Dr Madeline Robles

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  • Qualifications:PhD in Forensic Science and Forensic Anthropology (UCL, 2021); MRes Security Science (UCL, 2017); MSc Forensic Anthropology (University of Edinburgh, 2015); Associate Member of the Chartered Society of Forensic Sciences (ACSFS)
  • Position:Lecturer in Forensic Science
  • Department:Faculty of Health and Applied Sciences (HAS)
  • Telephone:+4411732 81138
  • Email:Madeline.Robles@uwe.ac.uk
  • Social media: LinkedIn logo Twitter logo

About me

Upon the successful completion of my doctoral research at UCL, I have established a number of national and international collaborations across UCL, the NHS and with institutions in the U.S. I have directed and collaborated on major interdisciplinary forensic anthropology research studies with colleagues across UCL including the Institute of Archaeology, which have resulted in several presentations at major international conferences and first author publications in international peer-reviewed journals. I also hold active membership with several relevant professional bodies, including an associate membership for the Chartered Society of Forensic Sciences as well as the British Association of Biological Anthropology and Osteoarchaeology and British Association for Forensic Anthropology (BAFA).

Area of expertise

My research focuses on the application of 3D imaging and virtual environments in not only forensic anthropology but in forensic science more broadly.
 My latest research project focused on developing a new approach to human identification in forensic reconstruction investigations using 1,500 three-dimensional (3D) models of the paranasal sinuses. Understanding biological patterns using linear measurements and geometric morphometrics on the paranasal sinuses in relation to age, sex, and ancestry may assist with identification in unknown human remains when other methods of identification (DNA or fingerprint comparison) are not possible. This project produced complex statistical modelling using discriminant function analysis and was the first to develop an identification method based on a modern U.K. population. It was also the first to establish an automatic approach to producing 3D models of the sinuses in an accurate, replicable and time efficient manner using free and open software. 


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