About Us

The continuously growing capacities for the acquisition and storage of data sets call for new approaches to process data efficiently and extract relevant information. In fact, the interest in large data sets is when they are actually 'strange' and allow us to learn about complex mechanisms generating them. In these contexts, it may be difficult or even counterproductive to employ parametric statistical models for the learning process.

The Center for the Discovery of Structures in Complex Data is funded by a grant awarded in 2018 by Iniciativa Científica Milenio from the Chilean Ministry of Economy to a group of Statisticians. The center is based at Pontificia Universidad Católica de Chile. The Center focuses on new statistical approaches for the efficient identification, reconstruction and classification of relevant structural information in complex data sets.

Associate Researchers

Bevilacqua, Moreno

Full Professor. Department of Statistics, Universidad de Valparaiso.

Jara, Alejandro (Director)

Associate Professor. Department of Statistics, School of Mathematics, Pontificia Universidad Católica de Chile.

Porcu, Emmilio

Adjoint Professor. Department of Mathematics, Universidad de Atacama.

Quintana, Fernando (Deputy Director)

Full Professor. Department of Statistics, School of Mathematics, Pontificia Universidad Católica de Chile.

Sing-Long, Carlos

Assistant Professor. Institute of Mathematical and Computational Engineering, School of Mathematics and Engineering, Pontificia Universidad Católica de Chile.

Young Researchers

Beaudry, Isabelle

Assistant Professor. Department of Statistics, School of Mathematics, Pontificia Universidad Católica de Chile.

García-Zattera, María José

Assistant Professor. Department of Statistics, School of Mathematics, Pontificia Universidad Católica de Chile.

Guzman, Cristobal

Ph.D. in Mathematics, Institute for Mathematical and Computational Engineering, School of Mathematics and Engineering, Pontificia Universidad Católica de Chile

Senior Researchers

Maceachern, Steve

Full Professor. Department of Statistics, The Ohio State University.

Müeller, Peter

Full Professor. Department of Mathematics, The University of Texas at Austin.

Prünster, Igor

Full Professor. Institute of Data Science and Analytics, Bocconi University.

Research Lines

For the period 2018-2021, the Center for the Discovery of Structures in Complex Data will be centered on the following aspects of the statistical learning in the context of complex data:

(I) The development, study of properties, and the implementation of scalable Bayesian nonparametric approaches for collection of probability measures indexed by predictors, and when both responses and predictors are defined on non-standard spaces,

(II) The development, study of properties, and the implementation of nonparametric approaches for misclassified doubly-interval-censored time-to-event data, and

(III) The development, study of properties, and the implementation of nonparametric approaches for space and time data.

Visitors

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Past Visitors

  • Alejandro Murua. Professor, Department of Statistics, University of Montreal. September, 7 - 16th, 2018
  • Garritt Page. Associate Professor, Department of Statistics, Brigham Young University. August, 7 - 14th, 2018
  • Evan Ray. Assitant Professor, Department of Statistics, Mount Holyoke College. August, 12 - 17th, 2018

Future Seminars

Past Seminars

Alejandro Murua

Cox regression with Potts-driven latent clusters model

We consider a Bayesian nonparametric survival regression model with latent partitions. Our goal is to predict survival, and to cluster survival patients within the context of building prognosis systems. We propose the Potts clustering model as a prior on the covariates space so as to drive cluster formation on individuals and/or Tumor-Node-Metastasis stage system patient blocks. For any given partition, our model assumes a interval-wise Weibull distribution for the baseline hazard rate. The number of intervals is unknown. It is estimated with a lasso-type penalty given by a sequential double exponential prior. Estimation and inference are done with the aid of MCMC. To simplify the computations, we use the Laplace's approximation method to estimate some constants, and to propose parameter updates within MCMC. We illustrate the methodology with an application to cancer survival.

Luis Gutierrez

A Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control

We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. A real application is also analyzed with the proposed methodology.

Garritt Page

Temporal and Spatio-Temporal Random Partition Models

Data that are spatially referenced often represent an instantaneous point in time at which the spatial process is measured. Because of this it is becoming more common to monitor spatial processes over time. We propose capturing the temporal evolution of dependent structures by modeling a sequence of partitions indexed by time jointly. We derive a few characteristics from the joint model and show how it impacts dependence at the observation level. Computation strategies are detailed and apply the method to Chilean standardized testing scores.

Workshops

Conferences

Outreach Videos

General video about staistics (spanish)
TED talk by Arthur Benjamin
TED talk by Alan Smith
Statistics is for everyone
Statisticians making a difference
Statisticians in other fields

Big DATA Olympiad

This is a contest in which teams of high school students from Chilean schools solve problems of data analysis. The objective of the competition is to stimulate the interest of students in Statistics and Data Science.

The intent of the competition is allow competitors to ‘get their hands dirty’ by performing in depth analysis of the data in order to come up with the best recommendation to address the problem.

The competition has two stages. In the pre-selection phase, the teams must prepare a written report using basic statistical techniques and MS Excel. The teams selected in this stage will be invited to a week of training at the Faculty of Mathematics of the Pontifical Catholic University of Chile. The training will include modern techniques for the description and visualization of data, and on the statistical program R. After the training, the final competition will be carried out. The costs of stay and transfer of selected teams from regions other than the Metropolitan one will be covered by the competition.

The Selection Committee will be formed by Professors of the Department of Statistics of the Faculty of Mathematics of the UC.

For more details please click here.

Outreach Conferences

Outreach Talks

News

Job opportunities

Postdoc possition
We are looking for highly motivated statisticians, data scientists or computer scientists, interested to applying for a Postdoctoral Research Grant from the Chilean National Fund for Scientific and Technological Research (FONDECYT), most likely opening in August 2018. Researchers who attained a Doctoral degree as of January 1st, 2015 or later, may apply to this competition. A local researcher at a Chilean university must sponsor the proposal and I would play that role. Therefore, the proposal should be about Bayesian nonparametric methods. The projects last for 2 or 3 years and the candidate must declare a full-time commitment to the research work. However, its execution is compatible with other paid academic, research and/or outreach activities for of up to 6 hours per week in the sponsoring institution. The grant will cover salary (approximately USD 30,400 / year), travel and operational expenses (USD 6,700/year), and health insurance (USD 670/year). Interested postdoctoral applicants should send a formal application to atjara AT uc.cl including the following information: (i) cover letter, (ii) CV, (iii) publication list, and (iv) summary of research accomplishments and potential research interest. Please do not hesitate to contacting us for further details.

How to Contact Us

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Phone: +56 22 354 4506
Fax: +56 22 354 4506

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Faculty of Mathematics UC,
Campus San Joaquin, Vicuña Mackenna 4860, Macul

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