Leandro Fabio Ariza Jiménez
Descripción personal breve
Leandro Fabio Ariza Jiménez se graduó como Ingeniero Electrónico de la Universidad Industrial de Santander en 2006, y como Magíster en Matemáticas Aplicadas de la Universidad EAFIT en 2014. Actualmente es estudiante del Doctorado en Ingeniería Matemática de la Universidad EAFIT desde el año 2016. Desde el año 2007 al 2015, ha participado en varios proyectos de investigación relacionados con el análisis de imágenes biomédicas en diferentes universidades. Durante este tiempo, también ha estado involucrado en la enseñanza de cursos de pregrado de métodos numéricos, álgebra lineal, análisis de Fourier, y procesamiento digital de imágenes. Desde el año 2016, está vinculado al "Centro de Excelencia y apropiación en Big Data y Data Analytics – Alianza CAOBA" como investigador de posgrado.
Áreas de interés e investigación:
- Análisis de redes, detección de comunidades en redes complejas, aprendizaje de máquina, análisis de clústeres, métodos de reducción de dimensionalidad, algoritmos de visualización de datos, y procesamiento digital de imágenes.
Director:
Olga Lucía Quintero Montoya, Nicolás Pinel Peláez
Grupo de investigación: Modelado matemático
Publicaciones científicas más relevantes
- Leandro Ariza-Jiménez, O. L. Quintero and Nicolás Pinel, "Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings". Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018, pp. 1315-1318. ISSN:1557-170X.
URL:
https://ieeexplore.ieee.org/document/8512529
- Andrés Cardona, Leandro Ariza-Jiménez, Diego Uribe, Johanna C. Arroyave, and July Galeano, Fabian M. Cortés-Mancera. "Bio-EdIP: An automatic approach for in vitro cell confluence images quantification". Computer Methods and Programs in Biomedicine, Vol. 145, p. 23-33, 2017. ISSN 0169-2607.
URL:
https://www.sciencedirect.com/science/article/pii/S0169260716308598
- L. Ariza-Jiménez, J.C. Gallego-Gómez, y J.C. Cardona. "Imagenología celular y máquina de aprendizaje para evaluar la distribución subcelular de mitocondrias en células infectadas con dengue". Biomédica: Revista del Instituto Nacional de Salud. Vol. 35(Sup.1), p. 28, 2015. ISSN: 0120-4157.
URL:
https://www.revistabiomedica.org/index.php/biomedica/article/view/2857/2713
- Juan Carlos Cardona-Gomez, Leandro Fabio Ariza-Jimenez and Juan Carlos Gallego-Gomez. "A Proposal for a Machine Learning Classifier for Viral Infection in Living Cells based on Mitochondrial Distribution". En Cell Biology. Editorial: InTech, 2015. ISBN: 978-953-51-4322-2.
URL:
https://www.intechopen.com/books/cell-biology-new-insights/a-proposal-for-a-machine-learning-classifier-for-viral-infection-in-living-cells-based-on-mitochondr
- A. Cardona, L. Ariza-Jiménez, D. Uribe, J. Arroyave, y F. Cortés-Mancera. "Automatic Image Segmentation Method for in vitro Wound Healing Assay Quantitative Analysis". En IFMBE Proceedings, Vol. 49, 2014. ISSN: 1680-0737.
URL:
https://link.springer.com/chapter/10.1007/978-3-319-13117-7_98
- Leandro Ariza-Jiménez, Luisa F. Villa, Nicolás Pinel, y O. L. Quintero. “Extracted information quality, a comparative study in high and low dimensions”. In press: International Journal of Business Intelligence and Data Mining, ISSN 1743-8195.
- Leandro Ariza-Jiménez, Julián Ceballos, y Nicolás Pinel. “Standardized Approaches for Assessing Metagenomic Contig Binning Performance from Barnes-Hut t-Stochastic Neighbor Embeddings”. En IFMBE Proceedings, Vol. 75, 2020.
URL: https://doi.org/10.1007/978-3-030-30648-9_101.
- Leandro Ariza-Jiménez, Luisa F. Villa, y O.L. Quintero. “An entropy-based graph construction method for representing and clustering biological data”. En IFMBE Proceedings, Vol. 75, 2020.
URL: https://doi.org/10.1007/978-3-030-30648-9_41.
