Jake Levi

Hi there

About | Vision | Papers | Talks | Social | Blog | Personal ]

About

I'm a PhD student on the AIMS CDT supervised by Professor Mark van der Wilk in the Department of Computer Science at the University of Oxford.

Training modern AI systems requires huge datasets and expensive data centres. In contrast, we as humans can learn much more efficiently, in terms of data and memory consumption. How can we design AI models that can learn as efficiently and robustly as humans can learn? Answering this question is the goal of my research.

Vision

AI is here to stay (like it or not), and is (at least in the short term) massively accelerating productivity for many people on an individual level. There are however 4 common complaints that people have about AI tools: (1) they're expensive, (2) they quickly run out of context length during complex tasks, (3) AI companies have the power to withdraw powerful models at will, (4) training and inference of AI models requires huge data centres which have a massive environmental cost and negative impact on communities.

Given the memory and data efficiency of human intelligence, I believe that one day it will be possible to develop AI models which are (1) open-source, (2) at least as powerful as current frontier models, (3) orders of magnitude less expensive to train, and (4) efficient enough to run locally for everyone on commodity hardware (cheap GPUs) without restrictions on context length. This will avoid all of the above shortcomings, and put AI research back in the hands of academia. Getting there will require a lot of fundamental research which is not necessarily well-aligned with current mainstream AI research, and that is exactly what I am working towards.

Papers

2026 "On The Scalability Of Forward Gradients, Evolution Strategies, And Control Variates" | Jake Levi, Seth Nabarro, Mark van der Wilk | Transactions on Machine Learning Research (TMLR)pdf | openreview | tweet ]
2025 "SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference" | Jake Levi, Mark van der Wilk | Methods and Opportunities at Small Scale (MOSS) workshop, ICML 2025 [ abstract | pdf | poster | tweet ]
2024 "Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games" | Jake Levi, Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster | Pre-print [ abstract | pdf ]

Talks

2026-05-13 "Variational Auto-Encoders (VAEs)" | OccaMLab reading group [ slides | source | repo ]
2025-02-06 "Parallelisable Recurrent Sequence Models" | OccaMLab x OATML joint-group seminar series [ slides | slides with notes ]

Social

Twitter @jakelevi_ml
LinkedIn @jakelevi1996
GitHub @jakelevi1996
Scholar @Jake Levi

Blog

2025-12-28 Is the future of AI in good hands? An analysis of 3 Tweets
2025-10-21 Thoughts about motivation for research
2025-02-09 What ADHD means to me

Personal

Besides rock climbing (trad + sport + bouldering), cinema, and playing classical guitar, my major passion is designing HTML websites. If you would like me to design your website, email me at
<a href="mailto:example@email.com">ERROR! BROKEN LINK</a>.


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