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Hi there 👋, I am Margarita Bugueño


PhD student in the Research School "Data Science and Engineering" at Hasso Plattner Institut (HPI, Germany) in the Artificial Intelligence and Intelligent Systems Research Group

MSc in Informatics Engineering (UTFSM, Chile) | BSc in Informatics Engineering (UTFSM, Chile)

Former part-time Lecturer at Federico Santa María Technical University (UTFSM, Chile)


Links

Currently, you can reach me at or

Do you want to know me a bit more?

The brick walls are there for a reason. The brick walls are not there to keep us out. The brick walls are there to give us a chance to show how badly we want something
Randy Pausch

I, Margarita Bugueño, started my studies in 2013 pursuing a major in Informatics Engineering at Federico Santa María Technical University (UTFSM). I have found a deep enjoyment in learning programming and data analysis, excelling in courses related to computer science such as Machine Learning, Artificial Neural Networks, and Pattern Recognition. This prompted me to work as a teaching assistant in such subjects. Later, in 2017 and 2018, I joined YoC+, teaching programming in C++ language to college students, which motivated my application as an associated researcher at Chilean Virtual Observatory (ChiVO) carried out by UTFSM with the collaboration of the University of Chile and Huawei company. My Master's studies at UTFSM involved a thesis on text generation (as a data augmentation technique) for unbalanced text classification problems using state-of-the-art modules, Transformer and Self-Attention. In turn, the master's led me to work as a lecturer in the Informatics Department at UTFSM as well as an assistant researcher in the Millennium Institute for Foundational Research on Data (IMFD) in the Explainable Artificial Intelligence project facing several problems of interest such as fake news detection, harassment detection, analysis of controversy and others.

More interested in my profile? Here I share the different areas where I have worked:
  • Natural Language Processing: I explored this topic during my Master's thesis under the supervision of Professor Marcelo Mendoza. You can find my different contributions to the scientific community here:

Journal paper: IDA 20' (code)
Conference papers:CIARP 19'

  • Social Media: I explored this topic in the Millennium Institute for Foundational Research on Data (IMFD) with the collaboration of Professor Marcelo Mendoza and Alvaro Soto. You can find my different contributions to the scientific community here:

Conference papers: HCII 19', ECML-PKDD 19' (code)

  • Deep Learning: I explored different applications of Deep Learning models in the help of society. Astroinformatics was one of them under the supervision of Professor Marcelo Araya.

Journal paper: CLEI-EJ 19' (code), Astronomy and Computing 21' (code), Signals 21' (code)
Conference paper: CLEI 18', ADASS 19'

Margarita Bugueño's Projects

grtc_gnns icon grtc_gnns

Public repository of our paper accepted to the Findings of EMNLP 2023: "Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks?"

ml icon ml

tareas ñanculef

piic19 icon piic19

Public repository of our works in Exoplanet analysis with Deep Learning

simahcomp icon simahcomp

Public repository of our 1st place work at the SIMAH competition held at ECML-PKDD 2019

tarea2ml icon tarea2ml

Clasificadores en sklearn, fronteras, LDA/QDA/PCA, hiper parámetros.

transfore icon transfore

Introducimos Transformer For Ensemble (TransForE), un método basado en Transformer para trabajar problemas de clasificación de texto de múltiples clases con un fuerte desequilibrio de etiquetas a fin de combinar el aprendizaje de múltiples modelos base a partir de las salidas de ellos, así como el texto mismo, en una especie de máquina de ensamblado parametrizada cuyo propósito es mejorar, o al menos mantener, la eficacia de los modelos base utilizados. TransForE utiliza los conocidos módulos de auto-atención de múltiples cabezales, propio de Transformer, con el propósito de aprender a combinar las múltiples componentes de entrada.

transformer_as_ensemble icon transformer_as_ensemble

Public repository of paper " Learning to combine classifiers outputs with the transformer for text classification"

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