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Achievements

Achievement: Pair Extraordinairex3Achievement: Pull Sharkx3Achievement: QuickdrawAchievement: YOLO

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araujocode/README.md

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Bruno Araujo

Computer Engineering student at Facens University, based in Sorocaba, Brazil. My work sits at the intersection of computer vision, deep learning, and applied research. Currently pursuing undergraduate research (IC) with a focus on Point Clouds.

Focus Areas

  • Computer Vision & 3D Reconstruction
  • Deep Learning (PyTorch)
  • Machine Learning & Statistical Modeling
  • IoT Systems & Environmental Monitoring

Projects

Deep Learning & Art Ambiguity Analysis

Investigates how deep learning models perceive and represent ambiguity in visual art. Combines vision models with aesthetic and perceptual analysis.

Deep Learning Parameter Stats

Tools for analyzing and visualizing the statistical behavior of parameters across neural network architectures during training.

Philosophical Topic Modeling

Applies NLP and topic modeling techniques to philosophical texts, surfacing latent conceptual structures across a curated corpus.

Energy Forecasting with Quantile Regression

Probabilistic energy demand forecasting using quantile regression, capturing uncertainty in predictions rather than relying on point estimates.

Stack

Languages:
Python C++ JavaScript

Frameworks & Libraries:
PyTorch OpenCV scikit-learn

Tools:
Docker Git Linux VS Code

Contact

LinkedIn GitHub

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  1. finger-counter-opencv finger-counter-opencv Public

    Python

  2. deep-learning-art-ambiguity-analysis deep-learning-art-ambiguity-analysis Public

    Python

  3. philosophical-topic-modeling philosophical-topic-modeling Public

    End-to-end machine learning pipeline for exploring Stanford Encyclopedia of Philosophy entries via scraping, TF–IDF vectorization, LDA/NMF topic modeling, KMeans/Agglomerative clustering, and inter…

    Python 1

  4. energy-forecasting-quantile-regression energy-forecasting-quantile-regression Public

    End-to-end pipeline for forecasting residential energy consumption using quantile regression with LightGBM, Optuna tuning, SHAP interpretability, and a Streamlit app.

    Python