PROJECT:Generative AI: Deep Learning Model using GANs and Visual Transformers to generate deep fake images of Kidney Stones - Masked Autoencoders.Developed ML model for an Exoskeleton arm specialized for ALS patients.Utilized continuous muscle signals (EMG) data for arm movement classification. Assisted with control and communications using web sockets.

Conducted AI research in various cybersecurity domains.Developed face recognition models using CNNs and GANs. Deep fakes generation (Generative AI)Worked on NLP projects for named entity recognition and scams detection. Utilized RNNs, LSTM, and Bidirectional LSTM architectures. Also worked in text generation (Generative AI) using transformers.Python for Machine Learning and Computer Vision implementations. TensorFlow, Keras, PyTorch and SciKit Learn. Azure, SQL, Numpy and Pandas for… Show more Conducted AI research in various cybersecurity domains.Developed face recognition models using CNNs and GANs. Deep fakes generation (Generative AI)Worked on NLP projects for named entity recognition and scams detection. Utilized RNNs, LSTM, and Bidirectional LSTM architectures. Also worked in text generation (Generative AI) using transformers.Python for Machine Learning and Computer Vision implementations. TensorFlow, Keras, PyTorch and SciKit Learn. Azure, SQL, Numpy and Pandas for the manipulation of Databases.C Language for Microcontrollers programming. USB Protocol implemented in Microcontroller for biometrics bypassing. Show less

Research stay in Quantum Computing. During the stay I generated a hybrid Quantum Machine Learning Model for Quantum Natural Language Processing (QNLP) that I named "Strongly Entangling Neural Network" (SENN), inspired by the general architecture of a Recurrent Neural Network (RNN). This is a hybrid model composed by both, quantum layers and classical layers. Tested the model in some language classification tasks. Used Python as programming language, PennyLane library for the generation of… Show more Research stay in Quantum Computing. During the stay I generated a hybrid Quantum Machine Learning Model for Quantum Natural Language Processing (QNLP) that I named "Strongly Entangling Neural Network" (SENN), inspired by the general architecture of a Recurrent Neural Network (RNN). This is a hybrid model composed by both, quantum layers and classical layers. Tested the model in some language classification tasks. Used Python as programming language, PennyLane library for the generation of quantum circuits and quantum layers; and TensorFlow for the whole integration of the model and the training. Show less

PROJECT:Developed Deep Neural Network Architectures for autonomous vehicle control using TensorFlow and ROS.Integrated Odometry, Local and Global path planning for neural network control system.Data generation (Generative AI) for training purposes: Lidar Cloud Mapping.