Scholar
Julia Camps
Google Scholar ID: W6gIwugAAAAJ
Universitat Pompeu Fabra
Citations & Impact
Citations
823
H-index
11
i10-index
15
Publications
20
Co-authors
24
list available
Publications
Resume (English only)
Academic Achievements
- - Lockey Fund Travel Award (2023)
- - EPSRC Doctoral Prize (2021)
- - The Rosanna Degani Young Investigator Award semi-finalist at Computing in Cardiology (2021, 2018)
- - The University of Oxford DPhil Scatcherd European Scholarship (2017)
- - Prize for the best MSc Thesis in AI, the Catalan Association for AI (2017)
- - Best academic record in the BSc in Computer Science, Universitat Politècnica de Catalunya (2014)
- - Invited plenary tutorial at the 18th International Work-Conference on Artificial Neural Networks (2025)
- - Invited lecture at the Summer school in Reduced-order and Data-driven models in Biomechanics (2025)
- - Invited plenary talk at the International Conference on Complex Acute Illness (2023)
Research Experience
- - La Caixa Postdoctoral Junior Leader Fellow, Universitat Pompeu Fabra (UPF) (2025-Present), researching how digital twins can improve the prediction of arrhythmic risk in heart attack survivors
- - Senior research associate, University of Oxford (2022-2025), leading the development of a scalable and efficient digital twinning pipeline aimed at enabling virtual clinical trials
- - Junior research associate, University of Oxford (2021-2022)
- - Visiting researcher at the University of Oxford (2025-Present)
- - Lecturer of Artificial Intelligence at Universitat Politècnica de Catalunya (2025)
- - MSc admissions interviewer at the University of Oxford (2023)
- - Demonstrator of Machine Learning at the University of Oxford (2018)
Education
- - DPhil in Computer Science, University of Oxford (2021), Supervisor: Prof. Blanca Rodriguez
- - MSc in Artificial Intelligence, Universitat Politècnica de Catalunya (2017)
- - BSc in Computer Science, Universitat Politècnica de Catalunya (2014)
Background
- Research Interests: Combining data-driven methods and physics-based models to better understand and simulate the human heart. Research focuses on developing machine learning and inference techniques to build cardiac digital twins—personalised, virtual replicas of a patient’s heart that integrate clinical data with mechanistic simulations. These models support precision cardiology by helping to predict disease progression and guide treatment decisions. Ultimately, my goal is to enable virtual clinical trials at scale and improve how we diagnose, monitor, and treat heart conditions on an individual level.
Miscellany
- Currently a member of the Physense group at UPF, led by Prof. Oscar Camara, and collaborates closely with cardiologists at Centro Médico Teknon to address clinical questions through personalised cardiac modelling.
