Welcome!

My name is Leandro and I’m a Machine Learning Engineer with a passion for leveraging data to solve complex problems.

About Me

With a background in Computer Science, I have started working with Machine Learning in 2007, during my Masters degree. After that, I used Machine Learning techniques to identify patterns in patients with breast and prostate cancer. During my PhD, I integrated and analyzed DNA and RNA data of patients with ADHD to identify patterns in the genes and biological functions underlying the disease.

 

Skills

•   Programming in Python (including Django and Flask), R, Bash, Javascript, C, PHP, Java, Perl

•   Machine Learning (Scikit-Learn, PyTorch, Tensorflow)

•   Statistical analysis, complex networks, Natural Language Processing, Recommender systems

•   Linux/Unix systems and high-performance parallel computing environments

•   Relational database management systems: MySQL, Postgres, SQLite

Teaching (Data Science and Computer Science)

I was a Computer Science lecturer for 4.5 years. I taught the following subjects at a university (FMU) in Brazil:

• Graph Algorithms
• Design and Analysis of Algorithms
• Data Structures
• Artificial Intelligence
• C Programming
• Game Development with Python
• Software Testing
• Object-Oriented Programming with Java

 

Machine Learning (recent projects)

At Gladstone Institutes, I built an end-to-end pipeline to:

1. extract, clean, and transform features

2. train, test, and validate different ML models

3. use SHAP values to check feature importance

4. compare the different inputs and models using tools like Weights & Biases and Neptune.ai (to be visualized by biologists)

At Meta, I worked on a project to predict the popularity/virality (number of views) of most of the content (videos, photos, posts, stories, etc.) on Facebook and Instagram. I was responsible for:

1. Creating/removing feature logging (PHP)

2. Adding new features to the model (Python)

3. Training/testing the model offline

4. Testing the model online (for a small, but increasing, percentage of clients/API requests)

5. Checking several metrics to ensure the model could be deployed to all clients, and

6. Deploying the model to all clients if the metrics look good.

Independently, I’m working on a project called “Videos to Books“, to leverage LLMs to create books based on a video playlist.

Areas of Interest

While I have experience with recommender systems and general ML systems, I’m also interested in computer vision and large language models.