Dr

Ana Namburete DPhil (Oxon) BASc

Royal Academy of Engineering Research Fellow

RAEng Engineering for Development Research Fellow

Associate Research Fellow at St Hilda's College

  • Biography
  • Research

Biography

Dr Ana Namburete completed a BASc Honours degree in Biomedical Engineering (2011) in the Department of Engineering Science at Simon Fraser University (Canada), where she worked on automatically quantifying the mechanics of muscle contraction during live cycling trials.

In October 2011, she moved to Oxford to pursue a doctorate degree supported by a Commonwealth Scholarship. Following completion of her DPhil, Dr Namburete joined St Hilda's College as an Associate Research Fellow.

In 2016, she was awarded a Royal Academy of Engineering (RAEng) Research Fellowship under the Engineering for Development Research scheme, at which point she was admitted into Faculty at the Department of Engineering Science.

Other Achievements

  • Dr Namburete delivered the keynote presentation at the MOZEFO Young Leaders Conference (Maputo, Mozambique)
  • In October 2018, Dr Namburete recieved the Best Paper Award at the International Workshop on Perinatal, Preterm and Paediatric Image Analysis. It was presented for her paper 'Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas', written with Junior Research Fellow Dr Bartek Papiez.

Research Interests

Dr Namburete's research is guided by the fact that ultrasound imaging is amongst the first steps in a continuum of pregnancy care. Although ultrasound machines are increasingly available in low-income settings, the lack of suitably trained sonographers presents a significant roadblock for delivery of safe and reliable prenatal care. 

Her research focuses on creating computational algorithms (using machine learning) to enhance the diagnostic value of sonographic images. Through this work, Dr Namburete's group aims to establish ultrasound as a cost-effective tool for early assessment of brain maturation during pregnancy.

Current Projects

  • Deep learning techniques to track fetal brain development
  • Automated detection of cerebral abnormalities in the womb
  • Fetal brain atlas construction