I’m a PhD candidate in the engineering department at the University of Cambridge. I’m interested in solving problems that maximise expected human prosperity, and I’m generally method-agnostic. That said, Reinforcement Learning is proving useful in many settings, so my PhD focuses on improving and applying RL algorithms in various ways. Current areas of interest include:

  • Zero-shot RL: pre-training RL agents capable of solving any task in a given environment;
  • Foundation models in RL: leveraging sequence modelling for generalisation in RL.

If you’re into this sort of thing, I’m always keen to chat so send me an email (srj38 (at) cam(dot)ac(dot)uk).

Scott

Updates

[27/10/23] Our paper Conservative World Models was accepted at the NeurIPS Workshop on Generalisation in Planning. I’ll present it in New Orleans. [paper]

[28/09/23] New pre-print: Conservative World Models [paper]

[19/11/22] Our paper Low Emission Building Control with Zero-Shot Reinforcement Learning was accepted at AAAI 2023. I’ll present it in Washington DC next February. [paper]

[17/10/22] I’m interning as a Research Scientist with Foresight Data Machines, working on RL for autonomous control in heavy industry.

[29/09/22] Our pre-print: Transforming agrifood production systems and supply chains with digital twins was accepted in Nature Science of Food. [paper]

[26/09/22] I went viral on TikTok for wearing pink crocs. [link]

[28/06/22] New pre-print: Low Emission Building Control with Zero-Shot Reinforcement Learning. [paper]

[06/08/21] I’ve become an aligned PhD student with Cambridge’s Artificial Intelligence for the Study of Environmental Risk (AI4ER) Centre for Doctoral Training. [link]

[21/07/21] A story covering my acceptance onto the Alan Turing Institute’s PhD Enrichment Scheme was published on the University of Cambridge’s website. [link]

[19/05/21] I’ve been accepted onto the 2022 Alan Turing Enrichment Scheme. I’ll spend most of next year at Alan Turing HQ in the British Library, London continuing my PhD research. [link]