Nahuel Costa

Machine Learning & AI researcher

University of Oviedo

Biography

Ph.D. in Computer Science, assistant professor at the University of Oviedo and Machine Learning researcher 🤖. My research primarily focuses on the diagnosis and prognosis of systems with limited data for decision-making. This has allowed me to work on various fields and problems including Atrial Fibrillation, fPET images from patients in coma, Li-ion Batteries, Aircraft Engines, Industrial Fans, and Urban Regulation. I have a special interest in developing models that are transparent, easily interpretable, and accessible to non-experts in ML/AI. As an educator, I aim to convey my enthusiasm to students and provide them with the necessary tools to discover and pursue their own goals.

💬 Feel free to reach out to me if you are interested in my research, looking for colaboration, or just for some interesting discussion.
✉️ You can shoot me a message at costanahuel@uniovi.es or any of my other social networks, I’ll try to respond as soon as I can!

Interests

Prognosis & Health Management
Explainable AI
Generative models (GANs, VAEs, Transformers, Difussion)
Domain adaptation
Conformal prediction
LLMs and RAG

Education

PhD in Artificial Intelligence, 2023
University of Oviedo
MSc in Computer Science, 2020
University of Oviedo
BSc in Computer Science, 2018
University of Oviedo

Research Experience

Assistant Professor

University of Oviedo

Sep 2023 – Present

Computer Science and Artificial Intelligence

Co-Supervisor for a PhD thesis in AI for medical image analysis

Projects:

Automated Moodle quiz creation and optimization using learning analytics, application to learning programming languages

Subjects I teach:

Business Intelligence Notes on text mining
Data Visualization
Algorithmics

Lecturer

University of Oviedo

Feb 2021 – Aug 2023

Computer languages and systems area

Co-Supervisor for four BA thesis (two of them with highest honors)

Subject I taught:

Business Intelligence
Data Visualization
Algorithmics
Operating Systems
Databases
Programming methodology
Introduction to programming

Visiting Researcher

University of Montpellier

May 2023 – Jul 2023 Montpellier

Research and development of Machine Learning diagnostic methods for positron emission tomography (PET) images at the Laboratoire d’informatique, de robotique et de microélectronique de Montpellier (LIRMM).

Visiting Researcher

University of Hawaii at Manoa

May 2022 – Sep 2022 Hawaii

Research and development of Machine Learning tools for the diagnosis and prognosis of lithium-ion batteries at Hawaii Natural Energy Institute (HNEI).

Research technician

University of Oviedo

Oct 2019 – Jan 2021

Development of computational health models for the treatment of rechargeable batteries.

Research intern

University of Oviedo

Oct 2019 – Jan 2021

Analysis of intracardiac electrocardiograms for the prediction of cardiovascular diseases.

Tech stack

python, matlab, R, C, C++, Java

TensorFlow, Keras, PyTorch, HF Transformers

LaTeX

Topics & Projects

Last updated on Apr 20, 2024

RapidAE

A Python library for creation, experimentation, and benchmarking of Autoencoder models

Last updated on Apr 20, 2024

CitiesDatalex (LLMs and RAG)

Software to improve access to legal regulations in the processes of sustainable urban and territorial development

Last updated on Apr 20, 2024

Li-ion batteries

Diagnosis and prognosis of lithium-ion batteries

Last updated on Apr 20, 2024

PHM

Prognosis & Health Management

Last updated on Apr 20, 2024

Cardiovascular Diseases

Diagnosis and treatment of Atrial Fibrillation

Publications

Here you can filter my publications.

Nahuel Costa, David Anseán, Matthieu Dubarry, Luciano Sánchez (2023). ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection. In Journal of Power Sources.

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Luciano Sánchez, Nahuel Costa, Jose Otero, David Anseán, Inés Couso (2023). Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment. In Array.

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Luciano Sánchez, Nahuel Costa, Jose Otero, Inés Couso (2023). Physics-informed learning under epistemic uncertainty with an application to system health modeling. In International Journal of Approximate Reasoning.

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Luciano Sánchez, Nahuel Costa, Inés Couso (2023). Simplified models of remaining useful life based on stochastic orderings. In Reliability Engineering & System Safety.

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Lucas Pérez, Nahuel Costa, Luciano Sánchez (2023). Enhancing Time Series Anomaly Detection Using Discretization and Word Embeddings. In SOCO.

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Matthieu Dubarry, Fahim Yasir, Nahuel Costa, Dax Matthews (2023). Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance.

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Matthieu Dubarry, Nahuel Costa, Dax Matthews (2023). Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics.

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Nahuel Costa, Luciano Sánchez, David Anseán, Matthieu Dubarry (2022). Li-ion battery degradation modes diagnosis via Convolutional Neural Networks. In Journal of Energy Storage.

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Luciano Sánchez, Pablo Luque, Daniel Álvarez-Mántaras, Jose Otero, Nahuel Costa (2022). Weakly Supervised Learning of the Motion Resistance of a Locomotive Powered by Liquefied Natural Gas. In SOCO.

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Luciano Sánchez, Nahuel Costa, David Anseán, Inés Couso (2022). Informed Weak Supervision for Battery Deterioration Level Labeling. In IPMU.

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Nahuel Costa, Luciano Sánchez (2022). Variational encoding approach for interpretable assessment of remaining useful life estimation. In Reliability Engineering & System Safety.

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Nahuel Costa, Luciano Sánchez (2022). RUL-RVE: Interpretable assessment of Remaining Useful Life. In Software Impacts.

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Nahuel Costa, Luciano Sánchez (2021). Remaining useful life estimation using a recurrent variational autoencoder. In HAIS.

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Nahuel Costa, Luciano Sánchez (2021). Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial Fibrillation. In IEEE Access.

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Nahuel Costa, Jesús Fernández, Inés Couso, Luciano Sánchez (2020). Graphical analysis of the progression of atrial arrhythmia using recurrent neural networks. In International Journal of Computational Intelligence Systems.

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