Dr. Michael Aupetit

Scientist
Social Computing
QCRI offers the opportunity to advance the state-of-the-art in computational science, staying connected to real applications with great potential impact. Working at QCRI within an international team of passionate and inspiring researchers to explore ground-breaking ideas is really challenging and exciting.

Research Focus at QCRI

At QCRI, Michaël's research focuses on the use and usability of machine learning, topological inference and information visualization to bridge the gap between data complexity and analysts understanding in bioinformatics. He is also interested in distributed computing techniques to tackle scalability issues.

Previous Experience

Before joining QCRI, Michaël was a research scientist and senior expert in data mining and visual analytics at CEA LIST in Paris, where he designed cutting-edge algorithms and decision support systems to solve complex industrial problems in health and security domains.  Additionally, Michaël contributes to the Data Visualization and Data Analysis task force of the IEEE Computational Intelligence Society Technical Committee on Data Mining. He advised 5 PhD, 4 Post Doc, 2 engineers, and 16 interns. He also initiated and co-organized 3 international workshops. He has reviewed hundreds of papers for journals and conferences, has more than 60 publications, and holds 2 WO and 1 EP patent.

Professional Experience

  • Engineer and Research Scientist in Computer Science, CEA LIST, LADIS (Data Analysis and Intelligent Systems Laboratory), France - 2008 - 2014
  • Engineer and Research Scientist in Computer Science, CEA DAM (Detection and Geophysics Laboratory), France - 2004 - 2008
  • Post doctoral fellow in Computer Science, CEA DAM (Detection and Geophysics Laboratory), France, 2002 - 2004

Professional Associations and Awards

Associations
  • Data Visualization and Data Analytics task force of IEEE
  • French Association for Artificial Intelligence (AFAI)
  • French Stastical Society (SFdS)

Awards
  • SPSS Best Presentation Award at CAp 2007
Patents granted
  • Method and system for evaluating the class of test data in a large-dimension data space.  2010.  WO/2011/047889
  • Method and system for evaluating the resemblance of a query object to reference objects. 2010.  WO/2011/048219
  • Semi-supervised learning method system for data classification according to discriminating parameters. 2009. EP2180436A1

Education

  • Habilitation for Research Supervision (HDR) in Computer Science, Paris-Sud University - 2012
  • Ph. D in Industrial Engineering, Grenoble National Polytechnic Institute, France - 2001
  • MSc in Robotics and Microelectronics, Montpelier University, France - 1998
  • Computer Science Engineer specialized in Artifical Intelligence, Ecole pour les Etudes et la Recherche en Informatique et Electronique (EERIE), France - 1998


Selected Research

  • Sylvain Lespinats, Michaël Aupetit.  ClassiMap: a supervised multidimensional scaling technique which preserves the topology of the classes.  Submitted to Neurocomputing, Elsevier, 2014
  • Michaël Aupetit, Sanity Check for Class-coloring-based Evaluation of Dimension Reduction techniques. Workshop BELIV @ IEEE VIS 2014, Paris, November 2014
  • Michaël Aupetit, Nicolas Heulot, Jean-Daniel Fekete, A multidimensional brush for scatterplot data analytics. Poster @ IEEE VIS 2014, Paris, November 2014
  • Ricardo de Aldama, Michaël Aupetit, Interpretability in Fuzzy Systems Optimization: A Topological Approach. 15th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014), Montpellier, July 2014
  • Sylvain Lespinats, Michaël Aupetit. ClassiMap : a Supervised Mapping Technique for Decision Support.  Workshop on Visual analytics using Multidimensional @ EuroVis 2013. Leipzig, Germany,  June 2013
  • Nicolas Heulot, Michaël Aupetit, Jean-Daniel Fekete. ProxiLens: Interactive Exploration of High-Dimensional Data using Projections. Workshop on Visual analytics using Multidimensional @ EuroVis 2013. Leipzig, Germany,  June 2013
  • Maxime Maillot, Michael Aupetit and Gerard Govaert. The Generative Simplicial Complex to extract Betti numbers from unlabeled data. Workshop on Algebraic Topology and Machine Learning @ NIPS2012, Lake Tahoe, NV, USA, December 2012
  • Nicolas Heulot, Michaël Aupetit, Jean-Daniel Fekete. ProxiViz: an Interactive Visualization Technique to Overcome Multidimensional Scaling Artifacts. Poster @ IEEE VIS 2012, Seattle, WA, USA, October 2012
  • Maxime Maillot, Michaël Aupetit, Gérard Govaert. A generative model that learns Betti numbers from a data set. ESANN’12 conference, Bruges, Belgium.  April 2012
  • Sylvain Lespinats, Michaël Aupetit. CheckViz : sanity check and topological clues for linear and nonlinear mappings (fast track EuroVis 2010) Computer Graphics Forum journal, 30(1): 113–125, Eurographics, July 2011
  • Michaël Aupetit. Nearly homogeneous multi-partitioning with a deterministic generator. Neurocomputing, 72(7-9): 1379-1389, Elsevier, March 2009
  • Gaillard Pierre, Michaël Aupetit, Gérard Govaert, Learning topology of a labeled data set with the supervised generative Gaussian graph. Neurocomputing, 71(7-9): 1283-1299, Elsevier, March 2008

