Welcome to my personal website 👋
ML Researcher and Professor
I explore how machine learning can help people make
better decisions in healthcare and industry
Medical: AI for EHRs, ECGs, and imaging (X-rays & PET scans)
Industrial: Data-driven insights for batteries, aircraft engines & fans
Teaching: Guiding students to define and pursue their own goals
Talks & Courses: Bringing AI to academia, industry & the public
Trusted by
Companies and organizations that I have work or collaborated with.
About Me – Nahuel Costa
Hello, my name is Nahuel (it means jaguar in Mapuche language), I'm an ML researcher and professor at the University of Oviedo.
My research focuses on maximizing the potential of Machine Learning to anticipate potential outcomes and support decision-making in monitoring systems, particularly in scenarios with limited data. I work both on the biomedical field, as well as in the industrial domain. I am particularly interested in developing models that are robust and effective, but also interpretable and easily accessible to non-ML/AI experts.
💬 I love sharing my work and insights on AI with a wider audience. If you’re interested in workshops, talks, or tailored training for your company or organization, feel free to reach out. You can explore the Services section for an overview of what I offer or contact me directly to discuss how we can create something that fits your needs.
Assistant Professor
University of Oviedo
- Generative models
- Business Intelligence
- Data Visualization
- Algorithmics
Visiting Professor
University of Alberta
Visiting Professor
CEU San Pablo University
Lecturer
University of Oviedo
-Business Intelligence
-Data Visualization
-Algorithmics
-Operating Systems
-Databases
-Programming methodology
-Introduction to programming
Visiting Researcher
Université de Montpellier
Visiting Researcher
University of Hawaii at Manoa
Research technician
University of Oviedo
Research intern
University of Oviedo
Research
Main topics and fields I work with
Industrial
- •Battery health monitoring and prognostics
- •Aircraft engine condition monitoring
- •Jet fan predictive maintenance
- •Physical systems modeling with digital twins
Medical
- •ECG analysis and cardiovascular disease prediction
- •Medical image analysis (X-ray, PET scans)
- •Electronic Health Records (EHR) analysis
- •Multimodal and Longitudinal patient data modeling
Others
- •Machine learning with limited data
- •Explainable AI methods
- •Chatbots and Retrieval-Augmented Generation (RAG)
- •Agent-based AI systems
- •Educational AI applications
Research Projects
Past and current research projects
GEOAI-Based Augmentation of Multi-source Urban GIS
NAC-ES-PUB-ASV-2025 PCI2025-163245
Still work to be done!
Team member
Intelligent Computing For Disruptive Data
SEK-25-GRU-GIC-24-055
The use of incomplete, inaccurate or partial data in machine learning can lead to biased or inefficient decisions. Commonly, large data sets are used, selecting informative elements and discarding those that do not meet a minimum quality level. Our research group proposes an alternative approach for scenarios where data scarcity requires maximising every piece of available information, a frequent challenge in areas such as industry, finance, economics, and medicine.
Team member
Sustainable Computing in Limited Information Scenarios
MCINN-24-PID2023-146257OB-I00
This project is dedicated to developing new methods and applications to address ML challenges in environments characterized by sparse information. It gives priority to the use of low quality data, which is often overlooked. In alignment with Green AI principles, our approach includes designing methodologies that enhance computational efficiency and, as a result, reduce energy consumption. Furthermore, we are adapting these methodologies to address specific industrial challenges, focusing on sustainable practices such as extending the lifespan of equipment and efficiently managing resources like water and energy.
Team member
Observatory for the Implementation of the 2030 Agenda in the Spanish University System
MDSC-24-2024D179-Agenda2030
Working on CITIES DATALEX®, a software whose purpose is to improve access to legal regulations resulting from the application in actions in the urban environment and, in general, in the processes of sustainable urban and territorial development. Also, supervising the IT team in helping gather and process data for the main purpose of the project.
Team member
Accurate estimation of health status and remaining life in advanced lithium-ion technologies
MCI-23-PID2022-141792OB-I00
Project led by my colleague and friend David Anseán. We are working on training machine learning models with simulated data from battery digital twins such as 'Alawa, and adapt them to real data to estimate the health status and remaining life of lithium-ion batteries.
