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PhD researcher in Computer Science

Top Doctors, Barcelona

Publication Work Experience Teaching Personnal

About Me

I am a 26-year-old researcher who recently earned a Ph.D., having successfully defended my thesis at Inria Rennes in France. As a sociable and meticulous person, I bring forth exceptional multitasking abilities and a demonstrated resilience to high-stress situations.

My expertise lies in the fields of explainability (XAI), natural language processing (NLP), and human-computer interaction (HCI). During my doctoral research, conducted under the guidance of Christine Largouët and Luis Galárraga from the LACODAM team, I focused on Explainability for Machine Learning Models: From Data Adaptability to User Perception

In the current landscape of advanced AI, particularly in deep learning, the escalating complexity of models raises concerns about trust. Relying on black-box solutions poses challenges for technical, ethical, and legal reasons. My Ph.D. aimed to address these issues by extending interpretable methods for machine learning, shedding light on the inner mechanisms of complex models.

Since gaining experience in utilizing techniques such as generating artificial instances to strengthen explanations during my Ph.D., I'm enthusiastic to employ and expand upon these competencies within a professional environment at Top Doctors. I look forward to discovering opportunities to make substantial contributions and drive developments in the domain of generative AI, with a concentrated emphasis on NLP, capitalizing on my newly acquired role.

Explore my academic achievements and research in detail with my academic CV, or delve into a concise overview of my industrial experience with my industry-focused CV.

If you are interested in collaboration, don't hesitate to send an email!


Julien Delaunay Explainability for Machine Learning Models: From Data Adaptability to User Perception. PhD Thesis (INRIA 2023), Rennes. [Full Text] [Presentation].

Julien Delaunay, Antoine Chaffin “Honey, Tell Me What's Wrong”, Global Explainability of NLP Models through Cooperative Generation Traitement Automatique du Langage Naturel (TALN 2023), Paris. [Full Text] [Presentation] [Code].

Julien Delaunay, Luis Galárraga, Christine largouët, Niels van Berkel Adaptation of AI Explanations to Users' Roles. Human-Centered Explainable AI (HCXAI 2023) CHI workshop, Hamburg. [Preprint] [Video].

Joel Wester, Julien Delaunay, Sander de Jong, Niels van Berkel On Moral Manifestations in Large Language Models. Moral Agents for Sustainable Transitions (CHI workshop 2023), Hamburg. [Preprint].

Julien Delaunay, Luis Galárraga, Christine largouët When Should We Use Linear Explanations? Conference on Information and Knowledge Management (CIKM 2022), Atlanta. [Full text] [Video] [Code].

Romaric Gaudel, Luis Galárraga, Julien Delaunay, Laurence Rozé, Vaishnavi Bhargava. s-LIME: Reconciling Locality and Fidelity in Linear Explanations. Intelligent Data Analysis (IDA 2022), Rennes. [Full text].

Julien Delaunay, Luis Galárraga, Christine largouët Improving Anchor-based Explanations. Conference on Information and Knowledge Management (CIKM 2020), Galway. [Preprint] [Video] [Code].

Luis Galárraga, Julien Delaunay, Jean-Louis Dessalles. REMI: Mining Intuitive Referring Expressions. International Conference on Extending Database Technology (EDBT/ICDT 2020), Copenhagen. [Technical report] [Full text] [Video] [Code].

Work experience

2024 -

  • Researcher in Natural Language Processing (NLP) specializing in AI at Top Doctors, Barcelona
  • In my role at Top Doctors, I focus on pioneering the development of chatbots tailored to empower patients in finding the most suitable healthcare specialists based on their specific needs. These chatbots utilize state-of-the-art NLP techniques to comprehend and respond to patients' inquiries in natural language, facilitating a user-friendly and efficient experience. Additionally, I am dedicated to providing intuitive explanations in natural language to healthcare specialists regarding the functionality of complex models. By bridging the gap between technical intricacies and practical healthcare applications, I aim to enhance the accessibility and effectiveness of healthcare services, ultimately benefiting both patients and healthcare providers.

    2020 - 2024

  • PhD student supervised by Christine largouët and Luis Galárraga in the domain of interpretability
  • My Ph.D. focused on two tasks, firstly I studied the technical aspect of explanation, before moving on the human aspect. I hence studied when are linear explanations adapted to a model and target instance that leads to a publication in CIKM 2022. Following this, I visited the Human-Centred Computing group and conducted user studies to measure how feature-attribution, counterfactual, and rule-based explanation methods affect the users' trust and understanding.

    2022 - 2023

  • Visiting Researcher at Aalborg University in Danemark
  • In collaboration with Niels van Berkel, I conducted user studies to quantify the impact of three well-known explanation techniques (feature-attribution, rule-based, and counterfactual) on users' trust and understanding. This visit ended with the submission of a paper at VIS 2023.
    I have been lucky to meet a group of extremely talented and friendly colleagues there. Together we worked on two additional projects that lead to the publication of workshop papers at CHI 2023.


  • Internship supervised by Luis Galárraga and Christine largouët
  • I completed my research master's degree with a six months internship at Inria Rennes. This internship was part of the FABLE project (that leads to my PhD thesis.) I proposed a better discretization method to improve Anchors for tabular data and extended the latent research space used by Anchors to generate textual explanation. This internship ended with a publication in CIKM 2020.


  • Internship supervised by Luis Galárraga.
  • I completed my bachelor degree with a four months internship in a research laboratory. This was my first foot in the research, and I never leave it after. This internship in the domain of semantic web ended with the publication of an article concerning the mining of referring expressions in EDBT 2020. During this internship I coded a programm called REMI, supervised by Luis Galárraga.


    PhD student in computer science,
    University of Rennes 1, France, 2020-

    Research master's degree in computer science,
    University of Rennes 1, France, 2019-2020

    Master's degree in computer science,
    University of Sherbrooke, Canada, 2018-2019

    University degree MIAGE informatics methods applied to business management,
    University of Rennes 1, France, 2015-2018

    Scientific and european baccalaureate,
    High School St Martin, Rennes, France, 2012-2015

    Technical skills

    Programming Languages:
  • Python
    • Matplotlib
    • Transformers
    • spaCy
    • Hugging Face
    • Scikit-Learn
  • Java
    Markup and Typesetting:
  • Latex
    • Survey Platforms:
    • Qualtrics
    • Prolifics
      • Database Management:
      • SQL
        • Version Control:
        • Git
          • Languages

            Native speaker
            Professional level
            Basic skills
            Beginner level


            Mentoring of Jacques Lacourt, a final year trainee at Centrale Marseille.

            Organisation member

          • 2020 - 2022
          • Member of the team organizing the monthly seminars of the Data Knowledge Management department at Inria/Irisa Rennes.
          • 2020 - 2022
          • Member of the Centre Committee at Inria Rennes where I represent the C College.
          • 2018
          • In charge of communication for the association of resident of Sherbrooke University. Agrus