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CRM > English > Activities > Curs 2015-2016 > Intensive Research Programme on Statistical advances for complex data
Intensive Research Programme on Statistical advances for complex data

General information

September 8 to November 27, 2015


  • Statistics is the science of data collection, analysis, and interpretation. Research in statistics has always been binded to  scientific discovery and has influenced science, humanity and society by transforming data into knowledge and policies.  Some of the problems we face today include complex data arisen from genomics, clinical trials, radiation analysis, etc. Datasets are currently being, or have already been, collected that contain, hidden in their complexity, important information waiting to be discovered. These discoveries will increase the scientific understanding of our world. The incredible potential of statistics in a world becoming more and more data-driven is the main goal of the Research Programme on Statistical Advances for Complex Data.

    To this end we have distinguished three main topics: 

    1.     Modeling and analysis of  biological/biomedical data where new statistical methods for radiation analysis and omics data analysis will be studied. This includes statistical methods for radiation analysis, to explain, for instance, the underdispersion found in the samples of dicentrics for very high doses of radiation and new multivariate methods for the integrative analysis of omics data in such a way that noise can be minimized and relevant biological information can be extracted. 

    2.     Biostatistical methods for  clinical trials and for complex time-to-event data where multistate models accounting for interval censored data and Bayesian methods for joint modeling of longitudinal and time to event data might help explain the time an HIV patient needs to have their next DXA scan as well as  to assess the impact of a false positive result in screening mammography.  New developments in clinical trials will be approached to handle missing data and to reduce the needed  sample size to detect the effect of a new treatment.

    3.     Statistics and big data where questions such as how big data differ from traditional data sets, and  what implications do these  differences have in the application of classical statistical methods will be studied. The use of distance-based methods when the size of data sets makes prohibitive the explicit computation of distance matrices and  the use of Generalized additive models for  large data sets to  fit complex and potentially more realistic models are among the methods to undertake. Computational together with new statistical tools, are the keys to answering questions about  data we might have had or might have in the future.

    ​ The  program aims to facilitate broader, and deeper, interaction between researchers  interested in either one of these broad fields. To this end, distinguished experts will participate in this program, teaching advanced courses on subjects of current interest. Further activities include workshops and regular seminars, with specific emphasis on opportunities for young researchers to learn new ideas and techniques.


Scientific Commitee

Alejandra Cabaña Universitat Autonòma de Barcelona
Malu Calle Universitat de Vic
Pedro Delicado Universitat Politècnica de Catalunya
Anna Es​pinal Universitat Autonòma de Barcelona
Guadalupe Gómez (coordinator) Universitat Politècnica de Catalunya
Rosa Lamarca Almirall SA
Pere Puig Casado Universitat Autonòma de Barcelona
Montse Rue Universitat de Lleida
Alex Sánchez Pla Universitat de Barcelona

Invited visiting researchers 

You can check a list of the confirmed researchers below (dates are given only for those who have confirmed them). Multiple dates are given to researchers participating in more than one scientific event.

CarmenArmeroUniversitat de València13/10/201530/10/2015
HerbertBrasselmannCenter for Environmental Health25/10/201508/11/2015
AlejandraCabañaUniversitat Autònoma de Barcelona20/09/201531/10/2015
MaluCalleUniversitat de Vic14/09/201527/11/2015
MaluCalleUniversitat de Vic26/10/201527/11/2015
AedinCulhaneHarvard University23/11/201527/11/2015
UraniaDafniNational and Kapodistrian University of Athens07/09/201518/09/2015
PedroDelicadoUniversitat Politècnica de Catalunya14/09/201527/11/2015
RodrigoDienstmannVall d'Hebron Institute of Oncology26/11/201527/11/2015
JochenEinbeckDurham University24/10/201501/11/2015
AnnaEspinalUniversitat Autònoma de Barcelona14/09/201527/11/2015
RonaldGeskusAcademic Medical Center13/10/201501/11/2015
RonaldGeskusAcademic Medical Center14/10/201516/10/2015
GuadalupeGómezUniversitat Politècnica de Catalunya07/09/201527/11/2015
GuadalupeGómezUniversitat Politècnica de Catalunya26/11/201527/11/2015
MoisesGómez-MateuUniversitat Politècnica de Catalunya07/09/201527/11/2015
KyungMannKimUniversity of Wisconsin-Madison04/10/201518/10/2015
CelestinKokonendjiLaboratoire de Mathématiques de Besançon18/10/201513/11/2015
RosaLamarcaAlmirall SA14/09/201527/11/2015
KlausLangohrUniversitat Politècnica de Catalunya13/10/201530/10/2015
JonathanMarchiniUniversity of Oxford25/11/201528/11/2015
GiovanniMontanaKing's College London25/11/201528/11/2015
JoaquínOrtegaCentro de investigación en Matemáticas de México19/09/201526/09/2015
NúriaPerezFundació Lluita contra la SIDA13/10/201530/10/2015
OleguerPlanaAarhus University07/09/201509/09/2015
PerePuigUniversitat Autònoma de Barcelona14/09/201527/11/2015
PerePuigUniversitat Autònoma de Barcelona26/11/201527/11/2015
Adolfo J.QuirozUniversidad de Los Andes20/09/201503/10/2015
MontserratRueUniversitat de Lleida13/10/201530/10/2015
AlexSánchezUniversitat de Barcelona14/09/201527/11/2015
MikisStasinopoulosLondon Metropolitan University11/11/201520/11/2015
MikisStasinopoulosLondon Metropolitan University16/11/201518/11/2015
Bruno MiguelTavaresUniversité d'Aix-Marseille13/09/201520/09/2015
Bruno MiguelTavaresUniversité d'Aix-Marseille16/09/201518/09/2015
SimonWoodUniversity of Bath25/11/201527/11/2015

Advanced courses:

Introduction to Python​, September 8 to 10, 2015. By Jaume Baixerias, Universitat Politècnica de Catalunya

Data mining and social network analysis​, September 16 to 18, 2015​. By Bruno Gonçalves, Université Aix-Marseille

Competing risks: Concepts, methods and software​, October 14 to 16, 2015. By Ronald Geskus, Academic Medical Center

Flexible Regression and Smoothing. The GAMLSS packages in R​, November 16 to 18, 2015. By Mikis Statinopoulos, London Metropolitan University


DoReMi LD-RadStats, October 26 to 28, 2015 


This program is made possible in part by the generous support from the Simons Foundation​, the Universitat Autònoma de Barcelona​ (Gobierno de España project number MTM2012-31118) and the Universitat Politècnica de Catalunya (project numbers Generalitat de Catalunya SGR 464, Gobierno de España MTM2012-38067-C02-01​, and MTM2013-43992-R​), and the Fundación Española para la ciencia y la tecnología​.

Further information

For inquiries about the program please contact the research program's coordinator, Neus Portet at