E-mail: johann on the same domain name than this page.
Senior Research Engineer in Artificial Intelligence Algorithmics for Decision Support Systems.
Scientific interests: optimization, search heuristics, artificial intelligence, machine learning, algorithm design
and engineering, automated planning, differential geometryi, graph mining, visualization, user interaction.
I'm currently working on:
- automated design of fitness-landscape-aware stochastic search heuristics,
- interactive visualization of clinical and omics data,
- integration of AI with decision support systems.
- Science: seminal papers and breakthrough in algorithm design.
- Innovation: award-winning open-source stochastic optimization solvers.
- Transfer: new planning algorithms used in actual products.
I also like:
- software development (I master C++, Python and Bash, under Linux),
- popular science communication,
- art/science projects (do not hesitate to contact me if you're an artist looking for a collaboration),
- philosophy and empirical science epistemology.
Some Free softwares I'm proud of:
See more on my pro Github and personal Github…
Ten noteworthy scientific articles
As seen by me.
Pareto-Based Multiobjective AI Planning (2013).
One of the very first Pareto-optimal approach to automated AI planning. This work extends the D A E solver and
proposes a simple multi-objective benchmark with proven optimal solutions. It also outperforms the only
known metric-sensitive solver, competing on a —albeit simpler— aggregated objective problems.
Per instance algorithm configuration of CMA-ES with limited budget
The method that won the 2017 Black Box Optimization Competition in the single objective track. The seminal
work on learning parameters-features mapping that can be embedded within solvers. Classical CMA-ES
versions were leading the competition since years and this work enabled a performances breakthrough.
Divide-and-Evolve: the Marriage of Descartes and Darwin
This solver won the 2011 AI planning competition in the temporal track. It was the first time a stochastic
metaheuristic won the IPC and it was on its hardest problems. It's currently used in command and control
prototypes. This work with ONERA and INRIA also led to the solver that won the following IPC.
Automatic differentiation of non-holonomic fast marching for computing most
threatening trajectories under sensors surveillance
Optimal threatening trajectories computation using a recent fast marching algorithm, which takes into
account curvature constraints. A surveillance system optimization leverages a reverse-mode
semi-automatic differentiation, estimating the gradient of a value function related to a sensor location.
Line formation algorithm in a swarm of reactive robots constrained by
One the first application of swarm intelligence where it actually is a breakthrough innovation. We has
filed a patent on this joint work with the DGA, ENSTA and UBO. The work has led to a whole new
study on the use of mini-drones for mine sweeping, which may be the future of this domain.
Operating Room Planning with Random Surgery Times
My first glance at automated planning. This work is an interesting combination of a classical Operations
Research approach with ideas that originates from the field of stochastic metaheuristics (most notably a
heuristic to solve the pricing sub-problem of a column generation approach).
Robust rigid registration of retinal angiograms through optimization
The application of my thesis. Back then, it was uncommon to use search heuristics in this field and high
resolutions images could not be handled because of inefficient multi-resolution algorithms. This work
showed that global optimization can outperform the windowed approaches generally used in imaging.
- 1: My family name may sometime be written with an accent: Johann Dréo. It is, in any case, pronounced with an accent.
- 2: Updated on 2021-05-06.
Images credits: icons by Jaime Serra, Kirby Wu, Jae Deasigner, Diego Naive, — CC-BY; pictures by Tschaeck, nojhan — CC-BY-SA.