• Hi!
    I'm Jeremie

    I'm a doctoral researcher at the University of Luxembourg


    Who Am I?

    I'm Jeremie Dauphin. I'm a doctoral researcher at the University of Luxembourg since December 2016. I mainly work on Artificial Intelligence, Knowledge Representation and Reasoning (more specifically formal argumentation and logical reasoning), and Explainable AI.

Cups of coffee
Homeworks graded
Student projects supervised
Papers published
Education

Education

At the University of Luxembourg.
Expected defense: November 2020.

Thesis:
Transparency, Expressivity and Explainability in and through Formal Argumentation.

At the University of Luxembourg.
Specializations validated:

  • Intelligent Systems
  • Information Security

Thesis:
Modeling Arguments about the Liar Paradox using Formal Argumentation Theory.

At Imperial College London.
Topics studied: Artificial Intelligence, software design, machine learning, robotics, ...

Thesis:
Designing a learning tool for argumentation theory.

Publications

Peer-reviewed articles

Other publications

Teaching

Teaching

Discrete Mathematics I (Academic) (Fall 2017 and 2018)

Discrete structures are foundational material for computer science. Relatively few computer scientists will be working primarily on discrete structures, but many other areas of computer science require the ability to work with concepts from discrete structures. The discrete structures covered in this introduction include important material from such areas as set theory, logic, graph theory, and number theory.

Intelligent Agents I: Knowledge Representation (Spring 2017 and 2018)

The objective of this course is to introduce students to knowledge representation and reasoning methods for intelligent agent systems. The course has 4 parts:

  • Belief revision and argumentation
  • Natural language semantics
  • Nonmonotonic reasoning under uncertainty
  • Modal logics for agent reasoning.
In the course, the nature and roles of different formal theories used for individual reasoning and autonomous agents is explained, such as various modal logics, belief change formalisms, or methods for uncertainty management. It defines and applies the basic concepts of one or two non-classical logics (e.g. modal logic and default logics), notably their semantics and proof calculi.

Selected Topics in AI (Fall 2018 and 2019)

The objective of this course is to prepare the student for individual research work (e.g. a master/PhD thesis) in Artificial Intelligence, Knowledge Representation, and Reasoning by reading research literature on a specific topic and realizing a small project. It offers a gentle introduction into current research issues linked to the modeling of intelligent agents, paying special attention to those addressed in the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS). The topics can vary. In the past years, we have discussed in particular causal reasoning, belief dynamics, defeasible reasoning, real-world argumentation, story modeling, generalized reasoning under uncertainty, epistemic decisions, and reasoning about actions. This year's seminar theme will be "Modeling reasoning in scientific texts". We will discuss research literature concerned with this topic and investigate how the proposed theoretical tools can be applied to analyze reasoning in selected scientific texts. Basic knowledge in logic and knowledge representation is required. If necessary, additional background will be provided.

Get in Touch

Contact

3rd floor, Maison du Nombre
6, rue de la Fonte, Esch-sur-Alzette, Luxembourg

+352 4666446041