Nahuel Costa

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

 
 
 
 
 
University of Oviedo
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:

 
 
 
 
 
University of Oviedo
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
 
 
 
 
 
University of Montpellier
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).
 
 
 
 
 
University of Hawaii at Manoa
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).
 
 
 
 
 
University of Oviedo
Research technician
University of Oviedo
Oct 2019 – Jan 2021
Development of computational health models for the treatment of rechargeable batteries.
 
 
 
 
 
University of Oviedo
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

Publications

(2023). ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection. In Journal of Power Sources.

PDF Cite Code Dataset Project DOI

(2023). Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment. In Array.

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(2023). Physics-informed learning under epistemic uncertainty with an application to system health modeling. In International Journal of Approximate Reasoning.

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(2023). Simplified models of remaining useful life based on stochastic orderings. In Reliability Engineering & System Safety.

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(2023). Enhancing Time Series Anomaly Detection Using Discretization and Word Embeddings. In SOCO.

PDF Cite Project DOI

(2022). Li-ion battery degradation modes diagnosis via Convolutional Neural Networks. In Journal of Energy Storage.

PDF Cite Code Dataset Project DOI Demo

(2022). Weakly Supervised Learning of the Motion Resistance of a Locomotive Powered by Liquefied Natural Gas. In SOCO.

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(2022). Informed Weak Supervision for Battery Deterioration Level Labeling. In IPMU.

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

PDF Cite Code Dataset Project DOI Demo

(2022). RUL-RVE: Interpretable assessment of Remaining Useful Life. In Software Impacts.

PDF Cite Code Dataset Project DOI Demo

(2020). Graphical analysis of the progression of atrial arrhythmia using recurrent neural networks. In International Journal of Computational Intelligence Systems.

PDF Cite Code Project DOI

(2020). Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators. In EUSFLAT.

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