Oncodash: a decision support system for tumor boards.
Paradiseo: an open-source full-featured evolutionary computation framework which main purpose is to help you write your own stochastic optimization algorithms. See the summary publication: 22 Years of Paradiseo.
Frictionlesser: a tool to search for cancer signatures in transcriptomics data.
Descarwin: The DaE and YAHSP temporal planning solvers, which won IPC 2011. Implemented with Paradiseo.
Elpida: The simplest possible message protocol able to connect black-box optimization problems and solvers.
Using performance fronts for parameter setting of stochastic metaheuristics (2009).
A seminal work in the area of multi-objective parameter setting. It's also one of the first works stating that
an experimental study can be used to design parameter-free metaheuristics on a sound basis by studying
correlations between an estimated Pareto front distribution and performances.
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms (2024).
A proof that a bi-variate distribution of the closed set of optimizers' performance trajectories (inspired by multi-objective parameter setting) is more generic mathematical object than the traditionally used expected runtime distributions. This has very interesting implications in how to assess the performances of randomized black-box optimizers.
Pareto-Based Multiobjective AI Planning (2013).
One of the very first Pareto-optimal approach to automated AI planning. This work extends the DAE solver and
proposes a simple multi-objective benchmark with proven optimal solutions. It also outperforms the only
known metric-sensitive solver, competing on an —albeit simpler— aggregated objective problems.
Per instance algorithm configuration of CMA-ES with limited budget (2017).
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 (2011).
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.
Line formation algorithm in a swarm of reactive robots constrained by
underwater environment (2015).
One the first application of swarm intelligence where it actually is a breakthrough innovation. We have 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 (2007).
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).
I am a research engineer with a widespread culture and experience in several corners of computer science and applied research, with a background in biology and free-culture movement.
I hold master's degrees in "Ecology, Ethology & Evolution", in "Biomathematics", and a Ph.D. in "Biomedical Engineering & Algorithmics".
I started my career working in academics, conducting research on biomedical applications of operation research and teaching computer science & network security. I then worked for 13 years in an industrial research center, conducting research on various subjects around AI; generally applied to automated decision & design; mainly on planning of advanced robotic systems and automated design of optimization solvers. I am now working as a research engineer at Institut Pasteur.
I have been involved in various open-source software projects (some of them very successful, like Liquidprompt), and free-culture movements; most notably in Wikipedia (since 2003) and Wikimédia France (since… the beginning, and for which I have been an early board member).
Adaptation of the ant colony optimization metaheuristic to continuous problems, applications in biomedical engineering. Metaheuristics, ant colony optimization, estimation of distribution algorithms, imaging.
Sorbonne University
(Paris)
&
Free University of Brussels
(Brussels)
Postgraduate certificate “Biomathematics”
Establishment of a model of an ant colony behaviour. Multi-agent modeling, complex systems, self-organization.
University of Rennes 1
(Rennes)
Master's degree “Ecology, Ethology & Evolution”
Establishment of a differential equations model of a social monkey behaviour.
Skills
Since I left my position as a researcher, my job is to help people conduct their own research. I can most notably help you on the following topics.
Decision-Support Systems
Design and implementation of decision-aid software
Workflow modelling, automated decision integration, data representation and integration, high-level UX design..
Visualization and diagrams
How to summarize complex data/concepts in a visual way.
Integration of third-party modules
How to design API to use external services, how to integrate software that does not really want to be integrated.
Automated Decision
Score function modelling
How to design a metric defining a quality for a solution to a decision problem, while maintaining good mathematical properties.
Optimization problem modelling
How to design a formal model of a decision problem to be automatically solved by a computer.
Solving automated configuration problems
How to set parameters of a complex system so as to maximize its performances.
Scientific Computing
Efficient algorithmics
How to cope with combinatorial explosion or curse of dimension when implementing complex algorithms.
Highly modular software architectures
How to structure your code to allow efficient —and automated— exploration of your ideas.
Parallel Programming
Multi-core and multi-process programming, Message Passing Interface.
Modern C++ / Python bindings
How to program with C++ using —almost— the same concepts than in Python, or within Python itself.
Shell scripting
How to use the existing Unix tools to —very— efficiently automatize any task.
Artifical Intelligence
Semantic graph mining
How to build, integrate and exploit semantic knowledge graphs.
Search heuristics, metaheuristics or evolutionary computation
How to solve hard optimization problems.
Automated planning
How to compute shortest paths, and more generally optimize sequences of actions.
Supervisor of masters students having won the regional "André Blanc-Lapierre" prize
French society of electricity, electronics and information & communication technologies
Among the winners of the Open Optimization Competition
Facebook AIR, Sorbonne Université, Leiden University
Among the winners of the Open Optimization Competition
Facebook AIR, Sorbonne Université, Leiden University
Best poster award
RADAR conference
Winner team of the Black-Box Optimization Competition
Association for Computing Machinery, our student also won in 2019
Winner team of the International Planning Competition
International Conference on Automated Planning and Scheduling
Ph.D. Genealogy
Below is the history of my Ph.D. "descendants" and "ancestors". I.E. the Ph.D. students I supervised (not always as an official director), and the list of the directors of my directors (while historically fun, probably not very useful).
Ph.D. students
Ph.D.
QuentinRenau
(France)
Algorithmics
Ph.D.
NacimBelkhir
(France)
Algorithmics
Ph.D.
ThomasSousselier
(France)
Robotics
Ph.D. Ancestors
Ph.D.
JohannDreo
(France)
Algorithmics
Ph.D.
PatrickSiarry
(France)
Algorithmics
Ph.D.
GérardDreyfus
(France)
Machine Learning
Ph.D.
JacquesLewiner
(France)
Physics
Ph.D.
Paul HermanErnst Meijer
(Netherlands)
Physics
Ph.D.
Hendrik AnthonyKramers
(Netherlands)
Quantum Physics
Ph.D.
PaulEhrenfest
(Austria)
Particle Physics
Ph.D.
LudwigBoltzmann
(Austria)
Mechanical Statistics
Ph.D.
JožefŠtefan
(Austria)
Thermodynamics
Ph.D.
Andreasvon Ettingshausen
(Austria)
Mathematics & Physics
Ph.D.
IgnazLindner
(Austria)
Mathematics
Ph.D.
Georg Jurij Bartolomej Vehavon Vega
(Slovenia)
Mathematics
Ph.D.
GabrielGruber
(Austria)
Mathematics
Ph.D.
Joseph Giuseppe Jakobvon Maffei
(Austria)
Unknown (to me)
Ph.D.
Nikolaus Boda Podavon Neuhaus
(Austria)
Mathematics, Entomology, Physics
Notes
1: My family name may sometime be written with an accent: Johann Dréo. It is in any case actually pronounced with an accent (sounds like "jo-ann dré-o", IPA: [ʒoan dʁeo]).
