Personal details

Jonathan O. - Remote full-stack developer

Jonathan O.

Based in: 🇹🇮 Colombia
Timezone: Bogota (UTC-5)

Summary

He has over three years of experience as a Full-stack (ReactJS, Java, Python, NodeJS) using the Scrum Agile methodology, and one as an SRE. At National University of Colombia MedellĂ­n branch, he published a paper. He was also a teaching assistant for nearly four years in a functional and logical programming subject in which he got passionate about topics such as graph-oriented databases, functional programming, and logical programming. Co-organizer of ScaleConf Colombia, JSConf Colombia, NodeConf Colombia, and MedellĂ­n DevOps with knowledge in areas such as logistics, with teamwork capabilities, and a good level of English.

Work Experience

Full-Stack Developer
Perficient | Aug 2021 - Jul 2023
TypeScript
React
Adobe Experience Manager
NestJS

Developed a web application where users can visualize each available vehicle with all its features and initiate the vehicle purchasing process.

SRE
NodeSource | Apr 2021 - Jun 2021
Ansible
Atmosphere
Terraform
AWS (Amazon Web Services)

Provided maintenance for the entire company's infrastructure, and developed new pipelines.

Education

National University of Colombia MedellĂ­n branch
Bachelor's degree・Systems and informatics engineering
Jul 2014 - Jul 2023

Personal Projects

Automatic skin lesion segmentation on dermoscopic images by the means of superpixel mergingIconOpenNewWindows
2018
Python
OpenCV
Academical article - We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images. The segmentation is carried out by over-segmenting the original image using the SLIC algorithm, and then merge the resulting superpixels into two regions: healthy skin and lesion. The mean RGB color of each superpixel was used as merging criterion. The presented method is capable of dealing with segmentation problems commonly found in dermoscopic images such as hair removal, oil bubbles, changes in illumination, and reflections images without any additional steps. The method was evaluated on the PH2 and ISIC 2017 dataset with results comparable to the state-of-art.