Mini Bio: I am an assistant professor in the Machine Intelligence Laboratory at the University of Cambridge in the UK. Previously, I was fortunate to be a researcher in the Visual Geometry Group and a research fellow at Balliol College, Oxford.

Research interests

Augmented scientists: The central focus of my current work is developing tools to augment human scientists (by applying machine learning to text, vision and code) to increase their productivity. If you are too, get in touch!

Foundation Models: I have an interest in the recent development of large self-supervised neural networks (sometimes referred to as “foundation models”). See here for some of my online lectures on this topic.

Code generation: As part of the augmented scientists project, I aim to develop tools that allow software development in natural language.

AI safety: This includes both long term risk and near term issues such as the potential for content recommendation algorithms to learn to manipulate humans (“unknowable manipulators”) in a manner that is hard to detect.

Why come to research and build machine learning/augmented scientists in Cambridge? The author of this paragraph is not an impartial judge, but: if you’re passionate about researching and building the machines of the future, Cambridge is the place to be! It has great computing resources, beautiful scenery, and most importantly, a wonderful assembly of smart, creative and energetic people. In the winter it can get a bit chilly, so bring a warm coat.

Other interests

Technical communication: I’m interested in the communication of technical ideas. I have a YouTube channel that represents an exploration on this topic.

Deployment: I have an interest in developing applications that see real-world usage through startups, non-profits etc., particularly those enabled by recent research developments in ML.

News

• 10 / 2022
  The SLRTP workshop for Sign Language Recognition, Translation and Production takes place at ECCV on the 24th of October. Videos of the keynotes can be found here.
• 10 / 2022
  The PMLR proceedings resulting from the NeurIPS 2021 pre-registration workshop are now live.
• 09 / 2022
  ReCo: Retrieve and Co-segment for Zero-shot Transfer will appear at NeurIPS 2022.
• 09 / 2022
  RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection will appear at NeurIPS 2022.
• 08 / 2022
  Serving as an area chair at WACV 2023.
• 07 / 2022
  Automatic dense annotation of large-vocabulary sign language videos will appear at ECCV 2022.
• 12 / 2021
  Thank you to everyone who participated in the Pre-registration workshop at NeurIPS! Video of the event can be found here.
• 11 / 2021
  Our large-scale BOBSL dataset is now available!
• 10 / 2021
  All you need are a few pixels: semantic segmentation with PixelPick won best paper at the ILDAV workshop at ICCV 2021. Congrats to Gyungin and Weidi.
• 09 / 2021
  The NeurIPS 2021 Pre-registration Workshop is soliciting submissions! Deadline: 17th September 2021.
• 09 / 2021
  Joined the ELLIS Society.
• 08 / 2021
  Audio Retrieval with Natural Language Queries was shortlisted for best student paper at Interspeech 2021. Congrats to Andreea-Maria, Sophia, Joao and Zeynep.
• 07 / 2021
  Serving as an Area Chair for BMVC 2021.
• 09 / 2020
  We are soliciting submissions for The pre-registration experiment: an alternative publication model for machine learning research at NeuIPS 2020. Find out more about the workshop here.
• 07 / 2020
  The schedule for the workshop Sign Language Recognition, Translation and Production at ECCV 2020 can be found here.
• 06 / 2020
  Materials from the workshop The End-of-End-to-End: A Video Understanding Pentathlon at CVPR 2020 can be found here. A technical report describing the challenge is available here. A recording of the workshop is on YouTube.
• 11 / 2019
  Materials from the Neural Architects Workshop at ICCV 2019 can be found here. A playlist of the invited talks is available on YouTube.
• 11 / 2019
  Talks and papers from the ICCV 2019 workshop "Should we preregister experiments in computer vision?" can be found here. A playlist of the talks is on YouTube here.
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