New PhD Students of 2021-2022 course

Basque Center for Applied Mathematics – BCAM has welcomed to the center a new group of PhD students for the course 2021-2022 and they have joined to the rest students, who number 41 in total.

They are going to work mostly in mathematics and informatics, but in the different research groups at BCAM. Below you will find more information about the doctoral theses they will be working on:

Ainhize Barrainkua (BCAM PhD Student)

“I am a graduate in Physics and Electronic Engineering, and I have a Master’s degree in Computational Engineering and Intelligent Systems”.

Project: Trustworthy machine learning

Abstract: In this new era in which some life-changing decisions rely on the predictions of mathematical models, their fairness is critical. Whenever any government, business, bank, social organisation, school makes use of a machine learning system, the latter cannot discriminate any individual because of their personal characteristics, such as their gender. Parallel to working towards a fairer society, every system we use to make decisions whose output involves human lives, needs to ensure the same principle.  In fact, it is a research project involving topics such as algorithmic fairness under uncertainty in a static setting, and transparency in algorithmic fairness.

Arkaitz Bidaurrazaga (Severo Ochoa Predoc 2020)

“Working as a PhD student on probabilistic models in the Machine Learning group of BCAM. I obtained my Double Degree in Physics and Electronic Engineering (University of Basque Country) in 2019, and my Master Degree in Computational Engineering and Intelligent Systems at San Sebastian in 2020”.

Project: Looking for the sinergy of Markov Networks and Boltzmann Machines

Abstract: Energy-based models (EBM) represent probability distributions that factorize according to an additively decomposable energy-function. Depending on the form of the additive decomposition we can define different families of EBMs. The main research line of this thesis consists of analyzing and finding synergies between Boltzmann Machines and Markov Networks, and proposing the Markov-Boltzmann models, a more flexible EBM that generalizes them.

Carlo Estadilla (Basque Government Health Project)

“I took my graduate degree in applied mathematics from the University of the Philippines – Diliman, where I studied the optimal control of an HIV/AIDS epidemic model. My general interest is in the optimization of interventions and policies affecting population-level dynamics and behavior”.

Project: Epidemiological and economic evaluation of public health disease control measures during the COVID-19 pandemic

Abstract: This project aims to perform an economical evaluation of public health control measures implemented during the COVID-19 pandemic in the Basque Country. By refining the SHARUCD model we will investigate, from a health sector perspective, the impact of the COVID 19 pandemic on clinical outcomes such as infections and mortality, health-related resources such as diagnostic tests, intensive care units admission and quarantine and its costs for the Basque population. The threshold at which interventions are considered cost-effective will be estimated, including the evaluation of vaccine programmes implementation in the following years.

Mikel Florez (BCAM PhD Student)

“I am a PhD student at BCAM working on harmonic analysis. In particular, I am interested in the theory of singular integrals, directional singular integrals, square functions, and time-frequency analysis”.

Project: Topics in harmonic analysis related to Carleson’s theorem and Rubio de Francia square functions

Abstract: The thesis lies in the area of harmonic analysis with particular emphasis to the theory of singular integrals, directional singular integrals, square functions, and time-frequency analysis. The driving themes of investigation are Kakeya-type directional maximal functions and square function estimates appearing in the work of J.L. Rubio De Francia. We are  interested in these notions in dimensions greater or equal to two; in this context only limited results are known. The study of such operators requires analysis both in space as well as in frequency. A relevant tool is the appropriate formulation of Rubio de Francia square function estimates in the directional context. Because of the higher-dimensional geometry, these  square functions are directional, and their analysis is intimately connected to the study of directional averages and directional Hilbert transforms. A common tool in the modern study of both directional singular integrals, and the Rubio de Francia square function estimates is a time-frequency analysis discretisation of the relevant frequency projections.

Imanol Gago (Severo Ochoa Predoc 2020)

“I received a degree in Mathematics from the University of the Basque Country (UPV/EHU) in 2014. During 2014-2016, I did a master degree in Mathematical Engineering at Complutense University of Madrid (UCM). Currently, in the Heuristic Optimization group of BCAM since November 2019: as research Technician at first and as PhD student since August 2021”.

Project: Applications of Mathematics: Applied Statistics, Energy, Biosciences, Advanced manufacturing, etc.

Abstract: Emergency medical services are one of the pillars of the public health system; their efficient management can improve the prognosis of patients and even save lives. Currently, decision making is largely based on field experience and knowledge. This doctoral thesis project studies the potential of mathematical modeling and optimization as a support tool in the decision-making process. In particular, it will address the problems of location, relocation and sizing of the resource portfolio.

Cristina Galán (Basque Government Health Project)

“I have studied the Mathematics degree in Madrid at Autónoma University. I also studied the master’s degree in Advance Mathematics at Autónoma University of Madrid. Particularly. I get interested in statistic topics as they can be applied in several scientific disciplines and that allows me to still in contact with current problems where maths play an important role”.

