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France-Mexico Meeting on Data Analysis

Desde Noviembre 03, 2016 09:00 hasta Noviembre 19, 2016 19:00
12 Data Analysis experts from France (5) and Mexico (7) will gather for this seminar on November 3 and 4.
The event will take place in the Instituto de Matemáticas of the UNAM in the Ciudad Universitaria of Mexico City.
Each expert will give a short speech (around 20-25 minutes) about a specific application of data analysis. Each day of the seminar includes a coffee break, a public lecture, a lunch break and ends with a discussion around the perspective of the data analysis science.


  • Avner Bar-Hen (Conservatoire National des Arts et Métiers, Paris) “How mathematicians deal with big data: the example of biostatistics”
  • Adeline Leclercq-Samson (Université de Grenoble-Alpes) “Some big data issues in Social and Human Sciences”
  • Anatoli Louditski (Université de Grenoble-Alpes) “Optimization and statistical learning”
  • Sarah Cohen-Boulakia (Lab. de Recherche en Informatique, Orsay) “The Paris-Saclay Center for Data Science: interdisciplinary projects and collaborative research opportunities”
  • Xavier Vigouroux (Centre Excellence en Programmation Parallèle du Groupe ATOS). “Contribution of High Performance computers to Big Data : Challenges, State of the art and Perspectives”
  • Carlos Gershenson (IIMAS-UNAM), “When Slower is Faster” 
  • Natalia García Colín (INFOTEC, Aguascalientes)“Using machine learning techniques on Mexican data, explorations and early results”.
  • Miguel Nakamura (CIMAT, Guanajuato) "Relating extinction rate to the fossil record".
  • Johan Van Horebeck (CIMAT, Guanajuato) “A look at radial base Kernel PCA in data space”
  • Rolando Biscay (CIMAT, Guanajuato) "Face recognitition in videos by Fisher vectors of binary features with spatial information".
  • Eduardo Gutiérrez Peña (IIMAS-UNAM) “Measures of niche overlap in Ecology”
  • Pablo Suarez-Serrato, (Instituto de Matemáticas-UNAM) "Social Automation, Twitter Bots and Human Rights".

Public lecture:

  • Eric Bonnetier  (Université de Grenoble-Alps) "Localized large gradients in composite media and the Neumann-Poincaré operator."
  • Anatoli Iouditski (Université de Grenoble-Alpes) Ellipsoid algorithm, or why convex programming is "simple".


9:30-9:55 Inauguration  
10:00 – 10:25 Avner Bar-Hen Pablo Suarez-Serrato
10:30 – 10:55 Carlos Gershenson Anatoli Louditski
11:00-11:30 Coffee break Coffee break
11:30 – 11:55 Miguel Nakamura Rolando Biscay
Public Lecture 1
Anatoli Iouditski
Public Lecture 2
Eric Bonnetier
13:00-13:25 Adeline Leclercq-Samson Sarah Cohen-Boulakia
13:30-13:55 Natalia García Colín Johan Van Horebeck
14:00-15:30 Lunch Break Lunch Break
15:30-15:55 Eduardo Gutiérrez Peña Xavier Vigouroux
16:00-16:30 Break Break

Discussion: perspectives (Big group)

Discussion: perspectives (small group)


Iscription fees (including the meals for the 2 days):

  • $750 general public
  • $350 students
  • Scholarships possibilities

Contact: Alma Díaz Barriga - Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.

For further information: www.data-analysis.matem.unam.mx

pdfPoster_Data_Analysis.pdf(917.65 KB)

Organized by:

  • Ambassade de France au Mexique,
  • Instituto de Matemáticas de la UNAM,
  • Laboratorio Solomon Lefschetz (LAISLA), Laboratorio Internacional Asociado del CNRS y CONACYT

Funded by:

  • Ambassade de France au Mexique;
  • Consejo Nacional de Ciencia y Tecnología (Mexico);
  • Universidad Nacional Autónoma de México.

 lembajada01     conacyt     unam


Avner Bar-Hen (Conservatoire National des Arts et Métiers, Paris)
How mathematicians deal with big data: the example of biostatistics.

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the mathematical, statistical and computational sciences. We present some issue for biological sciences with a special focus on health studies.

Adeline Leclercq-Samson(Université de Grenoble-Alpes)
Some "big data" issues in Social and Human Sciences.

Social and Human Sciences generate new data that are challenging to analyse, understand and interpret. In this talk, we will give an overview of some of them: social networks (facebook data, twitter, etc), human psychology (dynamical mouse tracking data), speech and language development, etc.

Anatoli Iouditski(Université de Grenoble-Alpes)
Optimization and statistical learning.

