Enhance project pages with customer details and related publications

- Added customer parameter to various project markdown files for better context.
- Updated single.html layout to display project tags and customer information on project pages.
- Implemented functionality to list related publications based on project references.

Author: Daniel Precioso, PhD

content/industry/atenea-aerospace-manufacturing/index.md

@@ -1,15 +1,21 @@
----
+---
 title: ATENEA for Aerospace Manufacturing
 start_date: '2019-04-01'
 end_date: '2019-10-31'
 description: AI-supported quality inspection and process monitoring for Airbus composite manufacturing.
 category: manufacturing
+customer: "Airbus D&S"
 ---
 
-ATENEA was an Industry 4.0 collaboration with Airbus D&S focused on quality control for composite fan-cowl manufacturing.
+ATENEA: systems based in artificial intelligence to support manufacturing engineering Contract Art. 83 between AIRBUS D&S and Universidad de Cádiz (CDTI Interconnecta) PI: David Gómez-Ullate (UCA), 01/04/2019 – 31/10/2019, Sum: 90.000 EUR.
+
+Modern aerospace manufacturing demands extremely high quality standards, especially in composite components where defects can be costly and difficult to detect. Within the context of Industry 4.0, ATENEA was a research and innovation project funded by CDTI and developed in collaboration with Airbus, with the goal of bringing data science and artificial intelligence directly into the production and inspection of fan cowls for the Airbus A320/A330 Neo.
+
+The project focused on transforming large volumes of heterogeneous industrial data—machine logs, sensor measurements, manufacturing records, and inspection images—into actionable insights. By integrating predictive analytics, computer vision, and real-time monitoring, ATENEA aimed to improve quality inspection, anticipate manufacturing deficiencies, and reduce non-conformities before they propagated through the production line.
+
+A key outcome of the project was the development of intelligent tools to support both process monitoring and quality assurance, enabling earlier detection of structural defects, better traceability, and more consistent inspection criteria. ATENEA demonstrates how advanced analytics can be embedded into real production environments to enhance reliability, efficiency, and decision-making in aerospace manufacturing. 
 
-The project integrated machine logs, sensor data, manufacturing records, and ultrasound inspection images to detect non-conformities earlier and improve traceability.
+From a methodological perspective, the project combined predictive modeling and computer vision with industrial data pipelines. Machine-learning models were developed to estimate the probability of non-conformities related to delamination and porosity using data from composite layup machines, environmental sensors, tooling information, and SAP production records. In parallel, image-processing algorithms based on ultrasound inspection data were designed to automatically detect and segment potentially defective regions, producing an objective quality score for inspection reliability. These results were integrated into a real-time dashboard architecture, allowing continuous monitoring of production variables and inspection performance across the manufacturing process. 
 
-Methodologically, it combined predictive modeling and computer vision, then exposed results through real-time monitoring dashboards for manufacturing and inspection teams.
+This was a challenging Industry 4.0 collaboration between Airbus D&S and UCA Datalab at University of Cádiz. I led a team of 4 data scientists and software developers to complete our work package. We made frequent visits to the Airbus production plant at CBC - Puerto de Santa María to work in situ with Airbus personnel, deliver formation courses, become familiar with the whole Fan Cowl production process, etc. Unfortunately, confidentiality requirements due to the sensitive nature of the data and production process did not allow the publication of the results in scientific journals.
 
-Due to confidentiality constraints, results were not published in scientific journals.

content/industry/climate-risk-insurance/index.md

@@ -4,10 +4,16 @@ start_date: '2025-01-01'
 end_date: ''
 description: Climate-adjusted life tables and actuarial risk projections for insurance portfolios.
 category: insurance
+customer: "Vienna Insurance Group"
 ---
 
-This collaboration with Vienna Insurance Group translates climate and health research into actuarial tools for life and health insurance.
+Climate change is no longer a distant concern for insurers: it is already reshaping mortality patterns, life expectancy, and long-term risk. Rising temperatures, more frequent extremes, and uneven adaptation across populations pose fundamental challenges to how life and health insurance products are priced, managed, and regulated.
 
-The project studies how temperature and related climate factors affect mortality by age, sex, and region, then propagates these effects into death probabilities, life expectancy, and life tables under scenario-based climate pathways.
+This project, developed in collaboration between IE University and Vienna Insurance Group (VIG), translates cutting-edge climate and health research into tools that insurers can actually use. We study how temperature and other climate-related risk factors affect mortality across age groups, regions, and future climate scenarios, and we embed these effects directly into actuarial quantities such as death probabilities, life expectancy, and life tables.
+
+The outcome is a new generation of climate-adjusted life tables that allow insurers to explore how different climate pathways may impact portfolios over time. By combining public data, scientific projections, and proprietary information, the project supports more informed decisions around pricing, capital requirements, and long-term risk management in a changing climate.
+
+Methodologically, the project builds on exposure–response functions linking temperature to mortality, combined with high-resolution climate data and future climate scenarios (Shared Socioeconomic Pathways, SSPs). These relationships are propagated through an actuarial pipeline to produce climate-adjusted mortality rates and life tables by age, sex, and region. The framework integrates results from state-of-the-art epidemiological studies with demographic and actuarial modeling, enabling scenario-based projections of longevity and climate risk relevant for insurance portfolios.
+
+To learn more about the project, visit the project website. We are organizing the 3rd edition of the Climate Change and Insurance conference series (CCI 26) at IE University’s campus in Segovia, from 2-4 September 2026.
 
