IE-RSME Workshop on Applied Mathematics in Sustainability and Climate Change

Iñigo Urteaga

Ikerbasque Research Fellow, Basque Center for Applied Mathematics

https://www.linkedin.com/in/i%C3%B1igo-urteaga-222b755/

Iñigo is a tenure-track Ikerbasque Research Fellow at the Basque Center for Applied Mathematics (BCAM), specializing in statistical machine learning, computational Bayesian statistics, and sequential decision-making processes. His work focuses on developing algorithms and statistical models for extracting information from data, enhancing computer systems' ability to perform various analytical tasks. Previously, he was an Associate Research Scientist at Columbia University, where he collaborated on machine learning projects for healthcare data. He earned his Ph.D. in Electrical Engineering from Stony Brook University, with a dissertation on Sequential Monte Carlo methods. He holds a degree in telecommunications engineering from the UPV/EHU Faculty of Engineering in Bilbao, Spain. 



Talk title: Probabilistic Machine learning for predictive models in healthcare: a use case on mobile health data

Abstract: Probabilistic machine learning (ML) can enable robust and personalized predictive models in healthcare. In this talk, I will present our research on generative and probabilistic ML to accommodate the idiosyncrasies of mobile health data, such as inconsistent self-tracking adherence. As a case study, I will discuss our statistical modeling approach for a better understanding of the menstrual cycle and its patterns, based on mobile health self-tracked data. I will showcase how probabilistic ML enables disentangling menstruation patterns from self-tracking adherence, to provide accurate, well-calibrated, and informative predictions of interest. 

Raquel Iniesta

Senior Lecturer in Statistical Learning for Precision Medicine, Kings College London, UK

https://www.linkedin.com/in/raqueliniesta/

Raquel is a Senior Lecturer in Statistical Learning for Precision Medicine and leads the Fair Modelling and TDA Lab at the Department of Biostatistics and Health Informatics. With a strong foundation in mathematics, statistics, and machine learning, her research focuses on developing innovative models for precision medicine, particularly in personalizing treatments for depression and hypertension. An experienced lecturer, Raquel teaches both introductory and advanced courses on statistics and machine learning for MSc and PhD students, both in the UK and internationally. Her current work not only involves advancing machine learning models for healthcare but also emphasizes ethical practices in AI, aiming to ensure transparency, fairness, and non-discrimination in medical AI applications. 



Talk title: Why should we care about ethics in medical AI?

 

Abstract: In a society heavily impacted by technology advances, and most particularly by AI emerging on every single sector, we are committed with understanding the current but also the future impacts of using AI for many vital and significant tasks in healthcare, like prescribing a drug or diagnosing a patent with a sever disease. Although AI is still timidly adopted for clinical routine care, it is expected to be gaining grounds quickly. Therefore, this talk is setting up the fundamentals that can assure an AI in medicine that respects the human integrity; that medical doctors will be respected, that patients will be aware on their rights, that developers will be responsible of their tools. On that way we will consider the bigger picture, including the long-term impacts of using AI in healthcare and its possible value to society together with the potential ethical risks for the humanity. We will discuss a framework to society that can contribute to translate ethical principles in human actions intended to preserve a patient centered AI-assisted medical care, that includes an ethical and conscious development of tools, and an ethical integration and deployment of AI systems for healthcare.

Fernando Martín-Sánchez

subdirector gerente del Área de Informática Médica, Estrategia Digital e Innovación

María Téllez-Plaza

National Center for Epidemiology, Instituto de Salud Carlos III

Vincenzo Nava

Senior Researcher at BCAM/Tecnalia

https://www.linkedin.com/in/vincenzonava/

Vincenzo Nava has been a Senior Researcher in Offshore Renewable Energy at TECNALIA, Bilbao, since 2013 and BCAM in the MATHDES group since 2017, working in technical and managerial tasks of R&D European, national and local projects. He obtained his PhD in Ocean Engineering in May 2009 at the "Mediterranea" University of Reggio Calabria, Italy. From 2006 to 2009 he was a fellow visitor at Rice University (Houston, USA). In 2007 he did an internship at BP America, Houston. In 2012 he developed his research activity at the Instituto Superior Técnico de Lisboa, Portugal, Department of Naval Architecture (CENTEC). He is the author and co-author of several articles for international conferences and journals, book chapters and other publications.

