Giovanni Molina

About:

Computer Science PhD candidate with experience in Image Processing, Computer Vision, Machine Learning, Deep Learning and Natural Language Processing. Currently applying machine learning techniques to improve medical image processing and MRI image reconstruction. | Optical navigation software developer at Intuitive Machines.

Research Interests

Computer Vision, Machine Learning, Deep Learning, Medical Image Processing, NLP.

Ongoing Research

I'm currently working on four main research topics:

  • Medical Image Synthesis - Using machine learning and deep learning (GANs, FCNNs) techniques to generate new medical image data with customizable parameters.
  • Medical Image Reconstruction - Using machine learning and deep learning (GANs, FCNNs) techniques to reconstruct MRI data from low resolution source images or k-space.
  • Medical Image Segmentation - Applying machine learning techniques to improve segmentation of anatomical structures in medical images from multiple modalities (MRI, CT, X-Ray).
  • Interactive Machine Learning - Integrating machine learning with Holographics to provide immersive and responsive aid to surgeons before, during and after surgeries.

Publications

  1. Stuart S., Molina G., Crain T. (2020). THIN VPU: OPEN SOURCE VISION PROCESSING FOR SPACE NAVIGATION. In 2020 AAS G&C Conference.
  2. Molina, G., Velazco-Garcia, J. D., Shah, D., Becker, A. T., Seimenis, I., Tsiamyrtzis, P., & Tsekos, N. V. (2019, October). Automated Segmentation and 4D Reconstruction of the Heart Left Ventricle from CINE MRI. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 1019-1023). IEEE.
  3. Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim, Abdelati Hawwari, Nicolas Rey-Villamizar, Mona Diab and Thamar Solorio. Overview for the Second Shared Task on Language Identification in Code-Switched Data, In Proceedings of the Second Workshop on Computational Approaches to Code Switching (EMNLP 2016), Austin, Texas, Association for Computational Linguistics, 40-49. (Presentation)
  4. Fahad AlGhamdi, Giovanni Molina, Mona Diab, Thamar Solorio, Abdelati Hawwari, Victor Soto and Julia Hirschberg. Part of Speech Tagging for Code Switched Data, In Proceedings of the Second Workshop on Computational Approaches to Code Switching (EMNLP 2016), Austin, Texas, Association for Computational Linguistics, 98-107. (Presentation)

Contact

e-mail: giovanni@gemolina.com
Google Scholar: Giovanni Molina
LinkedIn: Giovanni Molina
Twitter: Giovanni Molina
CV: Giovanni Molina
MRI Lab
RiTUAL Lab (Alumni)
Intuitive Machines