhD student @PedroCVieira0 just landed on arXiv!
โDo Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?โ
Spoiler alert: the answer is largely noโat least, not much more than a single model does. ๐งต๐
๐ arxiv.org/abs/2605.22593
๐น Convexity?
Message passing GNNs find different weights but learn remarkably similar functionsโeven where they fail. We hypothesize the architecture induces function-space convexity, making them behave like linear models despite their depth. Formalizing this is an open problem.
๐ป Code: github.com/PedrV/gnn-uq-inspโฆ
๐ Preprint: arxiv.org/abs/2605.22593
Co-authored w/ @PedroCVieira0 & @pedroribeiro_pt.
#MachineLearning #GraphNeuralNetworks #DeepLearning #UncertaintyQuantification #GeometricDeepLearning
Happy to share a major milestone: after years of development, we are officially launching Version 1.0 of the GeometricKernels library!
To top it off, our accompanying paper has just been published in JMLR (MLOSS)! ๐
github.com/geometric-kernelsโฆ
I am hiring a fully-funded #PhD in #ML to work at @EdinburghUni on ๐ ๐๐จ๐ฆ๐๐ญ๐ซ๐ข๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ and ๐ฎ๐ง๐๐๐ซ๐ญ๐๐ข๐ง๐ญ๐ฒ ๐ช๐ฎ๐๐ง๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง.
Application deadline: 31 Dec '25. Starts May/Sep '26.
Details in the reply.
Pls RT and share with anyone interested!
Note: I am also recruiting through @ELLISforEurope PhD program (ellis.eu/news/ellis-phd-progโฆ).





