Add overview sections to project pages and enhance project snapshot display

Author: Daniel Precioso, PhD

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

@@ -1,4 +1,4 @@
----
+---
 title: ATENEA for Aerospace Manufacturing
 start_date: '2019-04-01'
 end_date: '2019-10-31'
@@ -7,6 +7,7 @@ category: manufacturing
 customer: "Airbus D&S"
 ---
 
+## Overview
 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.

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

@@ -7,6 +7,7 @@ category: insurance
 customer: "Vienna Insurance Group"
 ---
 
+## Overview
 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.
 
 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.

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

@@ -7,6 +7,7 @@ category: health
 customer: "Instituto de Salud Carlos III"
 ---
 
+## Overview
 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.
@@ -20,11 +21,9 @@ Our study analyzed the effectiveness of non-pharmaceutical interventions (NPIs)
 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: 
+### Media Coverage
 
 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]
@@ -36,6 +35,6 @@ M. Salomone “Spanish mathematicians look for a model to predict how the pandem
 “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
 
-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
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+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

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

@@ -7,6 +7,7 @@ category: fintech
 customer: "Evendor / Fundación BBVA"
 ---
 
+## Overview
 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. 
 
 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.
@@ -16,17 +17,17 @@ The study tackled significant challenges typical of fraud detection—such as ex
 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.
+### Media Coverage
 
-In the Media:  “Matemáticas antirrobo y otras cuatro ideas para mejorar el mundo”, Diario EL País (30/07/2015)
-
+“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]
+### Projects and Contracts
 
-Project: Artificial Intelligence and Data Science: Applications in Payment Fraud Detection, Leonardo Scholarship, Fundación BBVA. [link Red Leonardo]
-
+Artificial Intelligence and Data Science: Applications in Payment Fraud Detection, Leonardo Scholarship, Fundación BBVA. [link Red Leonardo]
 Sum: 40.000 EUR (2015-2016)
+### Contract and Funding
 
-Contract: Learning for fraud detection in electronic payments 
-
+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.

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

@@ -7,6 +7,7 @@ category: legaltech
 customer: "Foqum Analytics / Lefebvre"
 ---
 
+## Overview
 Natural Language Processing with Deep Learning for retrieval of legal documents
 
 This project was developed in the early days of Deep Learning NLP, before the transformer architecture was built into commercial products.

content/industry/marketing/index.md

@@ -7,6 +7,7 @@ category: marketing
 customer: "Omnicom Media Group"
 ---
 
+## Overview
 Machine learning for precision marketing 
 
 Contract Art. 83 between Omnicom Media Group S.A. and Univ. Complutense de Madrid 

content/industry/neocam/index.md

@@ -7,6 +7,7 @@ category: health
 customer: "Open CV / Intel / Luxonis"
 ---
 
+## Overview
 Neocam was a beautiful project done in collaboration between many different members from UCA Datalab. It was originally motivated by the first OpenCV AI Competition in 2021, which asked for projects based on the new edge computing camera Oak-D from Luxonis, which has 3 objectives (depth field) and a built-in chip that runs CV models on the device itself.
 
 Our project involved developing a complete edge-cloud platform for real-time monitoring of newborns in neonatal intensive care units. Premature babies require continuous observation, yet much clinically relevant information — such as motor activity, stress indicators, environmental conditions, and subtle behavioral cues — is either assessed subjectively or not captured systematically. By embedding intelligent devices directly at the incubator level, the project enables continuous collection and analysis of multimodal data (video, audio, vital signs, and environmental signals) while preserving privacy and minimizing intrusiveness. The goal is to transform raw, heterogeneous sensor streams into structured clinical information that supports more informed and timely medical decisions.
@@ -16,20 +17,16 @@ Technically, the system combines optimized deep learning models deployed on edge
 The project won the first regional prize (Europe) in the 2021 OpenCV AI Competition and the second global prize, out of more than 1400 submitted projects. It also led to a publication in the high reputation journal IEEE Journal of Biomedical and Health Informatics.
 
 The PI of this project was my colleague Lionel Cervera. My role was to be responsible for the development of the three  AI algorithms that were incorporated into the NeoCam.
-
-In the Media:
+### Media Coverage
 
 OpenCV Blog Episode 32 (04/11/21) Real-time tele-monitoring of preterm neonates with NeoCam - Interview with Satya Mallick, CEO of OpenCV. [link]
-
 A UCA project wins first place in an international Artificial Intelligence competition, Diario de Cádiz (8/09/21) [link]
-
-Videos (insert in text?):
+### Videos
 
 Spot: https://www.youtube.com/watch?v=58KHGucW0dQ
-
 Motion detector: https://www.youtube.com/watch?v=CGLl9O9GtEg
 
 Breath rate detector: https://www.youtube.com/watch?v=ZsHf2NaaHW8
+### Publications
 
-Publications: A. Ruiz-Zafra, D. Precioso, B. Salvador, J. Jiménez, I. Benavente, D. Gómez-Ullate, and Lionel C. Gontard. NeoCam: An edge-cloud platform for non-invasive real-time monitoring in neonatal intensive care units,  IEEE Journal of Biomedical and Health Informatics 27 (2023) 2614-2624.
-
+A. Ruiz-Zafra, D. Precioso, B. Salvador, J. Jiménez, I. Benavente, D. Gómez-Ullate, and Lionel C. Gontard. NeoCam: An edge-cloud platform for non-invasive real-time monitoring in neonatal intensive care units,  IEEE Journal of Biomedical and Health Informatics 27 (2023) 2614-2624.