-The result is a climate-adjusted actuarial framework that supports pricing, capital planning, and long-term risk management in changing environmental conditions.

content/industry/covid-19-impact-of-npis/index.md

@@ -4,8 +4,38 @@ start_date: '2020-09-01'
 end_date: '2021-05-31'
 description: Modeling the effect of non-pharmaceutical interventions on SARS-CoV-2 transmission.
 category: health
+customer: "Instituto de Salud Carlos III"
 ---
 
-This work analyzed non-pharmaceutical interventions (NPIs) in Spain during the second COVID-19 wave. A daily restriction-intensity index was built from provincial and municipal policy data and linked to transmission metrics.
+The COVID-19 pandemics was a singular event where scientific activity proved to be instrumental in fighting against the disease and better decision making. Scientists worked round the clock from their homes during lockdown to establish networks, gather and process data, elaborate models and draft reports to help decision makers.
+
+In this context, the main mathematical spanish society CEMAT (Comité Español de Matemáticas) established a Committee of experts called “Acción Matemática contra el Coronavirus” from the 4 main societies (SEMA, RSME, SCM and SEIO) whose role was to elaborate a mathematical response to the challenges posed by the pandemics.
+
+The Committee elaborated a meta-prediction model where many modeling groups participated to predict short term prevalence and spread of the disease.
+
+The Committee started working with technical experts from the Ministry of Health and the Ministry of Economy, and it was tasked with modeling the effect of Non-Pharmaceutical Interventions (NPIs) both from a public health and an economic point of view. This was quite important since decision makers were often faced with the choice of adopting more restrictive measures with a considerable economic impact, whose effectiveness had to be predicted.
+
+Our study analyzed the effectiveness of non-pharmaceutical interventions (NPIs) implemented in Spain during the second wave of COVID-19 (September 2020 to May 2021). Researchers compiled detailed provincial and municipal data on restrictions across nine areas of activity and constructed a daily “restriction intensity index” ranging from 0 to 1 to quantify the strength of measures over time. Using statistical modeling under the framework of the Spanish Committee of Mathematics’ “Mathematical Action against Coronavirus” initiative, the team evaluated how changes in restriction intensity affected virus transmission.
+
+The results showed that increasing the overall intensity of measures by 34% was associated with a 22% reduction in transmission within one week. Interventions related to social distancing and indoor hospitality were found to be particularly effective, while measures affecting leisure, cultural activities, places of worship, religious celebrations, and indoor sports showed less clear effects—though these differences should be interpreted cautiously, as many measures were implemented simultaneously. The project also made all collected data publicly available to support transparency and future research, highlighting the critical role of mathematical modeling and data analysis in managing public health crises.
+
+My role in this project was mainly involved in coding and processing the NPIs into useful variables for the statistical model that matched the NPI intensity time series to incidence metrics. On a separate project, we made a predictive tool to assist hospitals in planning for extra beds in ICUs, leveraging what was known on disease dynamics and observed infected individuals.
+
+In the media: 
+
+Interview in eldiario.es “To fight the pandemic, we need transparency and access to good data.” (17/04/20) [link]
+
+Interview for Real Sociedad Matemática Española (09/04/21) [link]
+
+Las matemáticas frente a la Covid-19, Fundación Ramón Areces en colaboración con Real Sociedad Matemática Española  [video]
+
+M. Salomone “Spanish mathematicians look for a model to predict how the pandemic will evolve”, Fundación BBVA (07/04/20) [link]
+
+“Big Data contra el coronavirus y ¿nuestra privacidad?” , Fallo de Sistema, Radio 3 (19/04/20) [link]
+
+“Desarrollan un modelo predictivo de ocupación de camas en las UCI de los hospitales andaluces” Fundación Descubre, Junta de Andalucía [link]]
+
+Acción Matemática contra la COVID confirma que el incremento de las restricciones redujo la transmisión del virus en un 22% a la semana. CITIC-UDC (17/04/23)  [link]
+
+Publications: Effectiveness of non-pharmaceutical interventions in nine fields of activity to decrease SARS-CoV-2 transmission (Spain, September 2020–May 2021). Front. Public Health 11 1061331. doi: 10.3389/fpubh.2023.1061331
 
-Results showed that a 34% increase in overall restriction intensity was associated with a 22% reduction in transmission after one week. The study was published in Frontiers in Public Health (2023).

content/industry/fraud-detection-payments/index.md

@@ -4,12 +4,30 @@ start_date: '2015-06-01'
 end_date: '2016-12-31'
 description: AI and statistical learning methods for real-time payment fraud detection.
 category: fintech
+customer: "Evendor / Fundación BBVA"
 ---
 
-This project applied machine learning and data science to credit-card fraud detection using more than 150 million real transactions.
+This research project applied advanced techniques from artificial intelligence (AI) and data science to the problem of detecting fraud in electronic payment systems, with a particular focus on credit card transactions. 
 
