Antonio Stanziola

A smart, kind and curious being

and me.

Making something new.

I have been a Research Fellow in Teorectical and Computational Acoustics at the 🐛 Biomedical Ultrasound Group of UCL. I was also member of the Maths4DL programme.

Previously, I was briefly a consultant at TTP in the Health Tech division, after being a Research Fellow at the Biomedical Ultrasound Group, working on Deep Learning for Transcranial Ultrasound Neurostimulation. I have a PhD in Biomedical Engineering, from Imperial College with a specialization in ultrasound vascular imaging and beamforming with contrast agents.

My experience is in various areas more or less related to ultrasound and deep learning, such as acoustics, ultrasound imaging and beamforming, signal processing, numerical methods. I also have background in differentiable programming, PDE modeling, deep learning for scientific computing, medical imaging and inverse problems, computer vision, and image analysis.

I also like dogs and my parent’s golden retriever Spritz.

news

Jul 5, 2024	I received the ASA Computational Acoustics Early Career Presenter Award at the 186th ASA Meeting for my presentation on the Application of differentiable programming to wave simulation!
Feb 16, 2023	The paper describing `jwave` is available on SoftwareX
Dec 9, 2022	Our latest two papers have been published on Arxiv. The first one proposed a Learned Born Series for fast simulation on high-scattering media, and the second one proposes and auto-diff based method for uncertainty quantification in transcranial ultrasound.

selected publications

Physics-Based Acoustic Holograms

Antonio Stanziola, Ben T. Cox, Bradley E. Treeby, and Michael D. Brown

2023

Bib PDF

@misc{stanziola2023physicsbased,
  title = {Physics-Based Acoustic Holograms},
  author = {Stanziola, Antonio and Cox, Ben T. and Treeby, Bradley E. and Brown, Michael D.},
  year = {2023},
  eprint = {2305.03625},
  archiveprefix = {arXiv},
  primaryclass = {cs.SD},
}

j-Wave: An open-source differentiable wave simulator

Antonio Stanziola, Simon R. Arridge, Ben T. Cox, and Bradley E. Treeby

SoftwareX 2023

Bib HTML PDF

@article{stanziola2023jwave,
  doi = {https://doi.org/10.1016/j.softx.2023.101338},
  url = {https://www.sciencedirect.com/science/article/pii/S2352711023000341},
  author = {Stanziola, Antonio and Arridge, Simon R. and Cox, Ben T. and Treeby, Bradley E.},
  keywords = {Computational Physics (physics.comp-ph), Machine Learning (cs.LG), Mathematical Software (cs.MS), Sound (cs.SD), Audio and Speech Processing (eess.AS), Medical Physics (physics.med-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
  title = {j-Wave: An open-source differentiable wave simulator},
  publisher = {Elsevier},
  journal = {SoftwareX},
  year = {2023},
  volume = {22},
  pages = {101338},
}

A Helmholtz equation solver using unsupervised learning: Application to transcranial ultrasound

Antonio Stanziola, Simon R. Arridge, Ben T. Cox, and Bradley E. Treeby

Journal of Computational Physics 2021

Bib HTML PDF

@article{stanziola2021helmholtz,
  title = {A Helmholtz equation solver using unsupervised learning: Application to transcranial ultrasound},
  journal = {Journal of Computational Physics},
  volume = {441},
  pages = {110430},
  year = {2021},
  issn = {0021-9991},
  doi = {https://doi.org/10.1016/j.jcp.2021.110430},
  url = {https://www.sciencedirect.com/science/article/pii/S0021999121003259},
  author = {Stanziola, Antonio and Arridge, Simon R. and Cox, Ben T. and Treeby, Bradley E.},
  keywords = {Helmholtz equation, Learned optimizer, Unsupervised learning, Physics-based loss function, Transcranial ultrasound},
}