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Postdoctoral research fellow


My name is Johann Faouzi and I am a postdoctoral research fellow at Paris Brain Institute, Paris, France. My main research interest is machine learning, with applications to neuroscience and open source software.

My PhD thesis was about predicting impulse control disorders in Parkinson's disease. Impulse control disorders are psychiatric disorders characterized by difficulty in self-control of emotions, thoughts and behaviors. These disorders are common in Parkinson's disease and associated with impaired quality of life for patients and increased burden for caregivers. We investigated the predictability of these disorders from associated or suggested clinical and genetic risk factors by training machine learning algorithms. We also investigated the genetics of these disorders using genetic risk scores. Finally, in a more methodological work, we investigated the integration of time-independent data (such as genetic data) in recurrent neural networks.

I also had the opportunity to work on several other projects with many people, including several literature reviews (one on deep learning for brain disorders, one on impulse control disorders in Parkinson's disease, one on machine learning for Parkinson's disease and related disorders, and one on the prediction of mild cognitive impairment in Alzheimer's disease using machine learning), as well as other studies on machine learning applied to neuroscience (a challenge on brain-age prediction and a study on functional brain connectivities in Tourette disorder).

I am also interested in machine learning for time series. I have created a Python package dedicated to time series classification and contributed to other open source Python packages (not necessarly on time series). I am a strong believer of the importance of good programming practices, in particular for people working in academia (both professors and researchers).


Journal articles9 documents

  • Ninon Burgos, Simona Bottani, Johann Faouzi, Elina Thibeau-Sutre, Olivier Colliot. Deep learning for brain disorders: from data processing to disease treatment. Briefings in Bioinformatics, Oxford University Press (OUP), 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩. ⟨hal-03070554⟩
  • Johann Faouzi, Baptiste Couvy-Duchesne, Samir Bekadar, Olivier Colliot, Jean-Christophe Corvol. Exploratory analysis of the genetics of impulse control disorders in Parkinson's disease using genetic risk scores. Parkinsonism and Related Disorders, Elsevier, 2021, 86, pp.74 - 77. ⟨10.1016/j.parkreldis.2021.04.003⟩. ⟨hal-03298502⟩
  • Manon Ansart, Stéphane Epelbaum, Giulia Bassignana, Alexandre Bône, Simona Bottani, et al.. Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review. Medical Image Analysis, Elsevier, 2021, 67, pp.101848. ⟨10.1016/j.media.2020.101848⟩. ⟨hal-02337815v2⟩
  • Johann Faouzi, Jean-Christophe Corvol, Louise-Laure Mariani. Impulse control disorders and related behaviors in Parkinson's disease: risk factors, clinical and genetic aspects and management. Current Opinion in Neurology, Lippincott, Williams & Wilkins, 2021, Publish Ahead of Print, ⟨10.1097/WCO.0000000000000955⟩. ⟨hal-03298526⟩
  • Giuseppe A Zito, Andreas Hartmann, Benoît Béranger, Samantha Weber, Selma Aybek, et al.. Multivariate classification provides a neural signature of Tourette disorder: Running head: Multivariate analysis of Tourette disorder. Psychological Medicine, Cambridge University Press (CUP), 2021. ⟨hal-03480739⟩
  • Johann Faouzi, Hicham Janati. pyts: A Python Package for Time Series Classification. Journal of Machine Learning Research, Microtome Publishing, 2020, 21, pp.1 - 6. ⟨hal-02883389⟩
  • Lydia Chougar, Johann Faouzi, Nadya Pyatigorskaya, Lydia Yahia‐cherif, Rahul Gaurav, et al.. Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting. Movement Disorders, Wiley, 2020, ⟨10.1002/mds.28348⟩. ⟨hal-03046578v2⟩
  • Baptiste Couvy-Duchesne, Johann Faouzi, Benoît Martin, Elina Thibeau-Sutre, Adam Wild, et al.. Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Frontiers in Psychiatry, Frontiers, 2020, 11, ⟨10.3389/fpsyt.2020.593336⟩. ⟨hal-03136463⟩
  • Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, et al.. Tslearn, A Machine Learning Toolkit for Time Series Data. Journal of Machine Learning Research, Microtome Publishing, 2020, 21, pp.1 - 6. ⟨hal-02883390⟩

Conference papers1 document

  • Johann Faouzi, Samir Bekadar, Olivier Colliot, Jean-Christophe Corvol. Predicting Impulse Control Disorders in Parkinson's Disease: A Challenging Task. International Congress of Parkinson's Disease and Movement Disorders, Sep 2019, Nice, France. ⟨hal-02315533⟩

Book sections1 document

  • Johann Faouzi. Time Series Classification: A review of Algorithms and Implementations. Ketan Kotecha‬. Machine Learning (Emerging Trends and Applications), Proud Pen, In press, 978-1-8381524-1-3. ⟨hal-03558165⟩

Theses1 document