Key accomplishments

EXPERIENCE IN MACHINE LEARNING:
• Object-Oriented Programming, coding best practices, CD/CI, Agile methodologies, unit testing and translate requirements into code.
• Optimization of Pytorch models with OpenVINO (and ONNX).
• 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 (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 U-Net.
• Image Super-Resolution with Deep Neural Networks.
• Time Series Forecasting using Deep Neural Networks, RNN’s, LSTM, GRU, etc.
• Image classification using hybrid Quantum-Classical neural networks with Pennylane and Pytorch.
• Natural Language Processing using HuggingFace Transformers and OpenAI.

PUBLICATIONS:
• Bayro-Corrochano, E., Solis-Gamboa, S., Altamirano-Escobedo, G., Lechuga-Gutierres, L., & Lisarraga-Rodriguez, J. (2021). Quaternion spiking and quaternion quantum neural networks: theory and applications. International Journal of Neural Systems, 31(02), 2050059. https://doi.org/10.1142/S0129065720500598
• E. J. Bayro-Corrochano, G. Altamirano-Escobedo, A. Ortiz-Gonzalez, V. Farias-Moreno and N. Chel-Puc, “Computing in the Conformal Space Objects, Incidence Relations, and Geometric Constrains for Applications in AI, GIS, Graphics, Robotics, and Human-Machine Interaction,” in IEEE Access, vol. 10, pp. 112742-112756, 2022.
https://doi.org/10.1109/ACCESS.2022.3216266
• Altamirano-Escobedo, G., Bayro-Corrochano, E. Quaternion Quantum Neural Network for Classification. Adv. Appl. Clifford Algebras 33, 40 (2023). https://doi.org/10.1007/s00006-023-01280-0

INTERNATIONAL CONFERENCES:
• 13th International Conference on Clifford Algebras and Their Applications in Mathematical Physics. Session: To machine learning and beyond: data science in mathematics, physics and engineering. Presentation: “Quantum Convolutional Geometric (Clifford) Neural Network”.
• 12th International Conference on Clifford Algebras and Their Applications in Mathematical Physics.


Role 1
Data scientist
Role 2
Deep learning engineer
Test Score
AI, Machine learning, Data Science
100%

Unspecified
Any
Junior
Mexico
Any
NA
Onsite

Tags

AITech

Education

  • September 1 2010 - September 1 2014
    Unidad Académica de Física de la Universidad Autónoma de Zacatecas

    BSc in Physics

    Thesis Title: A Solution to General Relativity in 5D.

  • January 1 2015 - January 1 2017
    Instituto de Física de la Universidad Autónoma de San Luis Potosí

    MSc in Physics

    Thesis Title: Theoretical Analysis of the Semileptonic Decay of the Baryon Λ+𝑐.

  • January 1 2020 - Present
    Center of Research and Advanced Studies. CINVESTAV Unidad Guadalajara.

    PhD in Electrical Engineering (Research topic: Machine Learning)

    PUBLICATIONS: • Bayro-Corrochano, E., Solis-Gamboa, S., Altamirano-Escobedo, G., Lechuga-Gutierres, L., & Lisarraga-Rodriguez, J. (2021). Quaternion spiking and quaternion quantum neural networks: theory and applications. International Journal of Neural Systems, 31(02), 2050059. https://doi.org/10.1142/S0129065720500598 • E. J. Bayro-Corrochano, G. Altamirano-Escobedo, A. Ortiz-Gonzalez, V. Farias-Moreno and N. Chel-Puc, Computing in the Conformal Space Objects, Incidence Relations, and Geometric Constrains for Applications in AI, GIS, Graphics, Robotics, and Human-Machine Interaction, in IEEE Access, vol. 10, pp. 112742-112756, 2022. https://doi.org/10.1109/ACCESS.2022.3216266 • Altamirano-Escobedo, G., Bayro-Corrochano, E. Quaternion Quantum Neural Network for Classification. Adv. Appl. Clifford Algebras 33, 40 (2023). https://doi.org/10.1007/s00006-023-01280-0


Experience


Portfolio


Expertise

Python
90
Machine Learning
95

Languages

English,
Spanish

Honors & awards