• About me
  • Professional Me
  • Personal Me
• Projects
  • EPAL Project
  • Meru AI
  • WildAI
  • AWS Scholarship

I am a first-generation Hispanic and the first college graduate in my family. My goal is to break barriers and pave the way for others.

I want to democratize Artificial Intelligence(AI) and Machine Learning(ML) to help communities. By using AI/ML I can create programs that will improve the quality of life for a community and strengthen bonds between people. I am passionate about AI/ML because of its ability to scale and solve complex problems. Donations are greatly appreciated to the EPAL Project and Meru AI!

Amazon Web Services has given me this opportunity to tell my story and inspire others pursuing AI/ML by emphasizing the importance of learning and building. Whether you're in high school, college, or a self-starter learning on your own -- it is never too late to begin learning the power of AI/ML!

My passion is in helping others improve their quality of life and creating stronger bonds between people. I found that I can achieve this with the power of AI/ML. I first got started in AI/ML in the Fall of 2020 when I worked under an Assistant Professor to classify images of phytoplankton. This opened my eyes to the many strengths that this field has. My most recent project is an AI Coach for video games that provides actionable feedback for a player to improve their skills. By using AI/ML, I am able to tell stories through data and discover new insights into the gameplay of individuals. I have turned my AI Coach project into a company in which I am the CEO & AI/ML Lead. For any project that I am a part of, I take ownership and apply myself to the best of my ability.

🖥️ AI/ML:

⌨️Languages:

💾Databases:

On my off time, I love playing competitive games with my friends and going to Disneyland with my girlfriend. When I am not spending time with people that are close to me, I love thinking of ways that I can help people. My love for helping people initially started when I coached for esports teams such as Cloud9 and Tribe Gaming. I have since then extended my coaching skills to coaching at the Escondido Police Athletic League for ages 6-18. I currently coach and tutor individuals while working on my projects. I believe my passions can help others pursue their passions, and I want to do anything I can to help better themselves and their quality of life.

Fun fact: I've completed over 50 Escape Rooms within Orange and Los Angeles counties 🔍🕵️.
My favorite is "The Basement" in northern LA!

💡 Soft Skills

Mixing my passions together has led to some fun projects, including:

Github: Private , in development

The EPAL Project is an AI/ML powered recording system for sports, specifically basketball. The inspiration for this project came from wanting to see my brother's basketball games, and realizing that these special moments would be valuable to have available for other families as well. Using a Raspberry Pi, camera module, and an internet connection, the system is able to live stream the game, leveraging Amazon Web Services, and process the stream using aI/ML to create clips, keep score, and more!

🔧 Tools used: YOLOv5, Pandas, NumPy, Matplotlib; AWS: Kinesis/EC2/S3, Raspberry Pi, Camera Module, Python/C++

🧠 Concepts used: Computer Vision, Object Detection, Transfer Learning on YOLOv5, End-to-End Pipeline development for Machine Learning, Data ETL Pipeline Development

Github: Private , in development

Meru AI is initiated from my passions in gaming and coaching. Our pilot game, League of Legends, is a Multiplayer Online Battle Area (MOBA) that contains an immense amount of information that a user needs to absorb in order to understand the gameplay. This creates a steep learning curve, and can take the fun out of the game. In this project, I am using AI/ML to help players understand and improve their skills on a personal level by providing them with actionable feedback -- like a coach would do.

🔧 Tools used: Python, PyTorch, Keras, TensorFlow, NumPy, Matplotlib, Pandas, Neo4j Graph Database, SQL

🧠 Concepts used: Linear Regression, Multi-Class classification, Random Forests, CNN/RNN, GraphNN, Graph Database Relational Models, Data API Ingestion - ETL practices, Model Training/Testing/Evaluation

Github: Wild AI

WildAI is a tool created to help esports teams use fundamental data analysis in Wild Rift -- similar to the parent game League of Legends. Riot Games, the parent company for the aforementioned games, does not have a public API. Therefore, I created a Data Pipeline that extracts, transforms, and loads a Video Stream into a dataset. This enabled the potential for Data Science practices to be performed on the output datasets. The result was a number of datasets that helped the esports team I coached (Cloud9 Wild Rift) discover our strengths and weaknesses.

🔧 Tools used: YOLOv5, OpenCV, NumPy, Matplotlib, Pandas, SQL

🧠 Concepts used: Image Processing methods, Dataset organization and validation, Database creation, Data pipelin automation, Quantitative analysis

Read the full story here! Github: AWS Scholarship Program: Udacity AI Programming with Python Nanodegree

I applied for this scholarship at awsaimlscholarship.com. In order to be eligible, an applicant must complete the AWS DeepRacer Student League race and the provided AI/ML learning modules. This leads up to the applicant changing the code of a given template for training a "car" agent. The template is based on Reinforcement Learning and the goal is to complete a lap time under the 3:00 mark!

Once you meet the eligibility, you enter a pool of applicants that may be chosen for the Udacity "AI Programming with Python Nanodegree" course. This course covered the fundamentals within math, coding, and MLOps and tied it all together with an Image Classification system using transfer learning. You can look forward to learning about Linear Algebra, Calculus, Pandas, and NumPy for your math and coding fundamentals. From an AI/ML perspective the course does a great job at covering best data ETL practices as well as the full model training, evaluation, and inference. The course projects concluded with two ways of using the same Image Classifier: Command Line Interface and Jupyter Notebook.