Projects
Here you can find the different projects I have participated in regarding machine learning. This projects have been done at the University, at work or just for fun!. I invite you to check them out.
BIOFEEDBACK (2022)
BIOFEEDBACK is an artistic performance where you can experience the generation of art from brain frequencies in real time. On the one hand, sound is generated and on the other, images, which will be the next stimulus for the next generation.
In the last edition, expectators were able to get an NFT home to recall the experience!. This project was done with ANT and supported.
Technology stack: Python, Tensorflow, Raspberry Pi, EC2, Pure Data
Waste Classifier (2022)
Rootstrap’s Engineers Used Machine Learning to Build a ‘Waste Classifier App’ to Help Correctly Deposit Waste
Technology stack: Python, AWS, Flask
Prototype “Application of Private Aggregation of Ensemble Models to Sensible Data” with PySyft (2022)
A proof of concept of concrete implementation in the OpenMined PySyft platform of the solution proposed in "Application of Private Aggregation of Ensemble Models to Sensible Data" for the protection of private data.
Technology stack: Python, PySyft, Docker
ORT University
Framework for the automatic generation of logs for the training of machine learning models (2021)
Because the log data generated by the sites generally contains proprietary information, it is difficult to obtain massive data from these sites. Due to the lack of data for training machine learning models, both on attacks on sites and on normal traffic, the need arises to be able to generate data that can be used as a training set.
Technology stack: DVWA, DVPWA, ZaProxy, PortSwigger, Sqlmap, Postman, WAF, Threadfix, Python, Docker, Kubernetes, Classification, Jupyter Notebook
ORT University
Related projects:
Building a Scalable & Isolated Architecture for Preprocessing Medical Records (2022)
After performing several experiments with Airflow, we reached the best architectural design for processing text medical records in scale. Our hybrid solution uses Kubernetes, Apache Airflow, Apache Livy, and Apache cTAKES. Using Kubernetes’ containers has the benefit of having a consistent, portable, and isolated environment for each component of the pipeline. With Apache Livy, you can run tasks in a Spark Cluster at scale. Additionally, Apache cTAKES helps with the extraction of information from electronic medical records clinical free-text by using natural language processing techniques to identify codable entities, temporal events, properties, and relations.
Technology stack: Airflow, Kubernetes, Docker, Spark, cTAKES, Apache Livy
PCA and Clustering over COVID data (2020)
Analyzing the Coronavirus outbreak in France and South Korea.
Technology stack: Python
Song Dance Detector (2020)
A classifier that detects your dance poses and if it detects you are dancing the YMCA song, it starts playing the song until detects you are not dancing it anymore.
Technology stack: Python, Tensorflow, Posenet, Magenta, Docker, Javascript, Tune.js
Property Evaluation in NYC (2019)
The project was done at Texas Tech University as a Capstone project with Marcin Grzechowiak, Roger Valdez, Arjuna Menon and Jaimie Capps. The aim of the project is to predict the value of the property with or without land in NYC.
Technology stack: Python, R
The Austin Project (2018)
Visualization of Austin local information such as Car accident, house prices and alcohol consumption. This project was done at Texas Tech University as a collaborative effort with Marcin Grzechowiak, and Roger Valdez.
Technology stack: Python, Tableu
The World Report (2018)
Comparing countries from the economical, social and government perspectives. This project was done in collaboration with Marcin Grzechowiak, Roger Valdez and Jereamy Riggs
Technology stack: R
Application of Microservices on an SOA architecture with quality of service restrictions (2016)
Computer Science Thesis: Investigate Microservices architecture and migrate a system from a SOA architecture to microservices.
Technology stack: Docker, Kubernetes, RabbitMQ, Nginx, Consul, Mysql, REST, Java
Concepts: Microservices, Throttler, Circuit Breaker, Consistency, Concurrency, Fault Tolerance, State Machine