Rémi Varloot


I am a PhD student at the Microsoft Research–Inria Joint Center. My advisors are Laurent Massoulié, Marc Lelarge and Ana Bušić.

E-Commerce Recommendation Systems

My latest research topic. This is currently work in progress.

Rapid Mixing of Local Graph Dynamics

Most of my PhD has consisted in studying the mixing time of particular families of dynamic graphs. These were characterised by local edge dynamics for which the stationary distribution gives expanders with high probability. Such distributions are commonly used to model actual networks, such as social or peer-to-peer networks.

For a well-chosen dynamic over graphs with n vertices, we have shown that the mixing time is of order O( n logk n ). This implies that there are O( logk n ) updates per vertex, i.e. that the dynamic converges rapidly.

Perfect Sampling

This work consisted in devising a variant of the coupling from the past algorithm. The aim was to reduce the coupling time by skipping events: only active events which alter the state of the bounding chain are considered.

The algorithm is given explicitly, alongside a proof of its correctness and upper bounds on the improved coupling time for sampling from the stationary distribution of some toy queuing models.


Majord'Home Architecture

I worked at the Alcatel-Lucent Bell Labs on the elaboration of the Majord'Home architecture, an SDN approach to delegating the management of home networks to ISPs.

French Internet Resiliency

I worked for a bit for the French Internet Resiliency Observatory at the French Network and Information Security Agency (ANSSI). I implemented an algorithm that constructs a BGP-level map of the French Internet. This was used to study its resiliency and detect critical ASes.

Peer-to-peer super-scalability

I was in charge of the experimental results for a paper on the scalability of peer-to-peer networks.

Network tomography

I used new compression-based methods for clustering in the field of network tomography.


I have been in charge of some tutorial classes at the École Normale Supérieure.

Network Models and Algorithms

This class aims at studying the different mathematical and algorithmic techniques for modeling and studying networks of varying nature: communication networks, social networks, energy distribution networks, etc.

Random Structures and Algorithms

This class introduces the basics of probability theory and its applications to certain aspects of computer science: algorithms, communications networks, etc.

Start of Year Projects

First year students are given 2–3 weeks to complete a simple project in groups of 2 to 4. This is generally done under the supervision of a PdD student or a lecturer.

Dancing Links / Algorithm X

The aim was to implement Knuth's Algorithm X using dancing links, and to apply this to problems such as the brute-force solving of Sudoku.

Traffic Modeling

The aim was to simulate traffic in a grid road network, and to empirically determine the arrival threshold at which deadlocks appear.

Supervising Entry Exams

I was one of the students in charge of installing and supervising the practical computer science sessions for the ÉNS entry exam.

I namely wrote up two documents to help students: one describing how to set up a similar environment on their home computer to practice, and a "cheatsheet" of useful commands, which they were allowed to have with them during the exam. Though the exam changes over the years, these should still be somewhat relevant.

About Me

Computer Skills

Scientific Programming
Python ◆◆◆

Numpy Scipy Scikit-Learn

Pandas NetworkX

Jupyter Notebook

LaTeX ◆◆◆

PGF/TikZ Beamer BibTeX

Other Languages ◆◇◇

Mathlab Scilab Maple R

Web Development

Pug Less Sass

reveal.js D3.js plotly.js Three.js

JavaScript ◆◆◇

NodeJS CoffeeScript TypeScript

Gulp AngularJS nw.js

PHP ◆◇◇

Composer SilverStripe

Software Development
C/C++ ◆◆◆

Standard Libraries Boost

C# ◆◇◇

.NET Unity

Java ◆◇◇


Other Skills
Spoken Languages

Fluent in French and English

Version Control ◆◇◇

Git SVN Mercurial

Office Tools ◆◆◇


Education and Experience

PhD in Computer Science
Start of PhD
ÉNS Graduation
Pre-Doctoral Experience
Alcatel-Lucent Bell Labs

5 months


6 months

École Normale Supérieure
Master's Degree

Parisian Master in Computer Science (MPRI)

Internship at Dyogene (Inria)

4½ months

Internship at Trec (Inria)

4½ months

Bachelor's Degree

Computer Science Department

Internship at the University of Melbourne

2 months

Previous Education

Lycée Hoche

International Scientific Baccalauréat

Lycée International de Sèvres

International Option: English

Specialty: Mathematics

International Mathematical Olympiades

Honorary Mention

First prize at the Concours Général de Mathématiques

French national exam

Paris Model United Nations

3 years

Harvard Model Congress Europe