I'm an advanced Computer Engineering student at UCASAL focused on software development and artificial intelligence.

My work spans application architecture, backend services, local persistence, API integration and machine learning. I enjoy building systems end-to-end and exploring how AI can be integrated into practical software products.

Current areas of work include:

• Desktop application architecture with Tauri and Rust
• Backend systems and API design
• Relational data modeling and persistence
• AI agent and LLM integrations
• Machine Learning and Deep Learning
• Medical image analysis

Open-source desktop workspace for coordinating AI assistants, projects and conversations.

Architecture: React / TypeScript → Tauri → Rust → SQLite / AI providers

The native backend currently implements:

• Local persistence with versioned SQLite migrations
• Projects, conversations and message lifecycle management
• Provider-independent AgentAdapter architecture
• OpenCode session integration
• SSE streaming and tool activity handling
• Cancellation and recovery of interrupted executions
• Persistent mapping between local conversations and external provider sessions

The project is being designed so AI providers remain decoupled from the application layer and UI.

Rust Tauri React TypeScript SQLite OpenCode

Degree thesis focused on the application of artificial intelligence to brain MRI analysis.

The system explores classification and quantitative analysis of brain tumors using medical imaging and Deep Learning.

Current work includes:

• Multiclass MRI classification
• Transfer learning with ResNet50, Xception and EfficientNetV2
• TensorFlow / Keras experimentation
• 2D image processing
• Preparation and analysis of 3D NIfTI datasets
• FastAPI inference services
• React-based visualization interface

Python TensorFlow Keras FastAPI React Medical Imaging

Machine Learning study for the classification of potentially dangerous seismic events in underground mining.

The project evaluates Decision Trees, Naive Bayes, k-NN and Logistic Regression under a strongly imbalanced dataset.

The experimental methodology includes stratified cross-validation, SMOTE and analysis through Precision, Recall, F1, AUC and confusion matrices.

Machine Learning SMOTE Python Data Analysis

Languages
TypeScript · JavaScript · Python · Rust

Application Development
React · Node.js · NestJS · FastAPI · Tauri

Data
PostgreSQL · SQLite · MySQL · SQL Server

AI / ML
TensorFlow · Keras · Transfer Learning · Image Processing

Engineering
Git · GitHub · Docker · REST APIs · SSE · Scrum

Computer Engineering — Universidad Católica de Salta

Currently completing my degree and developing my thesis in artificial intelligence applied to medical imaging.