Judith Sáinz-Pardo Díaz

Data Science Researcher

Judith Sáinz-Pardo Díaz

About me

I hold a degree in Mathematics with a major in Computer Science from the University of Cantabria (UC), which I completed in 2020. During my undergraduate studies, I was awarded with a scholarship to pursue a Master’s in Entrepreneurship at the UC-Centro Internacional de Santander Emprendimiento. This opportunity allowed me to gain cross-disciplinary and soft skills in addition to the technical and analytical competencies I developed during the Mathematics degree.

Following this, I completed the Master’s in Data Science from the Menéndez Pelayo International University (UIMP) and the University of Cantabria (UC). Since completing this Master’s program, I have been working as a Data Science Researcher at the Advanced Computing and e-Science Group at the Institute of Physics of Cantabria, affiliated with the Spanish National Research Council (CSIC).

In September 2022 I joined the PhD program in Science and Technology at the University of Cantabria, to develop my PhD thesis on privacy preserving techniques in data science environments.

I’m currently working on different European projects focused on the development of advanced AI techniques, such as the AI4EOSC project, and also on the EOSC SIESTA project which aims to provide a set of tools for the effective sharing, management and analysis of sensitive data.

Research interests

  • Federated learning and others privacy preserving machine learning techniques.
  • Data privacy: anonymization, pseudonimization, differential privacy.
  • Privacy enhancing technologies.
  • Application of federated learning to different use cases: medical imaging, water quality, etc.
  • Artificial intelligence: machine learning and deep learning.

Short academic bio

  • (September 2024 - December 2024) Visiting Researcher at INRIA Paris Saclay (Campus de l’École Polytechnique). Project team: COMETE (Privacy, Fairness and Robustness in Information Management). Supervisor: Catuscia Palamidessi, PhD.
  • (December 2022 - present) PhD in Science and Technology. University of Cantabria. “Privacy Preserving Techniques for Data Science Environments”. Supervisor: Álvaro López García, PhD.
  • (July 2021 - present) Data Science Researcher at the Institute of Physics of Cantabria (Spanish National Research Council, CSIC). Working on different projects, mainly: FACE, AI4EOSC, EOSC SIESTA.
  • (September 2020 - June 2021) Interuniversity Master in Data Science. International University Menéndez Pelayo (UIMP) and University of Cantabria (UC). Final master thesis: “Predictive Maintenance and Spectral Analysis: from Fourier to Machine Learning” (cum laude), available here.
  • (October 2017 - September 2019) Master in Entrepreneurship. University of Cantabria and CISE (Santander International Center for Entrepreneurship) with the support of Santander Universities.
  • (September 2016 - June 2020) Degree in mathematics with a major in computer science. University of Cantabria. Final degree project: “Optimization Problems Associated with Differential Equation Models for Chemotherapy” (cum laude), available here.

Publications

  • (2024) Sáinz-Pardo Díaz, J., & López García, Á. (2024). An Open Source Python Library for Anonymizing Sensitive Data. Sci Data 11, 1289. https://doi.org/10.1038/s41597-024-04019-z}.
  • (2024) Sáinz-Pardo Díaz, J., Castrillo, M., Bartok, J., Heredia Cachá, I., Malkin Ondík, I., Martynovskyi, I., Alibabaei, K., Berberi, L., Kozlov, V. & López García, Á. (2024). Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas. Earth Science Informatics. https://doi.org/10.1007/s12145-024-01438-9.
  • (2024) Sáinz-Pardo Díaz, J., Heredia Canales, A., Heredia Cachá, I., Tran, V., Nguyen, G., Alibabaei, K., Obregón Ruiz, M., Rebolledo Ruiz, S., & López García, Á. Making Federated Learning Accessible to Scientists: The AI4EOSC Approach. In Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec ‘24). Association for Computing Machinery, New York, NY, USA, 253–264. https://doi.org/10.1145/3658664.3659642.
  • (2023) Sáinz-Pardo Díaz, J., Castrillo, M., & López García, Á. (2023). Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data. Water Research, 120726. https://doi.org/10.1016/j.watres.2023.120726.
  • (2023) Sáinz-Pardo Díaz, J., & López García, Á. (2023). Comparison of machine learning models applied on anonymized data with different techniques, 2023 IEEE International Conference on Cyber Security and Resilience (CSR), Venice, Italy, 2023, pp. 618-623. https://doi.org/10.1109/CSR57506.2023.10224917.
  • (2023) Heredia Cacha, I., Sáinz-Pardo Díaz, J., Castrillo, M., & López García, Á. (2023). Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain’s case study. Scientific Reports, 13(1), 6750. https://doi.org/10.1038/s41598-023-33795-8.
  • (2023) Sáinz-Pardo Díaz, J., & López García, Á. (2023). Study of the performance and scalability of federated learning for medical imaging with intermittent clients. Neurocomputing, 518, 142-154. https://doi.org/10.1016/j.neucom.2022.11.011.
  • (2022) Sáinz-Pardo Díaz, J., & López García, Á. (2022). A Python library to check the level of anonymity of a dataset. Scientific Data, 9(1), 785. https://doi.org/10.1038/s41597-022-01894-2.
  • (2022) Fernández, L. A., Pola, C., & Sáinz-Pardo, J. (2022). A mathematical justification for metronomic chemotherapy in oncology. Mathematical Modelling of Natural Phenomena, 17, 12. https://doi.org/10.1051/mmnp/2022010.