content/industry/nilm/index.md

@@ -6,6 +6,10 @@ description: Data-driven methods to infer appliance-level electricity use from s
 category: energy
 ---
 
+## Overview
+
+Understanding how electricity is used inside homes is essential for improving energy efficiency, reducing emissions, and designing smarter power systems. Non-Intrusive Load Monitoring (NILM) aims to identify the activity of individual appliances, such as fridges, washing machines, or dishwashers, using only the total electricity consumption measured by a smart meter. By avoiding the need for dedicated sensors on each device, NILM enables scalable and privacy-preserving energy analytics. Reliable appliance-level information can support demand response, personalized energy feedback, fault detection, and more effective integration of renewable energy into the grid.
+
 This work addresses a fundamental but often overlooked modeling choice in NILM: how to define when an appliance is considered ON or OFF. Since real datasets usually provide power consumption but not appliance states, this requires introducing thresholding rules that transform a regression problem into a classification task. We show that different thresholding methods lead to substantially different learning problems and performance outcomes, even when using the same deep learning architectures. By systematically comparing thresholding strategies and proposing objective criteria based on signal reconstruction error, the paper highlights the importance of problem formulation in NILM. In addition, a multi-task learning approach is explored, showing that jointly learning appliance status and power consumption can improve performance for certain types of devices through transfer learning between regression and classification tasks. 
 
 This was the first paper we wrote with Daniel Precioso when he joined UCA Datalab at University of Cádiz to start his industrial PhD. He was already interested in this topic because he had done an internship at Foqum analytics. We extended his work and addressed a relevant conceptual problem in how NILM problems are usually framed. The work was presented in a few conferences , among which:

content/industry/predicting-drifting-buoys/index.md

@@ -7,6 +7,7 @@ category: maritime
 customer: "Satlink"
 ---
 
+## Overview
 Satlink is one of the largest world manufacturers of smart buoys for the tuna fishing industry.  They approached me while I was working at University of Cádiz with an initial project that involved predicting the drifting trajectory of a FAD (Fish Aggregating Device) to which their buoys are attached. Besides other sensors, these smart buoys contain an echosounder that measures biomass and detect tuna presence, and they send this information every hour to a satellite. The buoys are deployed by ships that drop them in the ocean, and once released they drift with the ocean currents. The company found that, despite having an operation life of 6-12 months,  many buoys were lost within 2-3 weeks by collision with the coast. They wanted to identify the best spots to drop the buoys so that the drift would keep them circulating in the ocean.
 
 Satlink provided us with a very valuable dataset: the daily positions of more than 40.000 drifting buoys in the Indian Ocean.
@@ -16,8 +17,9 @@ We developed a Lagrangian numerical integration scheme which used the ocean curr
 As a side project, we used their data to validate predictions from these models, comparing the true trajectories of the buoys with those predicted by the Lagrangian model.  This is an example of a synergistic and positive by-product between industry and research. The largest publicly funded program to gather data from drifting buoys is the NOAA Global Drifter Program, which contains around 1000 buoys from a collaboration between 19 countries. By way of contrast, we had access to a proprietary dataset of more than 20 years of operation for ca. 40.000 buoys in the three major oceans, providing daily (sometimes hourly) positions. Of course these buoys were deployed for fishing purposes, but the data they gathered can be used for many other scientific purposes, in this case, validation of Ocean General Circulation Models.
 
 My role in this project was Principal Investigator and responsible for the contract. It was a good opportunity to work with Karan Bedi, a visiting MSc student from IIT Roorkee (India), who visited UCA Datalab in Cádiz during that period.
+### Projects and Contracts
 
-Project: Prediction of drifting objects in the ocean Contract Art. 83 between Satlink SL and Universidad de Cádiz PI: David Gómez-Ullate (UCA), 01/04/2019 – 01/07/2019, Sum: 70.000 EUR.
-
-Publication: K. Bedi, D. Gómez-Ullate, A. Izquierdo,T.F. Montblanc, (2019). Validating Ocean General Circulation Models via Lagrangian Particle Simulation and Data from Drifting Buoys. ICCS 2019. Lecture Notes in Computer Science 11539. Springer, Cham. DOI
+Prediction of drifting objects in the ocean Contract Art. 83 between Satlink SL and Universidad de Cádiz PI: David Gómez-Ullate (UCA), 01/04/2019 – 01/07/2019, Sum: 70.000 EUR.
+### Publications
 
+K. Bedi, D. Gómez-Ullate, A. Izquierdo,T.F. Montblanc, (2019). Validating Ocean General Circulation Models via Lagrangian Particle Simulation and Data from Drifting Buoys. ICCS 2019. Lecture Notes in Computer Science 11539. Springer, Cham. DOI

content/industry/snomed-ct-coding/index.md

@@ -7,3 +7,4 @@ category: health
 customer: "Hospital Puerta del Mar Cádiz"
 ---
 
+## Overview