Autoscaling geospatial computation with Python and Yirgacheffe This is an idea proposed in 2025 as a good starter project, and is available for being worked on. It may be co-supervised with Michael Dales. Python is a popular tool for geospatial data-science, but it, along with the GDAL library, handle resource management poorly. Python does not deal with parallelism well and GDAL can be a memory hog when parallelised.
Josh Millar just released our latest preprint on how to make sense of the growing number of dedicated, ultra-low-power 'neural network accelerators' that are found in many modern embedded chipsets. My interest in this derives from wanting to decouple from the cloud when it comes to low-latency local environments, and this needs fast tensor operations in hardware.
Our recently published LIFE biodiversity metric has just been integrated into a newly recognised Official Statistic from the UK government! This integrates the core LIFE biodiversity metric with food provenance data to track the environmental impacts of our consumption habits. I must admit that I'd not heard of "Official Statistics" before this, so I did a bit of research.
Access to reliable and timely scientific evidence is utterly vital for the practise of responsible policymaking, especially with all the turmoil in the world these days. At the same time, the evidence base on which use to make these decisions is rapidly morphing under our feet; the first entirely AI-generated paper passed peer review at an ICLR workshop today.
I've been an avid user of GitHub since its launch, and it really has revolutionised how communities come together to work on open source. In recent years though, I find myself utterly overwhelmed by its notifications and want to experiment with alternative workflows.
Srinivasan Keshav organised this week's EEG group discussion on what AI tools we use for our daily work. I was immediately struck by how few tools there are that are actually making us more productive, so I jotted down notes as the discussion was going on. Personally, the only tool I've found that's (only just recently) making me more productive is agentic coding, which I wrote about a few days ago.
I started pushing OCaml Docker images over to the Docker Hub in around 2017, to support the burgeoning automated build infrastructure around the use of the language. Back then, OCaml 4.06 was the latest release, and so I wrote an ocaml-version library to track the release metadata.
Yaron Minsky nerdsniped me last week into getting OCaml to drive the 80s-retro RGB Matrix displays. I grabbed one from the local Pi Store and soldered it together with help from Michael Dales. But instead of writing OCaml bindings by hand, we thought we'd try out the latest agentic CLI called Claude Code released last week to see if we could entirely autogenerate the bindings.
A hardware description language using OCaml effects This is an idea proposed in 2025 as a Cambridge Computer Science Part III or MPhil project, and is available for being worked on. It may be co-supervised with KC Sivaramakrishnan and Andy Ray. Programming FPGAs using functional programming languages is a very good fit for the problem domain.
The IETF announced their new AI Preferences Working Group (AIPREF), which will "work on standardizing building blocks that allow for the expression of preferences about how content is collected and processed for Artificial Intelligence models" . This is quite well timed; the IETF tries not to standardise too early before there is running code but also needs to move before it's too late and a bad defacto standard is chosen.
This week I've been reading three really nice pieces of work by my colleagues, in the form of a review paper on biodiversity and AI, a benchmark for 3D forest reconstruction using laser scanners and a mobile app for measuring the width of tree trunks. A real bonanza for forest lovers!