Sebastian M. Schmon, PhD

Machine Learning Researcher.

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Welcome to my webpage! I am a machine learning researcher, statistician, and professional curious person. Currently, I am working on machine learning for biology and I am excited to be part of the team at Latent Labs, where I’m working on developing frontier models for biologics.

Previously, I had the opportunity to contribute to Altos Lab and their mission of transforming medicine through cellular rejuvenation programming, worked 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.

news

Sep 21, 2025 The paper PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis from my time at Altos Labs has been accepted to Neurips 2025 Datasets and Benchmarks Track.
Jul 25, 2025 Our team at Latent Labs as published a preprint for our new all-atom protein design model Latent-X, where we demonstrate lab-validated state-of-the-art performance for the de-novo design of cyclic peptides and minibinders! Also check out the platform.
Feb 14, 2025 I am excited to join Latent Labs to work on the next generation of biologics frontier models!
Oct 12, 2024 Our team at Altos Labs will be presenting our paper PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis as a spotlight presentation at the Neurips workshop on AI for New Drug Modalities. If you’re planning to attend, feel free to reach out — we’d love to connect!
Oct 11, 2024 Together with Bastian Grossenbacker Rieck and Juius von Rohrscheidt we investigate what happens when Bayesian Computation Meets Topology. Now accepted at TMLR!

latest posts

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
    Capturing Label Characteristics in {VAE}s
    Tom Joy , Sebastian Schmon, Philip Torr , Siddharth N , and Tom Rainforth
    In International Conference on Learning Representations , 2021
  3. 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
  4. Biometrika
    Large-sample asymptotics of the pseudo-marginal method
    Sebastian Schmon, George Deligiannidis , Arnaud Doucet , and Michael K Pitt
    Biometrika, 2021
  5. 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
  6. PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
    Yan Wu , Esther Wershof , Sebastian M Schmon , Marcel Nassar , Blazej Osinski , Ridvan Eksi , Kun Zhang , and Thore Graepel
    arXiv preprint arXiv:2408.10609, 2024