• Chargé de recherche / Researcher @ CNRS
  • Postal address:

    7 Avenue du Colonel Roche
    BP 54200
    31031 TOULOUSE CEDEX 4

  • Phone:+33 5 61 33 68 77
  • Mobile:+33 6 48 41 10 36
  • Email: tredan@laas.fr

Short biography/

Since November 2011 I work as a full time researcher in the TSF group . I obtained a PhD degree in computer science from University of Rennes 1 in November 2009. From January 2010 to September 2011, I worked as a postDoc in the FG Inet group, Berlin. I defended my Habilitation a Diriger les Recherches in June 2019.

My current focus is on algorithmic (adversarial) transparency: how to infer properties of remote (online) algorithms ? Which properties can be inferred at reasonable cost ? Can such approaches be used by societies to dispute with tech giants over the control of our digital existences ?

I'm broadly interested in algorithms and graphs. I try to apprehend graphs both as mathematical objects, and as models of the interaction structure of real world objects. I'm particularly interested in algorithms that rely on/exploit/capture such graphs, and how to tailor them for "typical" "real world" interaction structures, where the meaning of "typical" and "real world" is defined by a product of fashion, context, and mathematical docility. For this I typically try a find a blend of abstract models and data mining approaches.

The objective of my thesis, entitled "Structures and Distributed systems", is to study the impact of communications structures on various distributed systems. My Habilitation a Diriger les Recherches focusses on the problem of graph metrology. Graphs are widely used to model real-life homogeneous systems. However, accurately capturing interactions in such systems into a graph is a challenge. I thus explore techniques to evaluate and mitigate the impact of such inaccuracies.

Research interests/



Regular papers:



Workshops & Other:


I teach data mining, exploratory data analysis (EDA), R and a glimpse of machine learning at INSA Toulouse since 2015. This content was initially created for PhD students. The approach is to both insist on the philosophy of scientific data processing, and try to convey good practices that will last, rather than presenting trendy algorithms and systems. Here are the lecture slides.

Former research activities/