- Leandro Ariza-Jiménez, Luisa F. Villa, y O.L. Quintero. “Memberships Networks for High-dimensional Fuzzy Clustering Visualization”. En Applied Computer Sciences in Engineering (WEA 2019) - Communications in Computer and Information Science, Vol 1052, 2019.
URL: https://doi.org/10.1007/978-3-030-31019-6_23
- Leandro Ariza-Jiménez, O. L. Quintero and Nicolás Pinel, “Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings”. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018, pp. 1315-1318. ISSN:1557-170X.
URL: https://doi.org/10.1109/EMBC.2018.8512529
Trabajos
- On the relation between big data and machine learning
Recently the term “Big Data” was coined to capture the meaning of a data-explosion trend from diverse sources and domains, which society has been exposed due to technological advances since the second half of the 20th century. Machine Learning (ML) is a sophisticated analytical technology that can be used to provide us with intelligent analysis of Big Data. In this talk the relation between Big Data and ML is discussed, and two approaches of ML are presented: Clustering, and Deep Learning. The fundamental concepts of, and problems with Clustering will be discussed, followed by a description of some traditional algorithms and the presentation of experimental results in artificial datasets. With regard to Deep Learning, an introduction is given and future work in relation with big data is described.
Ver más.
- Transfer learning on an Autoencoder-based Deep Network
It is widely known that deep neural networks can be difficult to train in practice, since in order to obtain state-of-the-art results we need a great amount of data and computing power. However, we can overcome this issue either using autoencoders as way to “pre-train” deep neural networks or following a “transfer learning” approach. In particular, here we carried out several experiments to study how both approaches can benefit the training of deep networks.
Seminarios dictados :
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A network analysis approach for metagenomic binning.
Abstract: Metagenomics is the application of modern genomics techniques to the study of communities of microbial organisms directly in their natural environments. In metagenomics, the assignment of genomic fragments to the corresponding taxonomic group, e.g. species, genera or higher taxonomic groups, is commonly referred to as "binning", a procedure wherein each of the sequences is placed into an imaginary bin representing ideally only fragments belonging to this group. Since this is essentially a data clustering problem, here we attempt to develop and implement unsupervised strategies to address such problem. In particular, in this talk we will present the progress made by following a novel approach for the binning of genomic fragments based on similarity networks and community detection algorithms.
URL:
https://youtu.be/TVEgN5NUXEI
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Similarity-based clustering using a network analysis approach
Abstract: Networks represent relations between objects connected pairwise. Networks can have community structure, that is, objects interacting in a network can be organized into groups called communities. In addition, objects forming a community probably share some common properties as well as play similar roles within the interacting phenomenon that is being represented by the network. Thus, community detection can provide an insight into the structure of the networks. Evident interactions between entities are often represented as networks, such as a social network of friendships between individuals or a network of citations between scientific papers. However, networks can be also used to represent similarity relationships between objects. Then, when it comes to cluster objects based on the above criteria, this problem could be solved by means of network community detection algorithms, rather than follow a cluster analysis approach. In this talk we expose an alternative approach for data clustering based on network community detection algorithms. Details about the implementation and performance of this approach are given. In addition, this approach is exemplified by applying it in the identification and delimiting of microbial genomic populations.
URL:
http://envivo.eafit.edu.co/EnvivoEafit/?p=27326
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Transfer Learning on an Autoencoder- based Deep Network
Abstract: It is widely known that deep neural networks can be difficult to train in practice, since in order to obtain state-of-the-art results we need a great amount of data and computing power. However, we can overcome this issue either using autoencoders as way to "pre-train" deep neural networks or following a "transfer learning" approach. In particular, here we carried out several experiments to study how both approaches can benefit the training of deep networks.
URL:
http://envivo.eafit.edu.co/EnvivoEafit/?p=26235
-
On the relation between Big Data and Machine Learning
Abstract: Recently the term "Big Data" was coined to capture the meaning of a data-explosion trend from diverse sources and domains, which society has been exposed due to technological advances since the second half of the 20th century. Machine Learning (ML) is a sophisticated analytical technology that can be used to provide us with intelligent analysis of Big Data. In this talk the relation between Big Data and ML is discussed, and two approaches of ML are presented: Clustering, and Deep Learning. The fundamental concepts of, and problems with Clustering will be discussed, followed by a description of some traditional algorithms and the presentation of experimental results in artificial datasets. With regard to Deep Learning, an introduction is given and future work in relation with big data is described.
URL:
http://envivo.eafit.edu.co/EnvivoEafit/?p=25698