Junior Engineer and Undergraduate Researcher with a strong background in Python (+5 Years), Machine Learning, and Data Analytics (+2 Years). Proficient with Large Datasets, Data Visualization, Feature Engineering, etc.
3.84 GPA; Physics; Math Minor; Gator Motor Spots; Society of Physics Students
- Made 2 ML Packages (more below)
- SciKit Learn, Keras (+3 Years)
- XGBoost, Tensorflow (+2 Years)
- SQL, Google BigQuery (+1 Years)
- Excel: Physics research, various simulations, personal projects
- Datasets: Kaggle, Hugging Face, SKLearn, SQL queries in Python
- Best Competition: 99th Percentile
- Avg. Competition: ~70th Percentile
- Over 50 contributions per month
- Completed 6 ML/DS Courses
Designed due to a need for streamlined ML Pipelines, this package offers tools for data filtering, automatic feature engineering, Neural Network optimizers, model hyperparameter optimization, Ensemble tools, and more.
Sole developer of this ML/AI package written in Python. Offers a variety of models including fully customizable Neural Networks, Autoencoders, and some custom models. In part due to the Monte Carlo training algorithm, the models have minimal overfitting. These models have even successfully learned Chess, Snake, and more.
Received after 4 of 6 semesters for having >3.75 GPA: Fall 2020, Fall 2021, Spring 2022, Fall 2022
GitHub: SciCapt LinkedIn: Sean Saliga Kaggle: SciCapt My Page: SciCapt.Github.io