Mohammad Sadegh Salehi

Mohammad Sadegh Salehi

MRes in Statistical Applied Mathematics

Biography

My interests span research and industrial R&D in Optimisation, Machine Learning, Computer Vision, Imaging Inverse Problems, and Data Science. My work focuses on developing algorithms for learning hyperparameters, particularly in tasks modelled using variational regularisation approaches. These approaches are widely used in image reconstruction, image processing, machine vision, implicit neural network training, and data science. I hold an MRes in Statistical Applied Mathematics, where I worked under the supervision of Dr. Matthias Ehrhardt and Dr. Subhadip Mukherjee. Previously, I completed an MSc in Applied and Theoretical Mathematics at PSL Research University in Paris.

Interests
  • Machine Learning
  • Numerical Optimisation
  • Computational Imaging
  • Variational Methods & Inverse Problems
  • Bilevel Optimisation
Education
  • PhD studies in Statistical Applied Mathematics, October 2021– February 2025

    University of Bath, UK

  • Master of Applied and Theoretical Mathematics, 2020– 2021

    PSL Research University- Dauphine, Paris, France

  • Bachelor of Mathematics and Applications, 2016– 2020

    University of Tehran, Iran

Recent News

  • (Feb-25) Papers accepted to SSVM 2025

    Our papers "Bilevel Learning with Inexact Stochastic Gradients" and "Fast Inexact Bilevel Optimization for Analytical Deep Image Priors" accepted to 10th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2025).

  • (Dec-24) New Research Preprint

    New preprint "Bilevel Learning with Inexact Stochastic Gradients" Subhadip Mukherjee (IIT Kharagpur), Lindon Roberts (University of Sydney), and Matthias Ehrhardt (University of Bath).

  • (Dec-24) New Research Preprint

    New preprint "An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation" Lea Bogensperger (University of Zurich), Matthias Ehrhardt (University of Bath), Thomas Pock (Graz University of Technology), and Hok Shing Wong (University of Bath).

Research Projects

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  • Scalable Algorithms for Bilevel Learning
    Supervisors: Dr. Matthias Ehrhardt, Dr. Subhadip Mukherjee
  • Modified Analytical Deep Image Priors
    Supervisors: Dr. Tatiana Bubba, Dr. Yuri Korolev
  • Gradient-Free Training of Neural Networks (ADMM Approach)
    Supervisor: Dr. Matthias Ehrhardt
  • Impact of Neural Network Architecture and Initialization on Gradient Confusion and Stochastic Gradient Descent
    Supervisor: Dr. Sandipan Roy
  • Interdisciplinary Research Project on Portfolios Design for Clinical Trials
    Supervisor: Dr. Alex Cox
    Partnership with: Roche Company
  • Master Thesis: Linear and Non-linear Acceleration Methods for Alternating Projections and Dykstra's Algorithm
    Supervisor: Prof. Antonin Chambolle

Publications

Quickly discover relevant content by filtering publications.
(2024). An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation.

Preprint

(2024). Bilevel Learning with Inexact Stochastic Gradients.

Preprint

Talks

  • Inexact Algorithms for Bilevel Learning

    International Congress on Industrial and Applied Mathematics (ICIAM) - Waseda University, Tokyo, Japan

  • Inexact Algorithms for Bilevel Learning

    International Conference on Bilevel Optimization - University of Southampton, UK

  • Deep Data-Adaptive Regularisers for Inverse Problems

    Online, Math4DL Seminars (Jointly by the University of Bath, University of Cambridge, and UCL)

Teaching

    University of Bath

  • MA20278: Machine Learning 1 Tutor: Semester 2 2022-2023
  • MA10276: Programming Lab (Python) Tutor: Semester 2 2022-2023
  • MA10207 Analysis 1A Tutor: Semester 1 2021-2022
  • MA10207 Analysis 1B Tutor: Semester 2 2021-2022