Leandro Bugnon
Associate Researcher
Santa Fe, Argentina
Summary
👤
Senior🎓
Top SchoolLeandro Bugnon is an associate researcher and AI scientist with 11 years of experience applying machine learning to computer vision, bioinformatics and biomedical signals across academic and industry settings. Based in Santa Fe, Argentina, he blends a strong research background (PhD in signals and computational intelligence) with hands-on engineering—contributing to open-source SOM libraries like MiniSom and SOMPY to improve core algorithms and testing. He works at CONICET and CellCo while teaching at Universidad Nacional del Litoral, routinely turning research insights into practical R&D collaborations and consulting projects. His recent work includes AI-driven design and characterization of biological sequences, reflecting a rare intersection of sequence biology and unsupervised learning expertise. Colleagues describe him as a pragmatic researcher who pushes algorithmic robustness (e.g., quantization and training control in SOMs) beyond pure prototyping into reusable tools.
11 years of coding experience
5 years of employment as a software developer
Doctorado en Ingeniería Mención en Señales Sistemas e Inteligencia Computacional, Doctorado en Ingeniería Mención en Señales Sistemas e Inteligencia Computacional at Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del LitoralBioingeniero Bioingeniería e ingeniería biomédica, Bioingeniero Bioingeniería e ingeniería biomédica at Universidad Nacional de Entre Ríos
Spanish, English
Github Skills (11)
machine-learning10
python10
numpy10
testing9
unsupervised-learning9
neural-network8
artificial-neural-networks8
clustering8
logging7
scikit-learn5
scikit5
Programming languages (10)
Github contributions (5)
sevamoo/SOMPY
Apr 2017 - Apr 2017
A Python Library for Self Organizing Map (SOM)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 2 PRs, 1 comment in 15 days
Contributions summary:Leandro primarily focused on modifying the core training and configuration logic of the Self Organizing Map (SOM) library. Their changes included adding parameters for more fine-grained control over training length and radius, incorporating maximum training length limits, and debugging quantization error handling. The commits reflect a focus on improving the SOM training process through more flexible parameter tuning and error management. These changes demonstrate a focus on the core functionality and performance of the SOM algorithm implementation.
python-librarypythonmapsomorganizing
JustGlowing/minisom
Jul 2021 - Jul 2021
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 1 PR, 2 comments in 4 days
Contributions summary:Leandro primarily contributed to enhancing the `MiniSom` library, a minimalistic implementation of Self-Organizing Maps. They modified the `distance_map` function, adding an option to compute the average of distances, and subsequently refactored the parameter name. The user also added unit tests to validate the new functionalities. These changes indicate a focus on improving the core algorithms and usability of the Self-Organizing Map implementation.
outlier-detectiondimensionality-reductionunsupervised-learningdeep-learningneural-networks
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