Ben Cox

CV

Updated

I am a computational statistician, currently working as a postdoctoral researcher at the Max Planck Institute for Physics. My background is in spatio-temporal Bayesian signal processing, in particular particle filters and parameter inference therein. My current work is on model serialisation: software layers for the creation, transmission, evaluation, and long-term storage of statistical models.

bcox@mpp.mpg.de · bjm.cox@posteo.de

Positions

  • Max Planck Institute for Physics, Postdoctoral Researcher, 2026–present

    Model serialisation: developing software layers for the creation, transmission, evaluation, and long-term storage of statistical models. Loosely associated with the MADMAX and LEGEND groups.

    My position is funded by Germany’s Federal Ministry of Research, Technology and Space (BMFTR) within the ErUM-Data programme, under grant FKZ 05D25PC1 (DEMOS consortium).

Education

  • University of Edinburgh, PhD Statistics, 2020–2025

    Thesis: Parameter estimation in sparse state-space models (doi:10.7488/era/6823). Supervisor: Víctor Elvira.

  • University of Edinburgh, BSc (Hons) Mathematics, 2016–2020

Awards and grants

  • University of Edinburgh – Rice University Strategic Collaboration Award, 2022
  • NERC SENSE CDT studentship (NE/T00939X/1)

Service

  • Reviewer, IEEE Transactions on Signal Processing

Publications

Preprints

  1. J.-J. Brady, B. Cox, Y. Li and V. Elvira, “PyDPF: A Python Package for Differentiable Particle Filtering,” 2026. arXiv:2510.25693 · package · source · docs

Journal articles

  1. B. Cox, S. Segarra and V. Elvira, “Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks,” Signal Processing, 2025. doi:10.1016/j.sigpro.2025.109998

  2. B. Cox, É. Chouzenoux and V. Elvira, “GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models,” IEEE Transactions on Signal Processing, vol. 73, pp. 1562–1576, 2025. doi:10.1109/TSP.2025.3554876 · arXiv:2411.15637

  3. B. Cox and V. Elvira, “Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models,” IEEE Transactions on Signal Processing, vol. 71, pp. 1922–1937, 2023. doi:10.1109/TSP.2023.3278867 · arXiv:2306.11652

Conference papers

  1. B. Cox, É. Chouzenoux and V. Elvira, “Learning a Sparse Polynomial Approximation to the Transition Function of General State-Space Models,” ICASSP 2025, Hyderabad. doi:10.1109/ICASSP49660.2025.10888706

  2. B. Cox, S. Pérez-Vieites, N. Zilberstein, M. Sevilla, S. Segarra and V. Elvira, “End-to-End Learning of Gaussian Mixture Proposals Using Differentiable Particle Filters and Neural Networks,” ICASSP 2024, Seoul, pp. 9701–9705.

  3. M. Sevilla, N. Zilberstein, B. Cox, S. Pérez-Vieites, V. Elvira and S. Segarra, “State and Dynamics Estimation with the Kalman-Langevin Filter,” 57th Asilomar Conference on Signals, Systems, and Computers, 2023, pp. 1372–1376. doi:10.1109/IEEECONF59524.2023.10476814

  4. B. Cox and V. Elvira, “Parameter Estimation in Sparse Linear-Gaussian State-Space Models via Reversible Jump Markov Chain Monte Carlo,” 30th European Signal Processing Conference (EUSIPCO), Belgrade, 2022, pp. 797–801. doi:10.23919/EUSIPCO55093.2022.9909829