New challenging subjects of statistical inquiry have encouraged massive collaboration between statistics, computer science and optimization. Its objective is in developing scalable algorithms for statistical inference. We discuss some of most efficient techniques of large-scale convex optimization and their applications in statistical learning.

Sarah Cohen-Boulakia(Lab. de Recherche en Informatique, Orsay)
The Paris-Saclay Center for Data Science: interdisciplinary projects and collaborative research opportunities.
Xavier Vigouroux(Responsable du Centre Excellence en Programmation Parallèle du Groupe ATOS)
Contribution of High Performance computers to Big Data : Challenges, State of the art and Perspectives.
Carlos Gershenson(IIMAS-UNAM)
When Slower is Faster.
Natalia García Colín(INFOTEC-CONACYT)
Using machine learning techniques on Mexican data, explorations and early results.
Miguel Nakamura(CIMAT-CONACYT)
Relating extinction rate to the fossil record.
Johan Van Horebeck(CIMAT-CONACYT)
A look at radial base Kernel PCA in data space.

Principal component analysis (PCA) is at the core of many dimension reduction techniques. Its extension based on implicit transformations (aka. kernels) is especially popular in the machine learning literature. Nevertheless, as one works only in an indirect way in the original data space, its interpretation is not always obvious and intuitive. In this talk we look at different kinds of characterizations and explore how linearizations in data space can be useful for the case of large n, based on random projections and random Fourier features.

Rolando Biscay(CIMAT-CONACYT)
Face recognitition in videos by Fisher vectors of binary features with spatial information.
Eduardo Gutiérrez Peña(IIMAS-UNAM)
Measures of niche overlap in Ecology.

In Ecology, the niche of a species is usually defined as a multidimensional hyper-volume in which a species maintains a viable population (Hutchinson 1957). The community structure may be shaped by resource partitioning between co-occurring species, so quantifying the degree of this partitioning (i.e. niche overlap) is very important when studying species co-existence (Geange et al. 2010). The niche space is often described by multiple axes or variables. When all such axes describe continuous measurements, the niche overlap may be quantified using a measure of similarity of two probability density functions, and is often estimated using non-parametric methods. Here we discuss a Bayesian approach to this problem based on Gaussian Dirichlet process mixture models. We also propose a simple exploratory --but more flexible-- measure of niche overlap. Both ideas are illustrated with real data concerning three mammalian species inhabiting the 'El Triunfo' Biosphere Reserve in Chiapas, Mexico. *Joint work with M. Mendoza, A. Contreras and E. Mendoza

Pablo Suarez-Serrato(Instituto de Matemáticas-UNAM)
Social Automation; Twitter Bots and Human Rights.
Joint work with Molly Roberts (UCSD), Fil Menczer (Indiana U.), and Clayton Davis (Indiana U.), and related recent work.
Automated social media accounts can provide useful information, become artistic vehicles, and provide certain benefits. They can also be detrimental to communication between human users, spamming controversial topics or even threatening users.
How can we detect these social bots and measure their presence?
I'll explain how to use the BotOtNot API, developed in Indiana University (http://truthy.indiana.edu/) to check for the presence of social bot accounts in Twitter. As examples I will focus on two cases related to human rights abuses in 2014 and 2016. These sparked online protests and a measurable subsequent activity of a large number of bots. The bot analysis of this empirical data provides an insight into the ease with which online discourse can be affected.
Even though the machine learning setup of BotOrNot was trained in English, its various classifiers can be used to study corpora in other languages as well. The non-language specific classifiers are able to flag hundreds of potential bots in the cases I will present. Using bi-variate distributions the BotOrNot outputs from these classifiers can be visualised to identify bot-areas, where the bots cluster.
The publicly available BotOrNot system can therefore also be used to study activities in other non-English languages where bots have been reported to disturb political discourse.
Our results highlight the need for research into Twitter data, and social media in general, to account for bots.
Eric Bonnetier(Université de Grenoble-Alps)
Localized large gradients in composite media and the Neumann-Poincaré operator.

In composite media, places where inhomogeneities are touching or close to touching are likely to be areas where the solutions of the governing elliptic differential equations present large gradients. This concentration phenomenon proves very interesting in many exciting applications, such as medical imaging, bio-sensing and optoelectronics. It is intimately related to the properties of the Neumann Poincaré operator, an integral operator that can be used as a tool to represent solutions to elliptic differential equations, and also appears in related phenomena of super-resolution and cloaking. In this talk, we describe how the blow up of the gradients can be inferred from the spectral properties of the Neumann-Poincaré operator. This is joint work with Faouzi Triki (Université Grenoble-Alpes).