Collaborating researcher
Artificial Intelligence applied to Data Analysis and Process Modeling
FUO-354-23
Project framed within the Total Energies Chair of Data Analytics and Artificial Intelligence. Projects developed with student participation were addressed, including: RAG with chatbots for internal company documentation, rainfall and river flow modelling for reservoir management, predictive models for demand forecasting, survival models for customer churning and retention...
Team member
Teaching
Study Materials and Projects
Generative Networks
View Materials →
Business Intelligence
View Materials →
Algorithmics
View Materials →
Innovation Projects
View Project ★
Mentored Students
Students I've guided in their research and academic projects
Internships
Supervision of internships in companies
Juan Fernández Martínez (Dupont), Marta Pastor Arranz (ArcelorMittal), Pedro Vallina Insua (Merkle), Iratxe García García (Total Energies), Marina Dáder Suárez (Total Energies), Antonio Gómez-Carrera Núñez(Total Energies), Álvaro Alcalde Rodríguez (Total Energies), Rubén Martínez Ginzo (NEO Ingeniería), Razmik Chakhoyan Grigoryan (Accenture), Gabriel Puja Lojo (Mecalux), David González Fernández (ArcelorMittal)
Samuel Camba Fernández
Prognosis of Degenerative Diseases Using Unsupervised and Partially Supervised Learning Techniques
PhD research focused on developing novel unsupervised and semi-supervised learning techniques for early prediction and prognosis of degenerative diseases.
Jorge Valdenebro Álvarez
Study and Application of Domain Adaptation Techniques in Deep Learning Models
Analysis of various domain adaptation techniques for the detection of cardiovascular risk, where differences in patient characteristics and capture equipment can influence the accuracy of trained models. Evaluation of the effectiveness of the selected techniques to improve generalization between different electrocardiographic datasets.
Mario Rabanal Pérez del Río
Intelligent System for Access and Consultation of Legislative Documents
Web tool capable of providing clear, secure and relevant legislative information, assisted by AI. The tool includes the ability to process and understand both structured (sections, tables, regulations) and unstructured (free text, figures, reports) information, ensuring reliability and avoiding typical errors of interpretation or "alucinations" of current language models
Marina Dáder Suárez
Adaptability and Generalization of Deep Learning Models in Electrocardiographic Analysis
Study of the generalization of Deep Learning models applied to electrocardiographic data. Exploration of strategies like fine-tuning and ICL to transfer knowledge between different datasets without requiring large training data volumes.
Iratxe García García
Intelligent Optimization of Predictive Models for Retention and Churn
Developed predictive machine learning models for retention and churn integration into the pipeline of the company where she did her internship.
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Consulting, Talks & Workshops
Current Courses
Discover the latest educational offerings designed to advance your skills in AI and technology.
Generative Artificial Intelligence
A course designed for anyone who wants to learn about how to use the current generative AI tools and how to identify use cases from everyday life to facilitate their work.
Image Generation with Artificial Intelligence
A course designed for anyone who wants to learn about how to use the current image generation and editing tools with AI.
Client Reviews
What clients say about working with me
"Thanks to the AI workshop I discovered new functionalities of AI that I didn't know about, as well as other types of AI. Very enriching."
Pablo Montes
ACG Ingeniería S.A.
"Thank you for the course. It has been a pleasure and I left wanting more. Good luck with your research. Thanks."
Álvaro González Marín
Consejería de Derechos Sociales y Bienestar, Principado de Asturias
"Nahuel, the course was great; it flew by and I would have loved it to last three times longer. Not everyone knows how to transmit their knowledge like you do, and with that level of professionalism and commitment."
Raquel García García
Unidad Especializada de Tabaquismo del Área IV de Oviedo
Past Experiences
Here you can find some of the past courses, workshops and talks I have given.
Introductory course on generative Artificial Intelligence
Database audit for a future public health information system
A training activity, organized by the Servicio de Salud Poblacional, aimed at professionals assigned to the Health Advisory Service whose work directly affects any aspect of Public Health. It is also extended to personnel belonging to the Health Service of the Principality of Asturias with an interest in this field, both as a complementary means of training and as a meeting point between both groups.
TotalEnergies Chair | AI: Present, future and applications to society
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Blog
Personal projects and lessons learned from past experiences.
How Mario convinced me to start a podcast: The Training Loop Podcast
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Cities DataLex 2024: A tool to improve access to legal regulations in sustainable urban development and territorial processes
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RapidAE: a library to deal with Autoencoders
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Showing 1-3 of 3 posts