Follow Us

  • YouTube
  • Twitter
  • Facebook
  • RSS Feed
  • Linkedin
  • github-web.png
Back to Top

In the Media

Forbes fake news pic.jpg

Can AI Put An End To Fake News? Don't Be So Sure

07/10/2018

Fake news was the Collin’s word of the year for 2017 with good reason. In a year where politics-as-usual was torn apart at the seams, high-profile scandals rocked our faith in humanity and the ...

Read More

roadtracer.png

MIT/QCRI system uses machine learning to build road maps

22/04/2018

Map apps may have changed our world, but they still haven’t mapped all of it yet. Specifically, mapping roads can be difficult and tedious: even after taking aerial images, companies still have to ...

Read More

Economist story pic.JPG

Improving disaster response efforts through data

08/02/2018

Extreme weather events put the most vulnerable communities at high risk. How can data analytics strengthen early warning systems and and support relief efforts for communities in need? The size and ...

Read More

Upcoming Events

2019

Dr Farnam Jahanian (2).jpg

“The Future of Higher Education in the Age of Technological Disruption” by CMU President Dr. Farnam Jahanian

Download ICS File 24/03/2019 ,

Dr. Farnam Jahanian, the President of Carnegie Mellon University, will deliver a public lecture, “The Future of Higher Education in the Age of Technological Disruption” at CMU’s Qatar campus on Sunday, March 24.

Read More

QCRI CSAIL Logos.JPG

QCRI - MIT CSAIL 2019 Annual Project Review

Download ICS File 25/03/2019 ,

Executive Overview Sessions Open to publi Date: March 25, 2019 Time: 10:15AM - 5:15PM Venue: Hamad Bin Khalia Reseach Complex Multipurpose Room To view agenda, please click here . To RSVP to this ...

Read More

Torralba.png

"Learning to See" Public talk by Professor Antonio Torralba (MIT-CSAIL)

Download ICS File 25/03/2019 ,

Visit by Antonio Torralba, who teaches machines to automate tasks that a human visual system can accomplish, is part of annual spring research update between QCRI and MIT-CSAIL.

Read More

News Releases

QCRI-iMMAP MOU.jpg

QCRI and iMMAP announce Memorandum of Understanding

03/03/2019

Pact aims to apply data analysis and artificial intelligence techniques to solve humanitarian problems.

Read More

UNDP workshop.JPG

UNDP partners with QCRI to use AI for social good

11/02/2019

Qatar forum on leveraging AI to solve humanitarian problems fills to capacity.

Read More

C. Mohan pic.jpg

Renowned computing expert C. Mohan to bust blockchain myths in Qatar talk

22/01/2019

Well-known inventor of database recovery algorithms to deliver keynote at QCRI's first blockchain workshop.

Read More