About
I am currently an electronic engineering student at the University of Antioquia (UdeA), deeply passionate about machine learning, deep learning, and computer vision research. My educational journey has cultivated a strong foundation in critical thinking and research skills, to apply machine learning algorithms for diverse fields, especially in Medical Image and Human Motion Analysis, and precision agriculture. I bring substantial hands-on experience in coding ML and DL algorithms using MATLAB and Python. My goal is to apply AI-driven solutions to real-world challenges. I am eager to contribute my knowledge and skills to the growth and success of Machine Learning research across various fields.
Electrical engineering
- Birthday: 02 september 1999
- Website: Click Here
- Phone: +57 305 779 0511
- City: Medellin, Colombia
- Age: 24
- Degree: Major
- PhEmailone: luis.torres1@udea.edu.co
- Freelance: Available
Skills
My expertise is primarily centered around the application and training of deep learning and machine learning models, with a strong focus on computer vision and image processing.
Grant/Awards
- Distributed Sensing Technology and Education Initiative (DSTEI) IEEE AESS Scholarship ($25.000):Grant to finance the design phase of a drone swarm system for coffee crop monitoring. The design and education process in aerospace systems engineering is conducted in 4 countries over a period of one year [link]
- Mentoring Experiences for Underrepresented Young Researchers (ME-UYR) IEEE SPS ($4.000) to attend ICASSP [link]: Financial support to cover travel and accommodation expenses to present a research paper at the ICASSP 2025 conference with University of Alberta mentor Dr. Li Cheng.
Resume
I've been deeply engaged in diverse research initiatives at the University of Antioquia, collaborating with the Group on Power Electronics, Automation, and Robotics (GEPAR), specializing in applied Computer Vision and Artificial Intelligence. I'm an active member of the student-led Machine Learning & Robotics research group, concentrating on probabilistic machine learning and Deep Learning, particularly in medical image analysis, segmentation, Pose estimation, and Human motion.
Beyond academia, I've dedicated my time to volunteering with the Aerospace and Electronic Systems Society (IEEE AESS) and the IEEE Chapter of Signal Processing Society (IEEE SPS)
Education
Electronic Engineering
2019 - Current
Universidad de Antioquia, Medellin
Técnico en programación de software
2014 - 2016
Servicio Nacional de Aprendizaje (SENA)
Volunteering
Drone Swarm System for Agricultural Surveillance of Coffee Crops in Colombia DSTEI PROPOSAL FOR IEEE AESS
March 2023- current
Volunteering IEEE-AESS Unicauca
- Integral project involving the development of a positioning system for in-flight navigation and mapping, programmed to integrate data from RGBD cameras and LIDAR sensors. In addition, a computer vision system specialized in the detection of health indicators in coffee crops has been created, using Convolutional Neural Networks (CNN) and digital image processing techniques. The implementation includes the use of the AgroCam geo Camera and the Parrot SEQUOIA multispectral sensor to optimize the collection of information in the agricultural field.
Vicechair of the Aeronautics and drones Division
April, 2021-March 2023
Volunteering IEEE-AESS Unicauca
- Researcher in aerospace electronics, space robotics, drones, image processing, and artificial intelligence.
- First place ALA ZAGI RACE 2022 professional category AI, autonomous driving programmer.
- Team leader project ROVEARTH IEEE radar challenge 2022 in New York city.[ Link]
- Provide lectures, courses, training and workshops in STEM technologies for young people between 7-17 years old.li>
- Co-organizer of Exploring the future 202X event. [Link]
- Volunteer in Hult prize regional Popayán Unicauca.
- Co-organizer and workshop leader in SPACE WEEK event.
- Third place in IA professional category in AESS ZAGI RACE 2021.
Research Experience
Medical image analisis.
March 2023 – August
Autosegmentation of computed tomography medical images for brain cancer detection | Machine Learning Group
- Define the network architecture to segment the images provided by the University of Manchester and program Convolutional Neural Networks using PyTorch to autosegment different parts of the brain.
- Perform a Bayesian analysis to compare with CNNs and obtain the best performance in auto segmentation.
Human Motion Analysis and Pose Estimation
October 2023 – Current
Analysis and tracking of children's gait to diagnose abnormalities using Artificial Intelligence | Machine Learning & Robotics Group, Vision and Learning Lab @ UAlberta
- This project involves the analysis and tracking of gait patterns in 3-5-year-old children, utilizing AI algorithms such as OpenPose and Yolo v8 to extract kinematic features from videos recorded by a Vicon camera. Additionally, Bayesian Neural Networks have been developed to quantify model uncertainty levels and estimate the latent force of the movement. To detect abnormalities in children, Convolutional Neural Networks (CNN) are implemented to obtain somatosensory area volumetric data from structural MRI. A multimodal algorithm has also been developed to correlate information obtained from gait videos and magnetic resonance imaging (MRI), providing a comprehensive approach for a more nuanced understanding of child development and potential abnormalities.
Real-Time Failure Detection in Race Walking using IMU and Cameras with Probabilistic Machine Learning | Machine Learning & Robotics Group.
- A comprehensive project has been undertaken involving the development of an embedded sensor system for inertial data collection during race walking. The analysis of race walking performance includes the extraction of features and points of interest from captured videos, employing models like OpenPose, HigherHRNet, and Pose Estimation Transformer for Single-View, along with C3D networks. To enhance fault classification and quantify model uncertainty, Bayesian Neural Networks and Gaussian Processes have been implemented in the system. This integrated approach aims to provide a robust and sophisticated system for evaluating and improving race walking performance.
Electronics engineering, Computer vision and AI modeling
August 2022 – June 2023
Young research intern in ALIES: Alimentadora de esquejes | GEPAR UdeA [Repository]
- Programming of the Computer Vision Algorithm for feature extraction to select and classify the types of cuttings (flowers) using the OpenCV library in Python.
- Calibration of the Ueye-IDS camera, with all its environment to obtain images in real time, and process the data for statistical analysis.
- Development of a desktop software with a graphical interface that displays statistics of the images stored in a database, provides information to the user and controls the operation of the machine
- Establish the integration between the software and hardware system for the precision agriculture system.