Gustavo A. Salazar-Gomez

I am a Mechatronics Engineer with a Postgraduate Diploma in Artificial Intelligence from Universidad Autonoma de Occidente (UAO), mainly interested in topics related to Mobile Robotics, Intelligent control systems, Artificial intelligence, Navigation, Aerospace, Computer vision, Machine learning and Deep learning.

My BEng degree project was entitled “Object recognition in images using deep learning” where I used transfer learning in order to retrain the last layers of inception-v3 neural network and created new categories from a specific environment under the supervision of Jesus Alfonso Lopez at UAO.

Currently, I’m a MSc student in Mobile, Autonomous and Robotic Systems at Grenoble INP, doing an intership at INRIA's Chroma lab.

Email: gustavo [dot] salazar [at] grenoble-inp [dot] org

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• [September 2022] Defended my Master thesis "Transformer-based Lidar-RGB Fusion for Semantic Grid Prediction in Autonomous Vehicles" performed at INRIA's Chroma lab.
• [March 2022] Started an internship at INRIA's Chroma lab, exploring sensor fusion techniques for robust situational awareness for Autonomous Vehicles.
• [Sept 2021] Started a MSc in Mobile, Autonomous and Robotic Systems at Grenoble INP.
• [June 2021] Finished Postgraduate Diploma in Artificial Intelligence at Universidad Autonoma de Occidente.
• [June 2021] The abstract High-level camera-LiDAR fusion for 3D object detection with Machine Learning was presented at the LatinX in AI Research Workshop at CVPR 2021 as a poster presentation.
• [August 2020] Started a Postgraduate Diploma in Artificial Intelligence at Universidad Autonoma de Occidente, the degree will end in June 2021.
• [January 2019] The article "Application of Transfer Learning for Object Recognition Using Convolutional Neural Networks" was selected to be published as an extended version in Applications of Computational Intelligence book part of the Springer Communications in Computer and Information Science book series (CCIS, volume 833).
• [May 2018] The paper "Application of Transfer Learning for Object Recognition Using Convolutional Neural Networks" was published in the conference IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI).

3D object detection of vehicles in the NuScenes dataset using classic Machine learning such as DBSCAN and SVMs.

A transfer learning technique is used to create a computational tool that recognizes the objects of the automatics laboratory of the Universidad Autónoma de Occidente in real time. The Inception-V3 is used as a pre-trained CNN.

The Inception-V3 is used as a feature extractor and a softmax classifier is trained from scratch, this contains the new classes that are going to be recognized. It was used Tensorflow framework with GPU in Python.

Style-transfer implementarion based on the paper A neural algorithm of artistic style using VGG-19.

Convolutional Neural Network Models LeNet5 and VGG16 with Tensorflow for MNIST dataset, trained with GPU on Colab.

Machine Learning projects from Supervised methods like KNN, Decision Trees and ANN, and Unsupervised methods like DBSCAN and PCA, finally feature engineering to improve models performance.

Generative Adversarial Networks(GAN) used to generate cars images from CIFAR10, trained with GPU on Colab.

Face recognition using LBP features, the system extract the LBP features from the face in the image and a SVM is trained in order to classify each face. There could be multiple faces in the scene.

Sudoku recognizer using OpenCV to process the image, and a KNN as ML model to classify each digit found, finally the result is displayed in a grid where the numbers are in the same position as the image.

• Introduction to Satellite Communications by Institut Mines-Télécom on Coursera. Certificate earned on January 2, 2020. [Credential]