Óscar A. Chávez Ortiz

Óscar A. Chávez Ortiz (He/Him/His)

A Final year Ph.D Candidate at The University of Texas at Austin.
My area of research revolves around understanding how Lyman-alpha
escapes from galaxies and its connection to galaxy properties
to better understand and improve Lyman-alpha based
reionization predictions. I leverage big-data analytics,
automated scripts, and ML frameworks to uncover the complex
mapping of galaxy properties to Lyman-alpha emission.

Research Focus

Understanding Lyman-alpha escape from galaxies to improve reionization predictions

I design and implement scalable data pipelines to process large observational datasets (≥1M records), engineer predictive features, and train machine learning models to infer key galaxy properties. My work emphasizes reproducibility, uncertainty quantification, and robust model evaluation, integrating Bayesian inference and probabilistic modeling to extract insights from diverse sets of astronomical data.

Machine Learning & Data Pipelines

Building predictive models and scalable workflows

I develop end-to-end pipelines for large datasets, including automated data cleaning, feature engineering, model training, hyperparameter optimization, and performance evaluation. I leverage scikit-learn, PyTorch, TensorFlow, and Keras for predictive modeling, and I apply Bayesian methods and MCMC sampling to quantify uncertainty and support robust scientific conclusions.

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Projects & Applications

Applying ML to research and healthcare

Beyond my core research, I work on a range of data science and machine learning projects spanning scientific, technical, and applied domains. These projects include designing end-to-end pipelines for data preprocessing, feature extraction, and model training, as well as exploring both classical and deep learning approaches. I place strong emphasis on model validation, generalization, and interpretability to ensure results are robust and meaningful. Click below to explore a full overview of my projects and ongoing work.

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Technical Skills

Languages, ML frameworks, data analysis, and visualization

Programming

Python (Expert), SQL (Intermediate), Git/GitHub (Intermediate), R (Basic)

ML & AI

scikit-learn, TensorFlow, Keras, PyTorch, Regression, Classification, Neural Networks (NN), Convolutional Neural Networks (CNN), Random Forest, XGBoost, Simulation-Based Inference (SBI)

Data & Analysis

Pandas, NumPy, SciPy, Matplotlib, Seaborn, Astropy, h5py, Photutils, emcee, Feature Engineering, EDA