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Xavier Ferrer

Data Scientist / PhD Visiting Research Associate at King's College London

About Me

Hello there!

I am a passionate Data Scientist and a Visiting PhD Research Associate in Machine Learning at King's College London, with over 8 years of experience in research, artificial intelligence, and machine learning. I have a keen interest in AI, Data Science, and Natural Language Processing. Proficient in Python, Java, and C#, I am always eager to learn and apply new techniques to tackle complex problems.

As a lifelong learner, I am constantly seeking out new experiences and opportunities to expand my knowledge and skills. Whether it's exploring new programming languages or delving into the latest breakthroughs in AI research, I am always looking for ways to push myself and stay ahead of the curve.

Thank you for visiting my website, and I look forward to sharing my passion for coding and machine learning with you. And don't forget to visit the AI Jungle, where I collect code and random projects related to AI!

  • Machine Learning
  • Natural Language Processing
  • Artificial Intelligence
  • Fairness
  • Python
  • PySpark
  • Airflow
  • AWS
  • Java

King's College London

2018 - present

Postdoctoral Research Associate / Visiting Research Associate

Postodoctoral Research Associate, on Discovering and Attesting Fairness and Digital Discrimination in Machine Learning models (DADD).

IIIA-CSIC

2014 - 2017

PhD in Artificial Intelligence

Excellent + Cum Laude. Artificial Intelligence Research Institute of the of the Spanish Council for Scientific Research (IIIA – CSIC)

Universitat Autònoma de Barcelona

2013

MSc Artificial Intelligence and Computer Vision

KU Leuven + Universitat Autònoma de Barcelona

2011

BSc Computer Science + MSc Artificial Intelligence

Education

Publications

2023

  • Mary Bispham, Suliman Kalim Sattar, Clara Zard, Xavier Ferrer-Aran, Jide Edu, Guillermo Suarez-Tangil, Jose M. Such. Misinformation in third-party voice applications. ACM conference on Conversational User Interfaces 2023 (CUI'23). [paper]

    • 2022

  • Mary Bispham, Clara Zard, Suliman Sattar, Xavier Ferrer-Aran, Guillermo Suarez-Tangil, Jose M- Such. Leakage of Sensitive Information to Third-Party Voice Applications. Proceedings of the 4th Conference on Conversational User Interfaces (CUI) 2022. [paper]
  • Jide Edu, Xavier Ferrer-Aran, Jose M. Such, Guillermo Suarez-Tangil. Measuring Alexa Skill Privacy Practices across Three Years. Proceedings of the ACM Web Conference 2022. [paper] [github]

    • 2021

  • Xavier Ferrer-Aran, Tom Van Nuenen, Natalia Criado, Jose Such. Discovering and Interpreting Biased Concepts in Online Communities. IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 4, pp. 3672-3683 [paper] [web]
  • Jide Edu, Xavier Ferrer-Aran, Jose M. Such, Guillermo Suarez-Tangil. SkillVet: Automated Traceability Analysis of Amazon Alexa Skills. IEEE Transactions on Dependable and Secure Computing 2021 [paper] [github]
  • Xavier Ferrer-Aran, Tom van Nuenen, Jose M. Such, Natalia Criado Pacheco. Discovering and Categorising Language Biases in Reddit. Proceedings of the International AAAI Conference on Web and Social Media 2021 (ICWSM) [paper] [web] [github]
  • Natalia Criado, Xavier Ferrer-Aran, Jose M Such. Attesting Digital Discrimination Using Norms. International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, pp. 16-23 [paper]
  • Xavier Ferrer-Aran, Tom van Nuenen, Jose M. Such, Mark Coté, Natalia Criado Pacheco. Bias and Discrimination in AI: a cross-disciplinary perspective. IEEE Technology and Society Magazine 40, vol. 2, pp. 72-80 [paper]

    • 2020

  • Tom van Nuenen, Xavier Ferrer-Aran, Jose M. Such, Mark Coté. Transparency for whom? Assessing discriminatory artificial intelligence. Computer 53. vol. 11, pp. 36-44 [paper]
  • Natalia Criado-Pacheco, Xavier Ferrer-Aran, Jose M. Such. Is my program sexist? Using Norms to Attest Digital Discrimination. IEEE Technology and Society Magazine 2020 [paper]
  • Natalia Criado Pacheco, Xavier Ferrer-Aran, Jose M. Such. A Normative approach to Attest Digital Discrimination. FAIR Workshop of the 24th European Conference on Artificial Intelligence 2020 (ECAI) [paper] [github]

