I am passionate about artificial intelligence and data science. I have a strong background in various modeling paradigms, including supervised, unsupervised and reinforcement learning. I use tools such as Python and Scikit-Learn proficiently to address classification, regression and clustering challenges. Exploring areas such as Computer Vision and Natural Language Processing, I have successfully solved complex problems with TensorFlow and Pytorch. In addition, I demonstrate proficiency in SQL for databases and leverage Azure Studio for cloud computing. My commitment to continuous learning constantly drives me to broaden my horizons of expertise.
📄 Medium
👨🏾💻 Kaggle
🎓 Education 🎓
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Ph.D., Computer Science Computing Research Center (Jun 2027) -
M.S., Computer Engineering Computing Research Center (Jun 2023) -
B.S., Control Engineering National Polytechnic Institute (Dec 2020)
Work Experience
Data Scientist @ Bluetab Solutions México (Jan 2024 - Currently)
- (BBVA) I have conducted statistical tests for the estimation of credit risk models, applied regularization methods to mitigate their deficiencies, and used tools for data analysis and processing. I have also generated documentation in compliance with regulations and developed dashboards to display the capital impact estimates derived from the implemented risk models.
- (Bluetab) I am the technical lead of a team at Bluetab, where I am responsible for addressing their questions and overseeing their activities.
Classroom Lecturer @ FEMEX (Dec 2022 - Jan 2023)
- Demonstration of the Habitat AI simulator via python framework. A demonstration of reinforcement learning models (PPO) as well as classical models (SLAM) within the simulator was presented with Python.
💡Projects
🍅 Image Segmentation for greenhouse 🍅 (2023-2024)
A series of state-of-the-art models — including Segformer, MaskFormer, Mask2Former, YOLOv11, and UNet — were implemented and fine-tuned for image segmentation in greenhouses. This project, covering everything from data collection (with a custom-built dataset) to deployment in Hugging Face Spaces, provided a comparative evaluation of each model’s performance in agricultural environments.
The approach utilized Hugging Face tools (Pipelines, Datasets, AutoTrain), along with PyTorch, Segments.ai, and Gradio, ensuring an end-to-end solution tailored to assess and deploy these models for agricultural applications.
🌽 Autonomous Navigation Vision System 🌽 (2023)
An autonomous navigation system was developed for agricultural robots based on the tracking of corn rows through image segmentation with deep learning models. The system was implemented on Jetson Xavier and Arduino devices. Languages and frameworks such as Python, Pytorch and Tensorflow were used.
❗ Risk Classification ❗ (2024)
This project intends to evaluate the risk of individuals as Good, Medium and Bad according to variables such as age, income, number of children, credit cards, mortgage among others for decision making. Cleaning and preprocessing tasks, outlier detection as well as modeling (AdaBoost) for the classification task were performed. Libraries such as Pandas, Plotly, Scikit-Learn and NumPy were used.
🍄 Fungi imaging with diffusion models 🍄 (2023)
This project aims at the unconditional generation of fungal images using a DDPM (Denoising Diffusion Probabilistic Models) model with a UNET architecture, with an image size of 128x128 pixels. The dataset used was extracted from Kaggle and two different approaches were explored: a model trained with the whole dataset and another trained only with two specific classes of fungi.
Libraries and tools:
- HuggingFace
- Datasets
- Kaggle CLI
- Pytorch
- Accelerate
- Wandb
🐄 Analysis of Milk Quality 🐄 (2023)
Classification of milk quality based on a dataset obtained from observations at milking. Interesting dimensional reduction techniques were applied as well as the deployment of the classifier in Flask, also Scikit-learn tools were used.
Further training
I am a person who is constantly learning, that is why I like to attend workshops, conferences or take online courses on professional topics or hobbies.