Wait till he hears about cuthbert github.com/state-space-...

I think this didn't get nearly enough traction. Sam did some fantastic work here leveraging the library we've been developing to make predictions for football. This was ported to a UI by Ryan Chan (Warwick MSc student) who's doing his thesis with me (on the methods behind the prediction).

The World Cup is about to start, I wrote a model using cuthbert to predict the games, check out cuthberto-carlos 🐛⚽ ryantjx.github.io/cuthberto-ca...

Co-organising a workshop: Non-Equilibrium Sampling: Diffusions · Flows · Particles September, Newcastle Come join the fun!

I'll be at ICLR this week 🇧🇷 presenting my complete SDE decomposition at the DeLTA workshop on Monday. Reach out if you want to meet!

The ensemble Kalman filter is now in cuthbert 🔥 The EnKF is one of those algorithms that "just works" - oftentimes in settings it has no right to github.com/state-space-...

It was my birthday this week, it really had to be done

“Parallelizing MCMC Across the Sequence Length”: This one is really cool. statmodeling.stat.columbia.edu/2026/02/03/p...

New open source: cuthbert 🐛 State space models with all the hotness: (temporally) parallelisable, JAX, Kalman, SMC

New preprint! A Complete Decomposition of Stochastic Differential Equations I characterise *all possible SDEs* that satisfy given time-dependent marginals p(x,t)

Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which i) mass is reasonably well-concentrated in the centre of the state space, and ii) the log-density is smooth and of moderate growth. Outside of this setting, things can go poorly.

New paper on arXiv! And I think it's a good'un 😄 Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.

In slides from a recent talk - the { virtuous / vicious } cycle of filtering, smoothing, and parameter estimation in state space models.

Me: Hey so where’s good to eat round here? Singapore taxi driver: Malaysia

posteriors 𝞡 published at ICLR! I’ll be in Singapore next week, let’s chat all things scalable Bayesian learning! 🇸🇬👋

A new instalment of office decor:

So simple! Normally we order our minibatches like a, b, c, ...., [shuffle], new_a, new_b, new_c, .... but instead, if we do a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, .... The RMSE of stochastic gradient descent reduces from O(h) to O(h²) arxiv.org/abs/2504.04274

Sequential Monte Carlo (aka. Particle Socialism?): "why send one explorer when you can send a whole army of clueless one"

Was revisiting the Neural ODEs paper the other day and greatly enjoying. But I found this super confusing, it’s not an A=B+A statement

Thrillingly (/s), I have today (lightly) updated my website (sites.google.com/view/sp-mont...). I highlight that I've added i) links to several slide decks for talks about my research, and ii) materials related to the (few) short courses which I've given in the past couple of years. Enjoy!

Hi there! This account will post about the AlgoPerf benchmark and leaderboard updates for faster neural network training via better training algorithms. But let's start with what AlgoPerf is, what we have done so far, and how you can train neural nets ~30% faster.

I've been using Cursor and enjoying it but I'm not sure I'm bullish on it for the long run. One of the best parts about VSCode is the ecosystem of extensions (and that it is open source). Cursor is already out of sync and having issues with extension compatibility

Thermo Matrix Exponentials has been published in Physical Review Research 🔥 On a thermodynamic computer, the matrix exponential occurs very naturally through the temporal covariance driven by the noise - a polynomial speedup over digital computers!

Discovered today that LinkedIn has daily “Puzzle games” and they’re quite fun!

Thermodynamic Linear Algebra is published! And I, for one, am delighted to see work from Normal Computing published in Unconventional Computing 😝

This is a lovely read

Parallel scans accumulate sequences on GPUs (or other parallel hardware) at logarithmic cost in the size of the input. The canonical example is cumulative sums (a, a+b, a+b+c, ...) from an input (a, b, c, ...), but this is hardly the only use, and, e.g., Kalman filtering can be handled in parallel.

Bluesky now @spmontecarlo.bsky.social is here: