Yann DAUXAIS
Computer scientist, Ph.D.
ABOUT ME
I am actually postdoctoral fellow at KU Leuven in the DTAI team. I am working on the SYNTH project dedicated to automate data science.
My Ph.D. thesis research field was centered on pattern mining and supervised machine learning. I have mainly worked on the usage of standard machine learning algorithms for mining discriminant temporal patterns. Pattern mining allows to mine patterns that are interpretable for the final user and then directly usable as new knowledge. In pattern mining framework, machine learning allow to represent the numerical dimension of data, for example the temporal dimension, more expressive. This research has contributed to propose new temporal patterns, expressive and interpretable.
Currently : http://yann.dauxais.fr/
ACADEMIC CURRICULUM
Ph.D. thesis: Discriminant chronicle mining
Rennes University | Rennes, France | February 2015 - April 2018
IRISA Laboratory, LACODAM team
Supervisors: David Gross-Amblard, Thomas Guyet and André Happe.
Funding: ANSM (French Agency for the Safety of Health Products), PEPS project (Pharmaco-Epidemiology of Healthcare Products) supported by the University Hospital of Rennes.
Ph.D. defended and written in english [Slides (PDF)]
Jury: Sandra Bringay, Florent Masseglia and Panagiotis Papapetrou.
Computer Science Master’s degree
Caen University | Caen, France | 2012 - 2014
DECIM option (decision support and optimization)
Courses: Graphs, flows and deterministic optimization, Introduction to Data Mining, Stochastic optimization, Metaheuristics, Data mining and business areas, Planning and decision support, Agent and reasoning, Constraints and Advanced Data Mining.
TEACHING
Imperative Programming 2
Rennes University | Rennes, France | 2015 - 2017
Teaching of imperative programming notions through the JAVA language.
Students at second year of computer science bachelor.
Lead professor: Alexandre Termier.
Lab project drafting. 60 hours as lab assistant (3 student groups) and 4 hours as tutorials assistant.
Database and Decision-Making 1
Rennes University | Rennes, France | 2015 - 2017
Lessons of Business Intelligence notions through Business Objects tools.
Students at first year of MIAGE master.
Lead professor: David Gross-Amblard.
30 hours as lab assistant (5 student groups).
Database and Decision-making 2
Rennes University | Rennes, France | 2016 - 2017
Teaching of MapReduce notions and practice through the uses of Hadoop and PigLatin.
Students at first year of MIAGE master.
Lead professor: David Gross-Amblard.
4 hours as lab assistant and 2 hours as tutorials assistant during vacances.
Operating systems organization 1
Rennes University | Rennes, France | 2015 - 2016
Teachings of Internet protocols concepts.
Students at third year of computer science bachelor.
Lead professor: Adlen Ksentini.
8 hours as lab assistant and 4 hours as tutorials assistant.
STUDENT PROJECTS
Master thesis: Design of a global data link constraint for itemsets mining
Caen University | Caen, France | 2014 - 6 months
Supervisors: Patrice Boizumault, Arnaud Lallouet and Samir Loudni.
Establishment of adapted data structures making it possible to apply filtering rules relating to the constraints specific to data mining on a set of Boolean variables expressed in constraint programming.
Annual project of master second year: Solution enumeration algorithm for Horn and 2-SAT formulas in propositional logic
Caen University | Caen, France | 2013 - 2014
Supervisor: Etienne Grandjean.
Understanding and implementing the linear or quasi-linear algorithms proposed in Johann Brault-Baron’s Ph.D. thesis enumerating the solutions of Horn and Horn-renominable formulas.
Annual project of master first year: Reconstruction of a polyomino
Caen University | Caen, France | 2012 - 2013
Supervisor: Jean-jacques Hébrard.
Understanding and implementing an algorithm for reducing the reconstruction of hv-convex polyominos to a 2-SAT problem and then adapting it to enumerate all the polyominos of given perimeters in order to study their properties.
INVITATIONS AND MENTORING
Pint of Science: Artificial intelligence, real stakes!
Warpzone bar | Rennes, France | May 4, 2018
Popularization of data mining and associated application. Slides in french. [Slides (PDF)]
Marine Antigny: Healthcare pathway analysis through topic models
IRISA | Rennes, France | 2018 - 3 months
First year master’s degree (ENSAI) internship mentoring.
François Mentec: Chronicle selection for temporal sequence classification
IRISA | Rennes, France | 2017 - 3 months
First year master’s degree internship mentoring.
PUBLICATIONS
- Dauxais, Y. (2018) Discriminant chronicle mining. (Exctraction de chroniques discriminantes). University of Rennes 1, France
- Dauxais, Y., Gross-Amblard, D., Guyet, T. and Happe, A. (2018) Discriminant Chronicle Mining. In AKDM
- Dauxais, Y. and Gautrais, C. (2018) Predicting Pass Receiver in Football Using Distance Based Features. In MLSA@ECML PKDD
- Gautrais, C., Dauxais, Y. and Guillemé, M. (2017) Multi-Plant Photovoltaic Energy Forecasting Challenge: Second Place Solution. In ECML PKDD Discovery Challenges
- Dauxais, Y., Gross-Amblard, D., Guyet, T. and Happe, A. (2017) Discriminant chronicles mining. Application to care pathways analytics. In AIME
- Guyet, T., Happe, A. and Dauxais, Y. (2017) Declarative sequential pattern mining of care pathways. In AIME
- Dauxais, Y., Gross-Amblard, D., Guyet, T. and Happe, A. (2017) Extraction de chroniques discriminantes. In EGC
- Lemière, V., Dauxais, Y., Boizumault, P. and Lallouet, A. (2016) Mining frequent patterns using CP: a comparative study. In CP Doctoral program
- Balusson, F., Botrel, M.A., Dameron, O., Dauxais, Y., Drezen, E., Dupuy, A., Guyet, T., Gross-Amblard, D., Happe, A., Le Meur, N., Le Nautout, B., Leray, E., Nowak, E., Rault, C., Oger, E. and Polard, E. (2016) PEPS: a platform for supporting studies in pharmaco-epidemiology using medico-administrative databases. In International Congress on e-Health Research
- Dauxais, Y., Gross-Amblard, D., Guyet, T. and Happe, A. (2015) Chronicles mining in a database of drugs exposures. In ECML Doctoral consortium