Sebastian M. Schmon, PhD

Senior Statt Machine Learning Engineer, Altos Labs.


Welcome to my webpage! I am a machine learning researcher, statistician, and professional curious person. Currently, I’m excited to be part of the team at Altos Labs, where I’m working on developing machine learning methods that contribute to Altos’ mission of transforming medicine through cellular rejuvenation programming. Previously, I had the opportunity to work as a Research Scientist at Improbable, where I gained valuable experience applying my skills to real-world problems. During my time at Improbable, I also served as an Assistant Professor in Statistics at Durham University, where I enjoyed contributing to the academic community and mentoring the next generation of statisticians and machine learning practitioners. My research interests lie at the intersection of statistics, machine learning, and probability theory, with a particular focus on applications in the sciences. I’m also intrigued by the philosophical aspects of science and enjoy digging into the foundations when the opportunity arises. Recently, I’ve been working on projects involving generative AI, including large language models, embeddings, and diffusion models. I obtained my DPhil (the Oxford equivalent of a PhD) from the Department of Statistics at the University of Oxford. Below, you’ll find a list of my publications, highlighting my contributions to the field.


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