1. Founding a great
research company
I help lead Arb, a research consultancy in the mould of Rethink Priorities, RAND, and IIASA.

We focus on forecasting and other important niche areas (large-scale data collection; machine learning for epistemics). Consultancies are reactive, but we have our own agenda - some ideas sketched here.

Current work:

  • I lead a team investigating AI talent for the new Emergent Ventures programme.
  • Improving the state of AI forecasting
  • Processing an author's entire corpus and automatically evaluating their forecasting ability
  • Following my Covid work, I’m helping Jan Kulveit set up the Active Long-termist Emergency Response Team.
2. Side-channel attacks
against AI risk
I'm interested in keeping an eye on AI. By a stroke of luck my PhD advisor is Laurence Aitchison. I'm currently on sabbatical.

Besides that, I’m working on a large project upstream of AI forecasting and leading a Mercatus Center project on AI talent, with the aim of bringing in non-financial support like technical mentors, visas, and excitement.

3. Becoming less well-rounded
My background is extremely mixed: a lot of philosophy, a bit of economics, a bit of stats, a bit of computer science, a bit of computational linguistics, a bit of epidemiology, a bit of literary criticism. But in research, much depends on you mastering one thing or one cluster of things.

So I am trying to become less rounded. This boils down to more maths. One carrot: I aim to fully understand the great explanations: relativity, QM, evolutionary dynamics, Bayes, reinforcement learning, and computational complexity.

4. Giving a damn
I started teaching and mentoring people at last. I help with the AI Safety Camp, the EA Bristol Uni group, and teach at ESPR. I received an Infrastructure Fund grant to try and fix executive function in the community (“Off Road”). I speak for the dead.

💪 Work 💪

  • Researching AI, forecasting, metascience, and cause prioritisation.

    Clients include Open Philanthropy, the Future of Humanity Institute, Mercatus, Alvea.

    Public work:

    * Forecasters vs experts
    * Scoring the Big 3

  • I am chair of the Emergent Ventures / Schmidt Futures panel on AI Talent.

  • Modelled COVID with Oxford / Imperial / Harvard colleagues. Probabilistic programming, technical writing, data cleaning, literature reviews.

    7 papers in 14 months. I led a major study of mask effectiveness.

    Buzzwords: PyMC3, MRP (hierarchical Bayes), AWS, Azure, Seaborn

  • Solo instructor in CS tutorials, training other TAs. Bayesian ML & Algos 2.

    Buzzwords: Gaussian processes, Markov Random Fields, Bayesian opt., variational inference, dynamic programming, graph theory, complexity

  • Built dozens of ML models (GLMs, GBMs, neural nets) for actuarial pricing, medical risk, & image recognition. Web scraping at scale; migration from SAS to Hadoop, & Hadoop to Azure. Came in as a graduate data scientist, left as tech lead for a team of 4.

    Buzzwords: Python, Spark, Keras, XGBoost, Tableau, Avro, Pytest, GDPR

  • Jumped into web dev; my code deployed worldwide within 2 weeks.

    Products: a medicine pump device manager & a pharmacy record system.

    Buzzwords: C#, WPF, node.JS, MEAN, Ractive, Mongo, Vagrant, VMware

  • Made official statistics, Education Analytics. Testing for a data visualisation tool for teachers: SAS/SQL. Prep for the Social Care Survey 2015.

  • A quiet business. Read all of Arts & Letters Daily, all of 3 Quarks Daily, all of Crooked Timber, and all of Marginal Revolution every day for 9 months.

  • Dogsbody work at a small software company in Beijing. Handled expenses, learned Mandarin, looked at inefficiencies, saved face.




  • AI PhD

    Probabilistic programming, AI safety. Long detour on COVID models.

    First year was a de-facto research Master’s (uncertainty modelling, NLP, GOFAI, legal aspects).

    Second year: COVID modelling. EPSRC Centre for Doctoral Training studentship (4 years fully funded).

    Thesis: Tensorised Probabilistic Programming

  • BSc Maths & Stats

    89% GPA ≈ 96th percentile.

    Probability up to diffusion processes, regression to GLMMs, numerics to BFGS, linear algebra to multi-linear, RCTs. Part-time during FT work. Reviewed here.

  • MSc Software Development

    82% GPA ≈ 85th percentile.

    Fairly vocational. Java, SQL, HTML/CSS/PHP, assembly, design patterns, data structures & algos.

    Thesis: Hidden Markov Models for Linguistic Accommodation.
    Full-ride Scottish Funding Council scholarship.

  • MA Economics & Philosophy

    85% GPA ≈ 90th percentile.

    Micro and macro, Ethics, global justice, Continental philosophy, development policy, formal logic, social statistics, health policy, development policy.

    Dissertation: On the Abolition of Foreign Aid. Alumni Fund scholarship.


  • Brought modern language modelling (Flair) into Prolog for some reason.

  • Journal-grade modern Bayesian workflow in PyMC3. Probabilistic programming for epidemic modelling and effect estimation. End to end with data getters.

  • Python wrapper for the Hidden Markov ToolKit.



    Co-founder / board member of the Active Longtermist Emergency Response Team, an on-call org watching the skies.


    The European Summer Programme on Rationality: a grand maths / experiential ed camp. I ran 4 sessions on personal agency, research skills, speculative astronomy, and cultural literacy.

    I'm running one of the 2022 camps.


    Vetting & interviewing for a scrappy intro to AI safety research.


    I started a thing to help people with executive dysfunction. Fully funded by a bitcoin rando! Thanks!

  • Resolution Council member of a short-lived AI prediction market.