Assistant Professor in Machine Learning @EdinburghUni. PhD in Mathematics. Ex @ETH_en. See https://xitter.cf/t.co/ZjGKiCBhyF

Edinburgh, UK
Joined February 2023
My group at the University of Edinburgh is recruiting #PhD students for 2027! ๐ŸŽ“ We are GLUEd: Geometric Learning & Uncertainty in Edinburgh. Group: vab.im/group/ How to apply: vab.im/vacancies/ 1/2
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Deadlines: ๐Ÿ”น ELLIS PhD Program: 31 Oct ๐Ÿ”น School of Informatics scholarships (fully funded; home & international): 30 Nov ๐Ÿ”น CDTs: Novโ€“Jan Home-fee applicants especially encouraged: more funding available. Email me before applying, and please help spread the word! 2/2
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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! ๐Ÿ‘‡ Link and more below! ๐Ÿ‘‡ 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!
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My first paper with my first PhD student @PedroCVieira0 just landed on arXiv! โ€œDo Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?โ€ Spoiler alert: the answer is largely noโ€”at least, not much more than a single model does. ๐Ÿงต๐Ÿ‘‡ ๐Ÿ“„ arxiv.org/abs/2605.22593
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๐Ÿ”น Convexity? Message passing GNNs find different weights but learn remarkably similar functionsโ€”even 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.
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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)! ๐ŸŽ‰ github.com/geometric-kernelsโ€ฆ
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Bonus: a concrete success story. In the traffic prediction benchmark below, a Geometric GP (powered by GeometricKernels) significantly outperforms GNN Ensembles and Bayesian GNNs in both prediction (RMSE) and uncertainty (NLL) quality.
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I am hiring a fully-funded #PhD in #ML to work at @EdinburghUni on ๐ ๐ž๐จ๐ฆ๐ž๐ญ๐ซ๐ข๐œ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  and ๐ฎ๐ง๐œ๐ž๐ซ๐ญ๐š๐ข๐ง๐ญ๐ฒ ๐ช๐ฎ๐š๐ง๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง. Application deadline: 31 Dec '25. Starts May/Sep '26. Details in the reply. Pls RT and share with anyone interested!
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