Department of Radiology, University of Cambridge School of Clinical Medicine.
Email: les44@cam.ac.uk Tel.: + 44 01223 (3) 36893
Profile
Dr Lorena Escudero Sánchez is an interdisciplinary researcher, specialised in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Data Science, Image Analysis and software development, applying the scientific vision and experience gained as a Particle Physicist to develop advanced cancer imaging techniques that will make an impact on society.
Lorena is a Turing Fellow of The Alan Turing Institute, a Borysiewicz Interdisciplinary Fellow of the University of Cambridge, and a Rokos PDRA of Queens’ College Cambridge.
She graduated from the University of Salamanca with a degree in Physics and a Master in Cosmology and Particle Physics. After a short time working at CERN, she then moved to Valencia with an FPU fellowship to complete her PhD in Neutrino Physics, working on the T2K experiment in Japan[1,2,3]. In 2016 she moved to Cambridge and worked as a Research Associate at the Cavendish Laboratory, working mainly on pattern-recognition algorithms and related software development[4, 5]. She has been awarded numerous scholarships and awards, including the 2016 Breakthrough Prize in Fundamental Physics (as part of the T2K collaboration)[6] and the PhD Award of Excellence by the University of Valencia.
Lorena has led teams within worldwide collaborations, e.g. as a convenor of the Simulation and Reconstruction working group of the DUNE experiment[7] (1000+ members), and managed international initiatives such as the “UK-Latin American Neutrino Initiative” (2018-2019)[8].
She now leads the Radiogenomics and Quantitative Image Analysis group at the Department of Radiology, University of Cambridge.
Current Research
Her research focuses on developing novel AI methods for advanced analysis of radiological images for cancer research. She is especially interested in understanding the robustness and generalisability of image-based biomarkers such as Radiomic features[9], as well as novel methods for tumour segmentations and analysis.
Lorena is also part of the NCITA[10] repository team, and her role is to build and manage a local imaging repository in Cambridge.
Professional Education
| Degree | Awarding Institution | Field of Study |
| Bachelor | Universidad de Salamanca | Theoretical Physics |
| MSc | Universidad de Salamanca | Cosmology and Particle Physics |
| PhD CSIC + | Universidad de Valencia | Neutrino Physics |
Selected Publications and References
- K. Abe et al. (The T2K Collaboration), Measurements of neutrino oscillation in appearance and disappearance channels by the T2K experiment with 6.6E20 protons on target, Physical Review D 91, 072010 arXiv:1502.01550 [hep-ex]
- L. Escudero (for the T2K Collaboration), Initial Probe of δCP by the T2K Experiment with νμ Disappearance and νe Appearance, Nuclear Physics B (Proceedings for ICHEP 2014)
- L. Escudero (for the T2K Collaboration), Joint νμ Disappearance and νe Appearance Analysis at the T2K Experiment using a Frequentist Approach, Nuclear Physics B (Proceedings for ICHEP 2014)
- MicroBooNE collaboration, The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector. Eur.Phys.J. C78 (2018) no.1, 82
- https://github.com/PandoraPFA
- K. Abe et al. (The T2K Collaboration), Observation of Electron Neutrino Appearance in a Muon Neutrino Beam, Physical Review Letters 112, 061802 (2014) arXiv:1311.4750 [hep-ex]
- B. Abi et al. (The DUNE Collaboration), Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II DUNE Physics, (2020) arXiv:2002.03005 [hep-ex]
- First UK-Latin America Workshop on Advanced Computing and Deep Learning https://indico.hep.manchester.ac.uk/conferenceDisplay.py?confId=5346
- L Escudero Sanchez et al. Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle, Scientific Reports, 2021, 11, 1-15
- https://ncita.org.uk
GoogleScholar: https://scholar.google.com/citations?user=MdLoWrkAAAAJ&hl=en
ORCID ID: 0000-0003-3464-9206
Scopus Author ID: 37074354600
Researcher ID: G-2435-2018
