Gabriel Larson
| Gabe | |
|---|---|
 |
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| gabe@oct6.org | |
| Location | Minneapolis, Minnesota |
| Education | University of Minnesota, Bachelor of Science in Data Science |
| Links | GitHub HuggingFace |
This article is about Gabriel Larson the data scientist. For other uses, see Gabriel (disambiguation).
Gabe Larson is an aspiring machine learning engineer. A graduate of UMN's College of Science and Engineering, Gabe's work focuses on the application of advanced machine learning techniques to solve complex computational problems. His technical expertise encompasses Python-based machine learning frameworks including TensorFlow and PyTorch, as well as extensive experience with statistical computing and database systems.
Currently seeking machine learning and data science opportunities, Gabe combines knowledge in statistical modeling with practical implementation skills in modern AI frameworks. In his free time Gabe is an enthusiast of the open-source AI community, with particular focus on local AI deployment and optimization.
▼ Projects and Papers
GABREIL
[ github , paper ]Implemented and compared multiple reinforcement learning agents in a poker environment using Python and Keras; developed custom environment wrapper and reward functions to enable AI agents to learn betting strategies
Doré LORA
[ article ]Developed a LoRA (Low-Rank Adaptation) model for Stable Diffusion XL that captures the distinctive artistic style of 19th-century illustrator Gustave Doré. Created using a curated dataset of 4000 high-resolution images and BLIP-2 for automated labeling.
PowerShell AI Assistant
[ github ]Developed a CLI utility that integrates PowerShell with local AI to provide instant analysis of command outputs, enabling rapid debugging and learning through natural language queries.
FED-STGODE
[ paper ]Implemented a privacy-preserving federated learning system for traffic flow prediction using PyTorch and the Flower framework, integrating multiple deep learning architectures (GRU, RNN, LSTM, STGODE) to analyze the PEMS traffic monitoring dataset. Achieved significant performance improvements with the STGODE model (98% reduction in prediction error compared to baseline approaches) while maintaining data privacy through distributed training across multiple nodes.
▼ Resume
Gabe's resume can be downloaded as a pdf here.
▼ Technical Skills
- Programming Languages: Python, SQL, R, Java
- Machine Learning: TensorFlow, PyTorch, Scikit-learn, etc.
- Data Science: Pandas, NumPy, Matplotlib, Jupyter
- Tools & Platforms: Git, Docker, AWS, llama.cpp
▼ Coursework
Advanced Machine Learning
Learning theoretical underpinnings of machine learning, and practical know-how to apply these methods to various problems and applications pertaining to machine learning and artificial intelligence
Operations Research for Data Science
- Examining real world cases of how various analytical methods have been used to transform a business or industry
- Analytical methods include: linear regression, logistic regression, trees, deep learning, missing data imputation, text analytics, clustering, optimization
Theory of Statistics
Development of probability and basic issues in statistics including probability spaces, random variables and their distributions and expected values, Law of large numbers, central limit theorem, and generating functions
Regression and Statistical Computing
- Analyzing data with the multiple linear regression model, including inference, diagnostics, validation, transformations, and model selection
- Designing and performing Monte Carlo simulation studies to improve understanding of coverage probability, Type I error probability, and power
Practice of Database Systems
- Understanding conceptual data models, physical data models, and representational data models
- Implementing a relational database design using the ER-to-relational mapping algorithm
Computer Vision
Exploring fundamentals of computer vision, including: registration (optical flow, image alignment, tracking), recognition (bag of features, template matching, object proposal), reorganization (graph cuts, superpixel, semantic segmentation), and reconstruction (camera geometry, epipolar geometry, stereo)