Seminar Schedule: Year 2022




  •  Friday, June the 10th  2022. Time: 12:00 

Lorenzo Capello, Department of Economics, Universitat Pompeu Fabra

An efficient coalescent model for heterochronously sampled data




An efficient coalescent model for heterochronously sampled data

SPEAKER: Lorenzo Cappello

LANGUAGE: english

PLACE Seminari EIO, ETSEIB (ed. Eng. Industrial), Planta 6, Campus Sud. UPC, Av. Diagonal 647, Barcelona

DATE: Friday, 10 June 2022. Time: 12:00

ABSTRACT: The observed sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary parameters. However, it is well understood that inference under this model does not scale well with sample size. In the talk, we will discuss recent works based on a lower resolution coalescent process, the Tajima coalescent, to model longitudinal samples. While the Kingman coalescent models the ancestry of labeled individuals, we will model the ancestry of individuals labeled by their sampling time. Some interesting statistical questions arise when trying to fit this model, and we will discuss them. The need to model longitudinal samples was motivated by applications (e.g., ancient DNA and RNA from rapidly evolving pathogens like viruses). Part of the talk will discuss some of these and possible future directions.

ABOUT THE AUTHOR: Lorenzo is an assistant professor in the Statistics group at Universitat Pompeu Fabra, Department of Economics, and affiliated professor at the Barcelona School of Economics. Previously, he was a postdoctoral scholar in the Department of Statistics at Stanford University. He earned his Ph.D in Statistics in 2018 from the Department of Decision Sciences at the Bocconi University. His research focuses on providing computationally manageable methods and scalable algorithms that adapt to big data problems. His research is motivated by specific questions that arise in applications - in particular population genetics and applied mathematics - and address them using statistical methods. For more information on his research interest, see





Una científica en temps de pandèmia: les matemàtiques com a eina clau en el control de la COVID-19

SPEAKER: Clara Prats

Language: Catalan

Place Manuel Martí Recober Room (FIB),  0 Floor, B6 Building, North Campus. UPC, Barcelona.

DATE: Tuesday, june the 14th, 2022. Time: 12:30

ABSTRACT: COVID-19 turned our lives upside down. From the science world, often too closed, we felt we had to make a step forward (or many!) to serve the society in the middle of the emergency. During these times, mathematics have been the essential tool for analyzing epidemiological data, and transforming a highly complex situation into a set of objective indicators, useful to understand the situation and support decision-making. It has also been an important tool to understand the pandemic's evolution, with the successive waves and the effect of the different adopted measures on their modulation. Finally, mathematical models have been crucial in the analysis of the virus spread dynamics, and on short and medium-term predictions. Mathematics have thus been an indispensable element in interdisciplinary teams that have worked against the pandemics from science and management.

ABOUT THE AUTHOR:  Clara Prats holds a degree in Physics from the University of Barcelona and a PhD in Applied Physics and Simulation in Science from the UPC. She is Associate Professor at the UPC, researcher in the group of Computational Biology and Complex Systems (BIOCOM-SC) and responsible of computational models at the Centre for Comparative Medicine and Bioimaging (CMCiB) of the Germans Trias i Pujol Research Institute (IGTP). Her research focuses on the use of computational modelling for the study of infectious diseases, mainly tuberculosis and COVID-19. Updated information about Clara can be found following this link.


Model selection and model uncertainty quantification in survival models

SPEAKER: María Eugenia Castellanos 

LANGUAGE: spanish

PLACE Seminari EIO, ETSEIB (ed. Eng. Industrial), Planta 6, Campus Sud. UPC, Av. Diagonal 647, Barcelona

DATEWensday, 29 June 2022. Time: 12:00

ABSTRACT: We consider covariate selection and the ensuing model uncertainty aspects in the context of survival regression, specifically for log-normal and Cox regression. The perspective we take is probabilistically handled within a Bayesian framework. One of the critical elements in variable/model selection is choosing a suitable prior for model parameters. Here we derive the so-called conventional prior approach and propose a comprehensive implementation that results in an automatic procedure. Our simulation studies and real applications show improvements over existing literature. Finally, with our proposal is possible to make predictions about the survival function for a specific type of patient, incorporating all the uncertainty probabilistically. Trabajo conjunto con Gonzalo García-Donato y Stefano Cabras.

ABOUT THE AUTHOR: María Eugenia Castellanos es catedrática de Estadística e Investigación Operativa en la Universidad Rey Juan Carlos de Madrid dónde imparte clases en diversos grados y masters, además de ocuparse actualmente de la Subdirección de Relaciones Internacionales e Investigación de la Escuela Técnica Superior de Ingeniería Informática. Anteriormente ha trabajado en la Universidad Miguel Hernández y la Universidad de Valencia. Su investigación se centra en la estadística bayesiana, en ámbitos como el contraste y selección de modelos, el análisis de supervivencia y modelos para valores extremos entre otros. Más información en:



Understanding complex predictive models through Ghost Variables

SPEAKER: Pedro Delicado


PLACE:  Seminari EIO, ETSEIB (ed. Eng. Industrial), Planta 6, Campus Sud. UPC, Av. Diagonal 647, Barcelona

DATE: Friday, 11 november 2022. Time: 12:30

ABSTRACT: Machine Learning (ML) models are more and more accurate in their predictions, many times at the cost of an increasing complexity, which is why we often refer to them as “black boxes”. A whole literature has recently appeared (Interpretable ML) whose purpose is to provide transparency and interpretability to predictive algorithms. In this context, we propose a procedure for assigning a relevance measure to each explanatory variable in a complex predictive model. Assuming that a test set is available, the individual relevance of a variable is computed by comparing the model predictions for the test set with those given for a modified test set, in which the variable of interest is substituted by its “ghost variable”, defined as the prediction of this variable as a function of the rest of explanatory variables. We illustrate our proposal with simulated examples and the analysis of a real data set on rental housing. (Based on a joint paper with Daniel Peña: TEST, 2022, DOI 10.1007/s11749-022-00826-x.)

ABOUT THE AUTHOR:  Pedro Delicado is Professor of Statistics at UPC. His research activity has been mainly dedicated to Functional Data Analysis (focusing on dimensionality reduction and spatial dependency), but in recent years he is also interested in exploring the links between Statistics and Machine Learning, with special interest in the interpretability of predictive models. For more information on his research interest, see