Sebastian M. Schmon, PhD

Senior Statt Machine Learning Engineer, Altos Labs.

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Welcome to my webpage. I am a machine learning researcher, statistician and professional curious person. I am currently at Altos Labs, working on machine learning methods that help the Altos mission to “transform medicine through cellular rejuvenation programming”. Before moving to industry full-time, I worked as an Assistant Professor in Statistics at Durham University and a Research Scientist in the Research team at Improbable. My research interests lie at the intersection of statistics, machine learning and probability as well as applications to the sciences, but I also don’t shy away from the occasional excursion into the philosophy of science. My recent work involves, for instance, work on generative AI including large language models, embeddings and diffusion models. A list of publications can be found below. I obtained my DPhil (Oxford parlance for PhD) at the Department of Statistics of the University of Oxford under the supervision of Arnaud Doucet and George Deligiannidis.

news

Apr 26, 2024 Our paper on Approximate Bayesian Computation with Path Signature got accepted as a Spotlight paper at UAI 2024!
Apr 24, 2024 I am serving as an Area Chair at Neurips 2024.
Mar 01, 2024 Our paper on simulation-based inference for agent-based models has been published in the Journal of Economics Dynamics and Control.
Jul 11, 2023 I have moved to Cambridge to join Altos Labs as a Senior Staff Machine Learning Engineer.

selected publications

  1. CVPR
    Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
    Julian Wyatt , Adam Leach , Sebastian Schmon, and Chris G Willcocks
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
  2. ICLR
    Denoising diffusion probabilistic models on so (3) for rotational alignment
    Adam Leach , Sebastian Schmon, Matteo T Degiacomi , and Chris G Willcocks
    In ICLR 2022 Workshop on Geometrical and Topological Representation Learning , 2022
  3. Biometrika
    Large-sample asymptotics of the pseudo-marginal method
    Sebastian Schmon, George Deligiannidis , Arnaud Doucet , and Michael K Pitt
    Biometrika, 2021
  4. ICLR
    Learning Multimodal VAEs through Mutual Supervision
    Tom Joy , Yuge Shi , Philip Torr , Tom Rainforth , Sebastian Schmon, and Siddharth N
    In International Conference on Learning Representations , 2022