Professor
Alison Noble
CBE FRS FREng FIET FWES CEng DPhil (Oxon) MA
Technikos Professor of Biomedical Engineering
Fellow of St Hilda's College
Email:
Tel: 01865 617690
College: St Hilda's College
Location: Institute of Biomedical Engineering, Old Road Campus Research Building, Oxford OX3 7DQ

Professor Noble began her career at the University of Oxford where she obtained a first-class honours degree in Engineering Science (1986) followed by a DPhil (PhD) in Computer Vision (1989). Following PhD graduation, she became a research scientist at the GE Corporate R&D Center, Schenectady NY USA before returning to the University of Oxford Engineering Science faculty in 1995. Appointed a Professor of Engineering Science in 2002, Prof. Noble was the youngest person and jointly the first female to hold that title at that time. Appointed to her current senior position as the Technikos Professor in Biomedical Engineering in 2011 Prof. Noble is also Oxford’s first female Statutory Professor in Engineering. Prof. Noble was the Director of the Institute of Biomedical Engineering from 2012-16 and an Associate Head of the Mathematical, Physical and Life Sciences Division at the University of Oxford, 2016-19. She is currently a Fellow of St Hilda’s College, Oxford and an Honorary Fellow of Oriel College and of St Hugh’s College.

Professor Noble’s research interests sit at the interface of AI with computer vision and clinical medicine. In recent years, her group has been at the forefront of international thinking in how to bring machine learning to ultrasound working closely with clinical research groups in Oxford (particularly the group of Prof. Aris Papageorghiou, Nuffield Department of Women’s & Reproductive Health) and overseas. This includes global health ultrasound applications research with groups in India and Africa, and pioneering research on multi-modal ultrasound and sonography data science. Prof. Noble co-founded Intelligent Ultrasound Ltd to commercial research from her laboratory which was acquired by MedaPhor Group Plc (now called Intelligent Ultrasound Group) in 2017. Her research has attracted international awards and distinctions. Recent awards are: BMVA Distinguished Researchers (2022), Gabor Medal, Royal Society (2019); and the MICCAI Society Enduring Impact Award (2019). She held an ERC Advanced Grant from 2016-2023.

In addition to playing a prominent role in the development of her research field, Professor Noble has held science and engineering leadership roles on national and international bodies.

For instance, she chaired the Royal Society Privacy Enhancing Technologies (PETs) Policy Working Group which published data science policy reports in 2019 and 2023. Prof. Noble co-chairs the Royal Society Data Community of Interest and is a member of the Royal Society Science Policy Committee. She currently chairs the Royal Society Industry Fellowship joint panel.

Professor Noble is a former president of the Medical Image Computing and Computer-Assisted Interventions (MICCAI) Society, the international society in her field. She served as chair of the EPSRC Healthcare Technologies Strategic Advisory Team and is a current member of the EPSRC Science, Engineering and Technology Board. She was a member of REF 2021 Subpanel 12 (Engineering) 2018-22.

Professor Noble is a former Trustee of the Institute of Engineering Technology (IET) 2016-19 and a current Trustee of the Oxford Trust. She is a current Council member and trustee of the Royal Society.

Professor Noble is an ELLIS Fellow, a MICCAI Fellow, and a Fellow of the IET, the Royal Academy of Engineering and of the Royal Society. She received a CBE for services to science and engineering in the Queen’s Birthday Honours list in 2013.

  • Biomedical imaging and image analysis
  • Machine learning applied to healthcare
  • Ultrasound image analysis
  • Point-of-care ultrasound
  • Global health technologies

Professor Noble’s collaborators and research partners include:

Enquiries from potential postgraduate research students who have interests in any of my research areas are welcome.

Machine learning for accurate estimation of fetal gestational age based on ultrasound images
Lee LH,  Bradburn E,  Craik R,  Yaqub M,  Norris SA,  Ismail LC,  Ohuma EO,  Barros FC,  Lambert A,  Carvalho M,  Jaffer YA,  Gravett M,  Purwar M,  Wu Q,  Bertino E,  Munim S,  Min AM,  Bhutta Z,  Villar J,  Kennedy SH,  Noble JA,  Papageorghiou AT,  et al. (2023)
CNSEG-GAN: a lightweight generative adversarial network for segmentation of CRL and NT from first-trimester fetal ultrasound
Sarker M,  Yasrab R,  Alsharid M,  Papageorghiou A,  Noble J,  et al. (2023)
Densely Attentional Network for first trimester fetal ultrasound video automated CRL and NT segmentation
Gridach M,  Yasrab R,  Drukker L,  Papageorghiou A,  Noble J,  et al. (2023)
Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
Yasrab R,  Alsharid M,  Sarker M,  Zhao H,  Papageorghiou A,  Noble J,  et al. (2023)
Gaze-probe joint guidance with multi-task learning in obstetric ultrasound scanning
Men Q,  Teng C,  Drukker L,  Noble JA,  Papageorghiou AT,  et al. (2023)
Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for evaluation of assistive artificial intelligence
Bowness JS,  Morse R,  Lewis O,  Lloyd J,  Burckett-St Laurent D,  Bellew B,  Macfarlane A,  Pawa A,  Taylor A,  Noble JA,  Higham H,  et al. (2023)
International consensus on anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia.
Bowness JS,  Pawa A,  Turbitt L,  Bellew B,  Bedforth N,  Burckett-St Laurent D,  Delbos A,  Elkassabany N,  Ferry J,  Fox B,  French JLH,  Grant C,  Gupta A,  Harrop-Griffiths W,  Haslam N,  Higham H,  Hogg R,  Johnston DF,  Kearns RJ,  Kopp S,  Lobo C,  Lobo C,  McKinlay S,  Memtsoudis S,  Merjavy P,  Moka E,  Narayanan M,  Narouze S,  Noble JA,  Phillips D,  Rosenblatt M,  Sadler A,  Sebastian MP,  Taylor A,  Thottungal A,  Valdés-Vilches LF,  Volk T,  West S,  Wolmarans M,  Womack J,  Macfarlane AJR,  et al. (2022)
Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia.
Bowness JS,  El-Boghdadly K,  Woodworth G,  Noble JA,  Higham H,  Burckett-St Laurent D,  et al. (2022)
Scene context-aware salient object detection
Siris A,  Jiao J,  Tam GKL,  Xie X,  Lau RWH,  et al. (2022)