Talk title:  Mathematics for the sustainable development of offshore wind renewable energy

Abstract: Offshore wind energy can help the world achieve UN Sustainable Development Goals by providing accessible, clean energy (Goal 7), fostering energy security (Goal 9), lowering CO2 (Goal 13), and conserving ecosystems (Goal 14). Aligned with this, offshore wind energy is essential for meeting the Paris Agreement targets for emission reduction. The Green Deal strategy, adopted by the European Commission, is a clear commitment to achieving these ambitious targets. This strategy has become a roadmap establishing the deployment of 300 GW offshore wind power by 2050. At a national level and on a closer horizon, in 2030, the Roadmap for the Development of Offshore Wind and Sea Energy contemplates targets for installing floating wind power of up to 3 GW. The development of innovations in offshore wind energy is geared towards the reliable, efficient, economical and safe design and operation of wind turbines. In this sense, the progressive increase in the power of the machines implies challenges in the structural elements that will be subject to greater loads. In addition, these wind farms are installed at greater distances from the coast and greater depths, which makes it necessary to develop floating solutions. Moreover, the marine environment introduces additional challenges due to the high costs of installing and accessing the assets and the enormous cost of stopping energy production due to failure.

This talk will discuss how progress in the renewable energy sector, particularly in deploying offshore wind solutions, can benefit from applied research in numerical and computational mathematics and artificial intelligence. We show examples of the current research lines we are working on to improve the viability of offshore wind solutions during the project lifecycle. During the design stage, we aim to optimise the designs and reduce the time-to-market by speeding up CFD simulations via AI techniques. We also show advances in developing autoencoders to detect damaged components early during the operation phase to plan maintenance actions, reduce the share in Operation and Maintenance costs, and keep the energy production at the highest level possible.

María Erquiaga Mendoza

Senior Manager at BBVA

https://www.linkedin.com/in/maria-erquiaga-mendoza-8248961/

María graduated in law with a specialization in economics at Deusto University and in an MBA at IE Business School. She has17 years of experience in the ESG field,  an acronym that stands for environmental, social and governance. For the last 2 years and currently, she has been developing sustainable solutions to fight against climate change and to promote inclusive growth at BBVA, a global financial institution in which sustainability is at its core strategy. Always on global roles, with reach to local banks, corporate areas and foundations.

Talk title: Sustainability at BBVA

Abstract: BBVA considers sustainability to be an important part of its overall strategy. Their approach to sustainability focuses on two main areas: climate action and inclusive growth. To achieve this, BBVA works towards supporting business growth while also managing their own environmental impact. BBVA has a clear plan with three main goals: promoting new businesses, achieving net zero carbon emissions, and making a positive impact on society. Within this framework, climate action is considered to be a key aspect. This involves using sustainable finance as a business opportunity, aligning their investment portfolio with targets for sectors that have high carbon emissions, and helping clients with their own decarbonisation strategies. BBVA also engages with the community and stakeholders to combat climate change.

David Gómez-Ullate Oteiza

Professor of Applied Mathematics at IE University

https://www.linkedin.com/in/david-g%C3%B3mez-ullate-oteiza-87a820b/

David Gómez-Ullate is Professor of Applied Mathematics and Department Head at IE University. He is also President of the Knowledge Transfer Commission of the Royal Spanish Mathematical Society. For the past 10 years he has specialized in knowledge transfer of mathematics to industry, where he has coordinated projects in the financial, fisheries, biomedical, legaltech and aeronautical sectors. He is currently leading a project on optimization of shipping routes that aims to have a considerable impact on decarbonization of the maritime sector.



Talk title: Mathematical optimization of maritime shipping routes



Abstract: Despite being the most efficient way to transport goods across the globe, maritime transport is still responsible for 3% of the global CO2 emissions. The latest regulation from the International Maritime Organization imposes severe restrictions on the efficiency and carbon emissions of vessels in order to decarbonize the maritime industry. While alternative fuels and better ship design require strong investments and research, there is a simple way of attaining a 3-5% reduction in fuel and emissions: optimizing shipping routes. Indeed, shortest distance (orthodromic) paths which most commercial routes follow are not optimal, in much the same way as the shortest path in road transport is not necessarily the best one. We will provide a short introduction to the mathematics involved in tackling this challenging issue. This project is funded by the BBVA Foundation and the Agencia Estatal de Investigación, and it includes naval engineers, meteorologists, data scientists, mathematicians and software engineers.