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lourdesLB/README.md

Hi, I'm Lourdes Linares 👋🏽 👩🏽‍💻

I am a passionate student and researcher in Deep Learning and Data Science, currently working part-time with the Minerva research team at University of Seville since last year. My research encompasses areas such as Machine Learning, Deep Learning, Time Series Forecasting, and Explainable Artificial Intelligence (XAI). While I keep learning about Computer Vision and Natural Language Processing.

Following this research role, I am a Graduate in Computer Engineering and also in Mathematics, where I gained a solid foundation in Optimization and Artificial Intelligence. My academic journey is marked by a continuous drive to align my education with my passion for data science and research. I am currently enhancing my academic credentials with a Master's in Data Engineering and preparing to embark on a Doctorate.

I am always eager to evolve and grow, both personally and professionally. Beyond my profound interest in research, I always keep an open eye for new opportunities to learn and acquire new skills. That is why I also find a great interest in other areas such as Software Development.

Find me 🌐

  • More about me and my interest on linkedin
  • Research profiles on scholar researchgate university of seville

Hard skills 💻

Data Science

Python TensorFlow PyTorch Scikit-learn R

Web Development

MongoDB Express.js React Node.js Django HTML5 CSS3 JavaScript

More

Linux Docker Git

Java Haskell MATLAB AMPL

Noticeable publications 📚

A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma

International Conference on Hybrid Artificial Intelligence Systems (HAIS 2023)

This work proposes a methodology that combines preprocessing, feature selection and association rule mining to identify relevant genes and significant relationships in biological data from sarcoma patients.

Read more

Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting

National Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2024)

Awarded as second best paper of TAMIDA Symposium. This paper presents a novel feature selection approach that integrates a statistical metric based on the Conditional Dependence Coefficient with a multi-objective evolutionary algorithm. Applied to the prediction of multivariate time-series in fields such as air quality forecasting, electricity demand, urban traffic management, and preventive maintenance of systems, the method demonstrates strong performance and significant reduction in model complexity.

Read more

You can find the complete list of my works in my Scholar profile.

Pinned Loading

  1. Clustering-Library Clustering-Library Public

    Clustering library implemented by Pablo Reina Jimenez and Maria Lourdes Linares Barrera

    Haskell

  2. bag-of-songs bag-of-songs Public

    The Bag of Songs project focuses on the use of advanced AI and signal processing techniques for the classification and differentiation of music genres.

    Python

  3. Business-intelligence-HyM Business-intelligence-HyM Public

    Bussiness Intelligence project analysing 3 areas of an important fashion enterprise such as H&M: sentiment analysis on public opinion, sale's tracking dashboards and marketing sales prediction. Aut…

    Python

  4. Explainable-ML-R Explainable-ML-R Public

    Explainable Machine Learning short project containing a simple script showing how to apply XAI over a sample estimator using ModelStudio library.

    R

  5. CSS-animations-amblyopia CSS-animations-amblyopia Public

    A collection of exercises to train the "lazy eye" for people suffering from amblyopia. All of them have been developed using CSS Animations.

    CSS

  6. Newspaper-web-app Newspaper-web-app Public

    Web application with Flask in Apache showing news stored on a MongoDB Database and IT infraestructure. Developped by: Lourdes Linares-Barrera, Paula Gomez-Matos, Pablo Reina-Jimenez, Javier Galan-S…

    HTML