Guillermo Giovanni Altamirano Escobedo

During the past 3 years I've worked in ML projects involving Deep Learning architectures, Deep Learning interpretability and medical image classification.

PhD EXPERIENCE IN MACHINE LEARNING:

• Histopathology image classification using state-of-the-art Deep Learning models (e.g. Swin Transformers).
• Transfer Learning for Medical Image Classification (fine-tuning of pre-trained Deep Learning models: ResNet50, VGG16, EfficientNet, Vision Transformers, Swin Transformers, etc).
• Explainable deep learning with GradCAM applied to histopathology and chest X-ray images.
• Medical image denoising (retina OCT images) using Deep Neural Networks.
• Medical image segmentation (Retina blood vessel segmentation) based on the U-Net model.
• Image Super-Resolution with Deep Neural Networks.
• Time Series Forecasting using different models of machine learning: Deep Neural Networks, RNN’s, LSTM, GRU, etc.
• Image classification using hybrid Quantum-Classical neural network models using Pennylane and Pytorch.

• BSc in Physics (2010-2014): Unidad Académica de Física de la Universidad Autónoma de Zacatecas; in Zacatecas, Zacatecas. Thesis Title: A Solution to General Relativity in 5D. 
• MSc in Physics (2015-2017): Instituto de Física de la Universidad Autónoma de San Luis Potosí; in San Luis Potosí, San Luis Potosí. Thesis Title: Theoretical Analysis of the Semileptonic Decay of the Baryon Λ+𝑐. 
• Phd in Electrical Engineering (2020 - Present):  Center of Research and Advanced Studies. CINVESTAV Unidad Guadalajara.