Postdoctoral researcher in the Bioinformatics Unit at CNIO, I specialize in deep learning applied to biological signals. Holding a Bachelor’s degree in Life Sciences, a Master’s in Bioinformatics, and a PhD specialized in deep learning, my academic path reflects a constant drive to explore new fields and understand new challenges. I enjoy pushing myself out of my comfort zone whenever it offers intellectual growth. As part of my postdoctoral research at CNIO, I am now broadening my research scope to focus on predicting metastasis risk in PPGL cancers using multimodal approaches.
Publications:
- Lence, A., Extramiana, F., Fall, A., Salem, J. E., Zucker, J. D., & Prifti, E. (2023). Automatic digitization of paper electrocardiograms – A systematic review. Journal of Electrocardiology, 80, 125-132.
- Lence, A., Granese, F., Fall, A., Hanczar, B., Salem, J. E., Zucker, J. D., & Prifti, E. (2025, July). ECGrecover: a deep learning approach for electrocardiogram signal completion. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V. 1 (pp. 2359-2370).
- Lence, A., Fall, A., Cohen, S. D., Granese, F., Zucker, J. D., Salem, J. E., & Prifti, E. (2026). ECGtizer: An open-source, fully automated pipeline for digitization and signal recovery from paper electrocardiograms. Biomedical Signal Processing and Control, 112, 108710.
Conferences:
- Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM 2023), France
- Applied Data Science – ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), 2025
Teaching:
- Data Mining, Université Paris Dauphine-PSL, Paris, France — September 2024 – December 2024
- Programming Elements 1 – Teaching Assistant, Sorbonne Université, Paris, France — September 2022 – January 2023
