Daniela Godinho was born in Fundão in 1994 and completed her Masters (Mestrado Integrado) in Biomedical Engineering in October 2016, at Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa.
She has a strong interest in programming, in diferente languages, and using this to do both signal and image processing, as well as machine learning.
During her Bachelors, in 2013, she completed an internship in which she explored signal processing of uterus electromyography, which was part of a larger study which addressed premature births. In 2016, she completed her Masters dissertation in Instituto de Biofísica e Engenharia Biomédica (IBEB), she developed a classification-based application based on convulotional neural networks, a type of deep learning classifier, to classify electroencephalography signals.
In 2017 she started working as a researcher also in IBEB, and she implemented machine learning algorithms to improve microwave imaging for the detection and diagnosis of breast cancer. Later that year, she joined the team of NeuroPsyAI where she implemented image processing and machine learning algorithms in a product which is designed to aid doctors in the diagnosis of neuropsychiatric diseases.
Since March 2018, she is a PhD student in Biomedical and Biophysics Engineering at Faculdade de Ciências da Universidade de Lisboa under the supervision of professor Raquel C. Conceição (IBEB/FCUL) and professor Carlos A. Fernandes (IT/IST). In this project, the design, simulation and validation of a microwave medical diagnosis system for axillary lymph nodes will be completed. Such system will contribute for improved staging of breast cancer, reducing pre-emptive removal of health lymph nodes, and consequently reducing patient morbidity and health care costs.