Project: New contributions of joint modelling of multivariate longitudinal and time-to-event data for Patient-Reported Outcomes in chronic disease studies

Abstract: The aim of the project is model the PRO data of patients with chronic diseases as a beta-binomial model considering longitudinal data and taking into account censored data. Also, we will consider non-linear covariate effects when considering a mixed model

Ander García (Basque Government Health Project)

“I received a degree in Physics and Master in Quantum Science and Technology at the University of the Basque Country”.

Project: Multiscale Pedestrian-Infection Simulation Framework for the Virtual Prediction of Pathogen Transmission in Urban Scenarios

Abstract: The target of this project is to formulate a novel particle-based simulation framework to model pedestrian dynamics fully coupled with stochastic infection-transmission models and apply it to urban situations characterized by the presence of large groups of people in motion. The long-term goal is to develop a computational tool capable of virtually monitoring different urban scenarios in terms of infection spreading, possibly leading to more effective and event-specific risk-mitigation strategies. In particular, the objective is, on the current route back to normal life, the development of a tool able to simulate the flow of pedestrians in specific urban areas of Bilbao, and to assist in the identification of movement strategies possibly leading to minimization of pathogen transmission in view of new COVID-19 outbreaks and/or for possible future pandemics.

Iker Gardeazabal (Severo Ochoa Predoc 2020)

“I obtained my mathematics degree in the UPV/EHU in 2019. Then I took the master’s degree on advanced mathematics at UCM between September 2019 and September 2020. During my master’s degree most of the subjects I have attended to were of the branch of Analysis. Between April 2021 and July 2021 I did an internship at BCAM directed by Carlos Pérez Moreno”.

Project: Poincaré-Sobolev inequalities and Harmonic Analysis

Abstract: The celebrated Moser iteration method is a powerful and flexible tool to prove the local Hölder regularity of the weak solutions of elliptic PDE due independently, and by different methods, to De Giorgi and Nash. This method has two important key steps. One is the (2, 2) Poincaré inequality and the other is its correspondent Poincaré-Sobolev (2*, 2) inequality.

Jesús González (Severo Ochoa Predoc 2020)

“Energy engineer by the University of Vigo (2015-2019), with a master degree in Industrial Mathematics by the University of Santiago de Compostela (2019-2021). Currently, Phd student at University of Basque Country in collaboration with BCAM. Research interests focused on computacional fluid dynamics, machine learning, HPC, offshore wind engineering, aerodynamics and optimal design”.

Project: Acceleration of offshore CFD simulations through machine learning techniques

Abstract: With the objective of improving the design and the performance of offshore high powered wind turbines, high-fidelity CFD simulations are needed to study the interaction between aerodynamics, hydrodynamics, and mooring dynamics. Unfortunately, they require high computational resources working during long time, which makes them unfeasible in most cases. However, by using innovative machine learning techniques could be possible to decrease their cost but keeping the same precision. Thus, the project will focus on accelerating such simulations through machine learning, to obtain a fast and reliable tool for multi-objective optimisation of offshore wind turbines and their platforms

Nicolás Gorostidi (Severo Ochoa Predoc 2020)

“I graduated in Mechanical Engineering at the University of Oviedo in 2015. I was then awarded a scholarship to pursue postgraduate education at Cranfield University (UK) where I completed an MSc in Computational Fluid Dynamics in 2020. For the past year I have been working at BCAM on the VIVIR Project, sponsored by Iberdrola Foundation, which consisted on implementing a novel deep learning model for failure detection of mooring lines in floating offshore wind turbines”.

Project: Structural Health Monitoring of Floating O shore Wind Turbine Components using Deep Neural Networks

Abstract: Although the installation of floating offshore wind turbines presents numerous technical and environmental benefits, excessive maintenance costs keep these systems economically unfeasible. This project aims at designing an assisting tool for the remote planning of mooring line maintenance operations. A multi-class classification Deep Neural Network (DNN) structure is to be implemented to detect and predict different kinds of mooring line failure modes in real time based only on the displacements of the platform caused by wave, drift and wind forces, potentially saving massive sensorization costs. A comprehensive 6-DOF model is to be solved combining calibrated structural and metoceanic parameters to generate an extensive database, which contains the necessary time and frequency-based features of the system’s response to be fed into the DNN. Synthetic and real turbine data are to be coupled with numerical simulations using OpenFAST.

Mario Martínez (Basque Government Health Project)

“I obtained a Bachelor’s degree in Physics in 2019 at the University of Murcia (UMU) with an exchange year at the University of La Laguna (ULL). Subsequently, I received a Master’s degree in Data Science at the Open University of Catalonia (UOC) in 2021”.  

Project: Machine Learning applied to Health

Abstract: Throughout the PhD program, machine learning techniques will be applied to health data from the Basque Country. The purpose is to provide information of interest to health professionals that can help them in making their decisions. Currently, we are working on a mortality prediction model of COVID-19 in the Basque Country.