Ricardo Pio Monti

Gatsby Computational Neuroscience Unit
University College London

Bio

I am currently a post doc within the Gatsby Unit, UCL working with Aapo Hyvärinen. Prior to this, I completed a PhD within the Statistics Department, Imperial College London under the supervision of Christoforos Anagnostopoulos and Giovanni Montana. My thesis is available here. For further details, please see my CV.

News

Our paper, Variational autoencoders and Nonlinear ICA: A Unifying Framework, was accepted at AISTATS 2020.
Our paper, Interpretable brain age prediction using linear latent variable models of functional connectivity, has been accepted for publication with PLOS ONE. Python code is available here together with a Jupyter notebook.

Selected Publications

Causal discovery with general non-linear relationships using non-linear ica

R P Monti, Kun Zhang and Aapo Hyvärinen

35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019

[arXiv] [NuerIPS Causal Learning Workshop] [Video] [bibtex]

@article{monti2019causal,
  title={Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA},
  author={Monti, Ricardo Pio and Zhang, Kun and Hyvarinen, Aapo},
  journal={35th Conference on Uncertainty in Artificial Intelligence},
  year={2019}
}

Learning population and subject-specific brain connectivity networks via mixed neighborhood selection

R P Monti, Christoforos Anagnostopoulos and Giovanni Montana.

The Annals of Applied Statistics, 2017

[paper] [arXiv] [bibtex] [code]

@article{monti2017learning,
  title={Learning population and subject-specific brain connectivity networks via mixed neighborhood selection},
  author={Monti, Ricardo Pio and Anagnostopoulos, Christoforos and Montana, Giovanni},
  journal={The Annals of Applied Statistics},
  volume={11},
  number={4},
  pages={2142--2164},
  year={2017},
  publisher={Institute of Mathematical Statistics}}

A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data

R P Monti and Aapo Hyvärinen

34th Conference on Uncertainty in Artificial Intelligence (UAI), 2018

[arXiv] [bibtex] [code] [abstract]

@article{monti2018unified,
  title={A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data},
  author={Monti, Ricardo Pio and Aapo Hyvärinen},
  journal={Uncertainty in Artificial Intelligence},
  year={2018}}

Adaptive regularization for Lasso models in the context of non-stationary data streams

R P Monti, Christoforos Anagnostopoulos and Giovanni Montana.

Statistical Analysis and Data Mining, 2018

[paper] [arXiv] [code] [bibtex]

@article{monti2017learning,
  title={Adaptive regularization for Lasso models in the context of non-stationary data streams},
  author={Monti, Ricardo Pio and Anagnostopoulos, Christoforos and Montana, Giovanni},
  journal={Statistical Analysis and Data Mining: The ASA Data Science Journal},
  volume = {11},
  number = {5},
  pages = {237--247},
  year={2018}}

The automatic neuroscientist: a framework for optimizing experimental design with closed-loop real-time fmri

Romy Lorenz, R P Monti, Inês R Violante, Christoforos Anagnostopoulos, Aldo A Faisal, Giovanni Montana, Robert Leech.

NeuroImage, 2016

[paper] [Wired article] [Imperial Press Coverage] [bibtex]

@article{lorenz2016automatic,
  title={The automatic neuroscientist: a framework for optimizing experimental design with closed-loop real-time fMRI},
  author={Lorenz, Romy and Monti, Ricardo Pio and Violante, In{\^e}s R and Anagnostopoulos, Christoforos and Faisal, Aldo A and Montana, Giovanni and Leech, Robert},
  journal={NeuroImage},
  volume={129},
  pages={320--334},
  year={2016},
  publisher={Elsevier}
}

For a full list of publications please see here

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