    • 2019

  • Xavier Ferrer-Aran, Natalia Criado-Pacheco, Jose M. Such. Attesting Biases and Discrimination using Language Semantics. Responsible Artificial Intelligence Agents Workshop AAMAS 2019 [paper]

    • 2017

  • Xavier Ferrer-Aran. Concept Discovery and Argument Bundles in the Web of Experiences. Universidad Autónoma de Barcelona [paper]
  • Xavier Ferrer-Aran, Enric Plaza. On argument bundles in the Web of Experiences. AI Communications, vol. 30 (3-4), pp. 235-249 [paper]

    • 2016

  • Kemo Adrian, Paula Chocron, Roberto Confalioneri, Xavier Ferrer-Aran, Jesús Giráldez-Cru. Link Prediction in Evolutionary Graphs. Artificial Intelligence Research and Development: Proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence, vol. 288, pp. 187-196 [paper]
  • Xavier Ferrer-Aran, Enric Plaza. Concept discovery and argument bundles in the Experience Web. Case-Based Reasoning Research and Development: 24th International Conference on Case-Based Reasoning, ICCBR 2016 [paper]

    • 2015

  • Yoke Yie Chen, Xavier Ferrer-Aran, Nirmalie Wiratunga, Enric Plaza. Aspect selection for social recommender systems. Case-Based Reasoning Research and Development: 23rd International Conference, ICCBR 2015 [paper]

    • 2014

  • Xavier Ferrer-Aran, Yoke Yie Chen, Nirmalie Wiratunga, Enric Plaza. Preference and sentiment guided social recommendations with temporal dynamics. Research and Development in Intelligent Systems XXXI: Incorporating Applications and Innovations in Intelligent Systems [paper]
  • Yoke Yie Chen, Xavier Ferrer-Aran, Nirmalie Wiratunga, Enric Plaza. Sentiment and preference guided social recommendation. Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014 [paper]

    • 2012

  • Andreas Pashalidis, Nikos Mavrogiannopoulos, Xavier Ferrer-Aran, Beñat Bermejo Olaizola. For human eyes only: security and usability evaluation. Proceedings of the 2012 ACM workshop on Privacy in the electronic society, pp. 129-140 [paper]

    • Projects

    The AI Jungle

    Welcome to "The AI Jungle" – a wild and exciting project where we embark on a thrilling adventure through a jungle of code related to AI and ML! In here you will find a diverse array of code snippets and unordered projects, from snippets of code to sort pandas dataframes to Fine-tuning LLMs and Deep Learning models.

    Enter the AI Jungle

    Discovering And Interpreting Conceptual Biases (DAICB)

    The DAICB is a tool to interactively compare the discovered biases between two attribute concepts inherent in large textual datasets taken from the internet, as captured by Word Embeddings models. It is an extension over previous work presented at ICWSM, which can be accessed here.

    View Project

    Language Bias Visualiser

    The DADD (Discovering and Attesting Digital Discrimination) Language Bias Visualiser is a tool to interactively compare men and women stereotypes inherent in large textual datasets taken from the internet, as captured by Word Embeddings models.

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    Photo Geo-Tagging

    The project aims to update the metadata of the image files present in a directory by adding location and date information. This is achieved by mapping the file names to corresponding location and date information stored in a CSV file, and updating the image EXIF metadata with the corresponding values.

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    Are We There Yet? Alexa Market Comparison

    Files used to organise and corectly map traceability data for Alexa skills between csv and json files during three different years 2019, 2020, 2021. This repo also contains example executions that show how to compare the datasets over the years. This project was used in the creation of the paper "Alexa, are we there yet?"

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    View More Projects

    Attesting Digital Discrimination Using Norms

    This repository contains the source code of the original paper 'Attesting Digital Discrimination Using Norms' accepted at the International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), and for the paper "A Normative approach to Attest Digital Discrimination" accepted at AI4EQ workshop of the 24th European Conference on Artificial Intelligence 2020 (ECAI 2020), both part of the project Discovering and Attesting Digital Discrimination (DADD).

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    Sentence Creator

    SentenceCreator Generates Similar Sentences Using GoogleTranslate Python Library.

    Consist of a simple wrapper built on top of the google translate python library to generate similar sentences when provided a seed sentence. Given a sentence in any language, SentenceCreator generates similar sentences by leveraging the googletranslation library: the algorithm translates the original sentence to another language (up to `n` times), and then back to the original language. This results in similar but not exactly lexically equivalent sentences.

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