🧑‍💻 Professional Experience

Here you can find my main professional roles. I have more 🙂 if you want to know about my full professional experience ask me!

🧑‍🔬 R+D Developer: Data, IA and Computer Vision

Aeorum

Where: Aeorum
When: Aug/2020 - Present
What I do: I've developed solutions in different fields:
Deep Learning: Implementing perception in unmanned aerial vehicles through the use of deep neural networks (YOLO, SSD-MobileNet...) and classic computer vision algorithms. Training and deploying models in GPU and embeded systems. Integrating it functionality using C++, Docker, Python and ROS.
Data: As Data Engineer building from scratch the company's data layer: big data processing with Spark, and real-time data processing with Kafka. As Database engineer taken decisions on how to store data, using databases like Mongo, MySQL and Redis. taking part on the architecture and deployment decisions.
Structure from Motion: building 3D reconstructions out of 2D images. Using images from drones to create a point cloud that represents the environment seen by de UAV.
Plant Health Analysis: Reasearch project to use pictures taken from special cameras to retrieve information about field health status. Building specific wavelength orthomosaics to be used by agronomists in order to take data based decisions on how to take care of the field.
Photovoltaic Panels Analysis: Reasearch project to use pictures taken from special cameras to retrieve information about photovoltaic panels. Detecting anomalous thermal registries on photovoltaic cells to find possible damages on an early stage.

UMA

Where: University of Malaga
When: Dec/2018 - Mar/2020
What I did: Part of the Smart-Campus I projects (DIAS2P and StreetQR): We built a solution to reduce Car-Pedestrian collisions attaching embeded systems running object detection neural network architectures into traffic signs so drivers could be warned about the intention of pedestrians to cross the street using lights. Registries about urban mobility were also recorded. The paper was published at CIPI 2019 and some media like 20 Minutos and Málaga Hoy published news about the project. My role by field:
Deep Learning: Training different neural networks architectures (YOLO, SSD-MobileNet, Mask-RCNN...) to detect and predict real-time car-pedestrian collisions. Deployment in IoT and embedded systems like Jetson Nano Family. Object tracking algorithm development (Hungarian Algorithm).
Electronics: Designing electronic architecture to run some actuators (sound and lights) that warn the driver about the presence of pedestrinas in the street.
Data: Building an API using Firebase to register statistics into a database about how many cars/pedestrians crossed the street.

Monkeat

Where: Monkeat
When: Mar/2019 - Oct/2019
What I did: As part of the incubation program offered by Demium we founded Monkeat, a livetime marketplace to help pubs to boost their customer acquisition through real-time offers. As CTO I developed a full Progressive Web App using Vue + Ionic, MongoDB, Flask and Firebase. We could not finally validate our MVP and closed the project months after.