-The modeling strategy addressed severe class imbalance, changing fraud behavior over time, and real-time decision constraints for financial institutions.
+In those days, commercial AI-based tools were still in their infancy, and many anti-fraud systems were still a combination of rule based and very basic statistical filters. Our work involved analyzing over 150 million real transactions collected over one year by a first tier bank, to identify statistical traces and behavioural patterns associated with fraudulent activity. By leveraging AI-driven models, the research aimed to improve the ability of financial institutions to decide in real time whether a transaction should be blocked.
 
-The project was supported by a Leonardo Scholarship from Fundacion BBVA and an Art. 83 collaboration with Evendor Engineering and Universidad Complutense.
+The study tackled significant challenges typical of fraud detection—such as extremely imbalanced data (with approximately one fraudulent transaction per 6 000 legitimate ones), evolving fraud strategies that change over time, and the need to model the decision-making utility for both the bank and the fraudster. The project developed new algorithms that substantially improved model efficiency compared to existing approaches.
+
+In addition to its technical contributions, the project emphasised training early-stage researchers in statistical learning and data science, responding to strong market demand for these skills and the lack of formal academic programmes in this area at the time. 
+
+Due to confidentiality clauses, we were unable to publish publicly available results on this research.
+
+In the Media:  “Matemáticas antirrobo y otras cuatro ideas para mejorar el mundo”, Diario EL País (30/07/2015)
+
+“La Fundación BBVA financia un proyecto basado en matemáticas que permitirá adelantarse al fraude bancario” ICMAT (29/07/2015) [link]
+
+Project: Artificial Intelligence and Data Science: Applications in Payment Fraud Detection, Leonardo Scholarship, Fundación BBVA. [link Red Leonardo]
+
+Sum: 40.000 EUR (2015-2016)
+
+Contract: Learning for fraud detection in electronic payments 
+
+Contract Art. 83 between Evendor Engineering SL and Univ. Complutense de Madrid 
+
+PI: David Gómez-Ullate (UCM-ICMAT), 01/06/2015 - 31/12/2015, Sum: 20.000 EUR.
 
-Due to confidentiality constraints, technical results were not fully published.

content/industry/legal-document-retrieval/index.md

@@ -4,12 +4,28 @@ start_date: '2018-07-02'
 end_date: '2019-07-01'
 description: Deep-learning NLP for legal document classification, labeling, and semantic retrieval.
 category: legaltech
+customer: "Foqum Analytics / Lefebvre"
 ---
 
-This legaltech project modernized judicial-document workflows for a corpus of more than one million legal rulings and documents.
+Natural Language Processing with Deep Learning for retrieval of legal documents
 
-The work started from unlabeled data and required a custom annotation pipeline, domain-specific labeling strategy, and active-learning loop adapted to Spanish legal language.
+This project was developed in the early days of Deep Learning NLP, before the transformer architecture was built into commercial products.
 
-The resulting hierarchical classifier reached accuracy above 90% and improved retrieval efficiency by a reported factor of 22 compared with the previous workflow.
+In 2018, Lefebvre (Spain’s leading provider of legal information) sought to modernize the way it managed judicial content. The goal was to automatically classify, label, and extract relevant information from a corpus of over one million court rulings and legal documents — a repository that continues to grow daily.
+
+Beyond structuring the data, the project aimed to enable advanced search capabilities that would support faster and more informed legal decision-making.
+
+The project began with entirely unlabeled data. Due to the highly specialized nature of legal language — particularly within the Spanish legal system — off-the-shelf pre-trained NLP models were not suitable.
+
+Addressing this required the development of a dedicated annotation pipeline, the design of domain-specific labeling strategies, and the implementation of an active learning framework to efficiently guide expert annotation. At the time (2018), this meant deploying state-of-the-art NLP methodologies adapted specifically to the legal domain.
+
+The project delivered a hierarchical classification system capable of organizing judgments and legal documents across multiple levels of legal categories, achieving accuracy rates above 90%. In addition, the implementation of semantic search capabilities improved information retrieval performance. Compared to the previous system, the new solution was 22 times more efficient, significantly reducing operational workload and increasing productivity and service responsiveness.
+
+Due to confidentiality agreements, the team could not write a publication in this project, but the results were explained in the specialized conference JURIX 2019 - IberLegal with a talk in the industry session.
+
+I was Principal Investigator of the project and responsible for delivery of results under the contract between UCA Datalab and Quantum Analytics.
+
+Contract Art. 83 between Quantum Analytics and Universidad de Cádiz
+
+PI: David Gómez-Ullate (UCA), 02/07/2018 – 01/07/2019, Sum: 72.600 EUR.
 
-The project was developed under an Art. 83 contract with Universidad de Cadiz and industry partners.