Samuel J. Bell

Machine learning fairness, robustness and metascience.

profile-august.jpg

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

While machine learning is often thought of as a pipeline from data to decision, my research investigates the orthogonal pipeline from researcher decisions to impacts, both positive and negative, in broader society. The choices we make as researchers—how to collect data, which model architecture to use, how we evaluate performance or even how we define the question—result in different conclusions, different systems, and ultimately different outcomes for those our systems affect.

In 2022 I completed my PhD in machine learning as part of the ML@CL group at the University of Cambridge, supervised by Prof. Neil Lawrence. 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 learning more about what I do, or discussing 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. confusion-scores.png
    Confusing Large Models by Confusing Small Models
    Vı́tor Albiero, Raghav Mehta, Ivan Evtimov, Samuel J. Bell, and 2 more authors
    In Out of Distribution Generalization in Computer Vision Workshop, ICCV, 2023
    HTML PDF
  2. 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
  3. 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
  4. graph-example.png
    The Effect of Task Ordering in Continual Learning
    Samuel J. Bell, and Neil Lawrence
    2022
    HTML PDF
  5. 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
  6. 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