cv

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Basics

Name Axel Alvarenga
Label Computer Scientist / Software Developer
Email ax.alvarenga19[at]gmail[dot]com
Url www.axelalvarenga.com

Work

  • 2025.02 - Present
    IT & Business Operations Support
    Borehole Seismic, LLC
    • Deployed web application prototype for asset management in AWS using a Linux EC2 instance.
    • Performed maintenance, configuration, and repairs of production computers for oil field operations.
    • Automated creation of backups of inventory data using Microsoft 365 automations.
  • 2023.07 - 2025.01
    Software Development & IT Intern
    Borehole Seismic, LLC
    • Resolved critical compilation error in proprietary Java software and enhanced its functionality by removing a batch file upload limitation, reducing time to task completion from 8 hours to less than 15 minutes, boosting operational efficiency and reducing errors, resulting in a more reliable workflow.

Volunteer

  • 2024.12 - Present

    Houston, Texas

    Research Collaborator
    Univesity of Houston Real-Time Systems Laboratory
    2nd author in 4-page research paper accepted for publication at top-tier Real-Time Systems conference Brief Presentations (RTAS 2025).

Education

  • Houston, Texas

    Bachelor of Science
    University of Houston
    Computer Science
    • Real-Time Systems
  • Master's Coursework
    Georgia Institute of Technology
    Computer Science
    • Machine Learning
    • Machine Learning for Edge Devices Seminar

Certificates

Publications

  • 2025.05.09
    Work in Progress: Biologically Inspired Dynamic Task Prioritization in Computer Vision Systems
    Jeremy R. Easton-Marks, Axel Rolando Alvarenga Munoz, Albert M. K. Cheng
    This paper describes our work in developing a computer vision system that more closely mimics the way the human vision system operates. Autonomous vision systems are crucial for modern vehicle safety, demanding real-time object detection and prioritization without external computational resources. Current systems struggle with this on-board prioritization. This paper presents a novel approach inspired by the human eye and brain, segmenting the video input into foveal and peripheral areas for specialized processing.

Skills

Machine Learning
Real-Time Systems
Python
Linux
AWS
Software Development
Systems Programming

Languages

English
Fluent
Spanish
Native Speaker

Interests

Real-Time Systems
Machine Learning
Edge Computing
Computer Vision
Operating Systems