Samuel J. Bell

Machine learning reproducibility, robustness and fairness.

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I’m Sam 👋, a Postdoctoral Research Scientist at FAIR, Meta’s fundamental AI research group. I currently split my time between Paris, France and Cambridge, UK.

In 2023 I completed my PhD in machine learning as part of the ML@CL group at the University of Cambridge, supervised by Prof. Neil Lawrence.

My research focuses on understanding how (and often, whether) contemporary machine learning models actually work. Broadly, I’m interested in reproducibility and metascientific research, tooling and theory for improving the efficiency of machine learning research, and understanding algorithmic biases and questions of fairness.

Previously, I have studied at The Alan Turing Institute, obtained a master’s in natural language processing at the Cambridge Computer Laboratory, and did my bachelor’s in computer science at the University of Manchester.

In between, I’ve simulated financial crises in market risk at Goldman Sachs; built new retail banks at Thought Machine, and developed next generation credit scores at Credit Kudos.

I’m also the Founder and Chair of The Preptrack Foundation, a registered charity building technology for HIV prevention. Our first app, Preptrack, helps people who use PrEP, the medication that eliminates the risk of HIV infection.

If you’re interested in what I do, research collaborations or opportunities, please do drop me a line. My email is samueljamesbell [at] gmail [dot] com. I’d love to hear from you.

Selected publications

  1. sketch-distribution-full-with-group.png
    Simplicity Bias Leads to Amplified Performance Disparities
    Samuel J. Bell, and Levent Sagun
    In ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023
    HTML PDF
  2. svm-final-test-acc-mean-ivr.png
    Modeling the Machine Learning Multiverse
    Samuel J. Bell, Onno Kampman, Jesse Dodge, and Neil Lawrence
    In Advances in Neural Information Processing Systems (NeurIPS), 2022
    HTML PDF
  3. graph-example.png
    The Effect of Task Ordering in Continual Learning
    Samuel J. Bell, and Neil Lawrence
    2022
    HTML PDF
  4. example-task.png
    Behavioral Experiments for Understanding Catastrophic Forgetting
    Samuel J. Bell, and Neil Lawrence
    In AI Evaluation Beyond Metrics (EBeM) Workshop, IJCAI, 2022
    HTML PDF
  5. ICLR
    Perspectives on Machine Learning from Psychology’s Reproducibility Crisis
    Samuel J. Bell, and Onno Kampman
    In Science and Engineering of Deep Learning (SEDL) Workshop, ICLR, 2021
    HTML PDF