PROJECTS
View more →
Ctrl + LAB
A full-stack lab management system with a RAG-powered AI assistant.
FOLIVIX
An end-to-end system with a ViT model and Android app for maize leaf disease classification.
Gender Face Classifier
A classical ML project comparing Perceptron and SVM models for gender classification from facial images.
Satellite Image Classifier
A custom CNN for classifying high-resolution satellite imagery into 10 land cover types.
Face Detection
An adaptive face detection system improved with Reinforcement Learning from Human Feedback (RLHF).
Voice Classifier
A GUI-based system for training and testing KNN and SVM models on audio classification tasks.
S K I L L S
Technical
Soft
P R O J E C T S
Ctrl + LAB: Intelligent Lab Management System
A full-stack, responsive web application built to streamline academic lab management. It provides teachers with tools for scheduling, booking, and managing materials, while admins get full system oversight. Its core feature is 'Controly', a RAG-powered AI assistant using LangChain and a local LLM (Ollama) to answer contextual user queries. The entire system is containerized with Docker for seamless setup and deployment.
GitHubSkills
Results
FOLIVIX: Maize Leaf Disease Classifier
An end-to-end solution for maize leaf disease classification, featuring a Vision Transformer (ViT) model with 95% test accuracy. The system includes a Python backend with a Flask API for model serving and a native Android client, FOLIVIX, built with Kotlin and Jetpack Compose for real-time, on-the-field diagnosis. The app provides users with instant predictions, a filterable analysis history, and an in-app educational guide.
GitHubSkills
Results
Face Detection with RLHF
An advanced face detection system that integrates Reinforcement Learning from Human Feedback (RLHF) to continuously improve its performance. Built on a MobileNetV2 architecture, this project goes beyond static models by creating an adaptive learning loop. A custom GUI allows users to provide real-time feedback, which is used to retrain the model on challenging cases, achieving a 57% improvement in bounding box precision and a 64% reduction in overall loss.
GitHubSkills
Results
People Detection
This project showcases a classical machine learning approach to people detection, utilizing an Artificial Neural Network (ANN) trained on a rich set of custom-extracted features. Instead of relying on deep learning for feature extraction, this method demonstrates a deep understanding of feature engineering—using HOG, GLCM, and color metrics to classify image patches. The system is integrated into a custom GUI for real-time analysis of urban environments.
GitHubSkills
Results
Face Gender Classifier (Perceptron vs. SVM)
This project implements gender classification from facial images using two distinct classical machine learning approaches: a Neural Network Perceptron and a Support Vector Machine (SVM). Both models achieve over 90% test accuracy through an extensive feature engineering pipeline that combines color, texture (GLCM), shape (HOG), and geometric analysis. The project includes a user-friendly GUI built with CustomTkinter that enables real-time testing and side-by-side model comparison.
GitHubSkills
Results
Satellite Image Classifier
This project leverages a custom Convolutional Neural Network (CNN), 'SpectrumNet', to classify high-resolution satellite imagery from the EuroSAT dataset into 10 distinct land cover categories. The methodology includes robust data preprocessing, data augmentation, and a weighted loss function to handle class imbalance, achieving a 96% classification accuracy. The project is delivered with a user-friendly GUI built with Tkinter for real-time classification.
GitHubSkills
Results
Is this your voice? - Voice Classifier
This application provides a complete system for voice classification using KNN and SVM models. It features an intuitive GUI built with customtkinter that allows users to train models with custom hyperparameters, evaluate performance with confusion matrices and classification reports, and perform real-time classification on recorded or uploaded audio files. The system processes audio by extracting a rich set of acoustic descriptors like MFCCs, Spectral Centroid, and Zero-Crossing Rate.
GitHubSkills
Results
Animal Classifier (ResNet50)
An image classification system built using transfer learning with the ResNet50 model. This project fine-tunes the network to distinguish between cats, dogs, and snakes, achieving an impressive 98.67% accuracy. It features a custom Tkinter GUI for real-time, interactive predictions and employs robust data augmentation to ensure the model generalizes well to new, unseen images.
GitHubSkills
Results
E D U C A T I O N
University
Curriculum Map
Semester 1
Semester 2
Semester 3
Semester 4
Semester 5
Semester 6
Semester 7
Semester 8
Extracurricular Activities
Languages
Spanish
NativeEnglish
B2Chinese
HSK 1Courses
ABOUT ME
My Mission
My goal extends beyond engineering; I aspire to be an architect of the future. I leverage my skills in Artificial Intelligence not just to solve complex problems, but to design systems that fundamentally enhance our quality of life. I believe in a future where AI acts as a force for the common good, and my mission is to actively contribute to building that better, more intelligent world.
My Approach
Long-Term Vision
I build solutions that not only solve today's problem but are designed to scale and evolve into the future.
In-Depth Analysis
Before implementing a solution, I conduct a detailed analysis of the problem, considering various scenarios to choose the optimal path based on needs, resources, and time constraints.
World Impact
My motivation is to use AI to create a positive and meaningful impact on society and industry.
Beyond the Code
- Self-taught creator, editing engaging videos for my social media @alvarovasquez.ai.
- Passionate about music, playing instruments and exploring sound.
- Committed to physical discipline and strength training at the gym.
- Strategic thinker, honing my skills through competitive chess.
- Exploring the future of the decentralized web with Web 3.0 and Blockchain technology.
Academic Foundation
My education at the National Polytechnic Institute has provided me with a fundamental understanding of Artificial Intelligence. I not only master the mathematical theory and algorithms that drive the models, but also their practical application across diverse fields, their integration into complex systems, and the crucial ethical considerations that ensure responsible, human-centric technological development.
Professional Experience
AI Trainer (Mathematics & Programming)
Scale AI & Outlier AI | Nov 2024 - Sep 2025
I specialized in training Large Language Models (LLMs) using Reinforcement Learning with Human Feedback (RLHF), an advanced technique for aligning AI models. My role involved refining and correcting model-generated outputs to align with human-level intuition and quality. I acted as a subject matter expert across two key domains: in programming, by fixing bugs and optimizing code; and in mathematics, by rectifying and validating logical reasoning and solutions to complex problems, all with the goal of training high-precision AI agents.
SOCIAL MEDIA
Alvaro Vasquez
@alvarovasquez.ai
My Objective
We will live in a world transformed by AI, and knowledge is our greatest tool. Often, the theory and formulas are a barrier. My objective is to tear it down using intuitive animations, allowing anyone to understand the fundamentals of AI from scratch to adapt and thrive in this new era.
Featured Content