CV

Education and research experience

• (September 2024 - present) 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 Menendez 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 in peer reviewed high impact journals

• (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.
• (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) 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.

Publications in high impact peer reviewed international conferences:

• (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., & 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.

Attendance to conferences and workshops:

• (2024) Data in research: challenges and opportunities. Summer courses International University Menéndez Pelayo (UIMP). Spanish. Curso: Los datos en investigación: retos y oportunidades. Cursos de verano de la Universidad internacional Men´endez Pelayo (UIMP). August 26-28, 2024. Santander, Spain. Talk (1/2): “Distributed Learning: advantages and risks”. (Spanish - “Aprendizaje distribuido: ventajas y peligros”). Talk (2/2): “Practical session on federated learning”. (Spanish - “Sesión práctica sobre federated learning”).
• (2024) 12th ACM Workshop on Information Hiding and Multimedia Security. Special Session: Security and Privacy Challenges towards Trustworthy Federated Learning. June 24-26, 2024. Baiona, Spain. Presentation of the paper: “Making Federated Learning Accessible to Scientists: The AI4EOSC Approach”.
• (2024) Zero-code tools BMZ Community partners – Workshop (organized by the AI4Life project). June 10-11, 2024. Madrid, Spain. Talk: “Advanced AI for scientists: the AI4EOSC platform approach” (given jointly with Ignacio Heredia).
• (2024) Flower AI Summit 2024. March 14-15, 2024. London, United Kingdom. Talk: “Federated AI in the European Open Science Cloud”.
• (2024) EOSC SIESTA project Kick off Meeting. January 25-26. León, Spain.
• (2023) AI4EOSC users workshop. November 15-16, 2023. Bratislava, Slovakia. Talk: “Federated Learning with Flower”.
• (2023) IEEE International Conference on Cyber Security and Resilience 2023. July 31 - August 2, 2023. Venice, Italy. Presentation of the paper: “Comparison of machine learning models applied on anonymized data with different techniques”.
• (2023) AIHUB CSIC Summer School 2023. July 4-8, 2023. Barcelona, Spain.
• (2023) X Jornadas Doctorales G-9, University of Oviedo. May 31 - June 2, 2023. Oviedo, Spain.
• (2022) AI4EOSC project Kick off Meeting. October 5-6, 2022. Santander, Spain.
• (2022) 2nd Inria-DFKI European Summer School on Artificial Intelligence 2022 (IDESSAI 2022). August 29 - September 2, 2022. Saarbrücken, Germany.
• (2022) AIHUB CSIC Summer School 2022. July 4-8, 2023. Palma de Mallorca, Spain.

Online talks and seminars given:

• (2024) Joint Workshop PTI Digital Science - PTI Green Horizon. September 17, 2024. Title: “AI and development of AI-based solutions” (Spanish - “IA y desarrollo de soluciones basadas en IA”).
• (2024) AI4EOSC webinars (3): introduction to federated learning. April 22, 2024. Title: “Demo: FL in AI4EOSC”.
• (2024) Talk given to the MONAI FL working group. April 10, 2014. Title: ‘Federated Learning within the AI4EOSC platform”.
• (2024) Flower monthly January 2024. January 3, 2024. Title: “Federated Learning with Flower in the European Open Science Cloud”. Talk given joinly with Álvaro López García.

Scientific posters

• (2023) Sáinz-Pardo Díaz, J. López García, Á. (2023). Title: “Privacy preserving techniques for data science”. X jornadas doctorales y V jornadas de divulgación grupo de Universidades del G9. University of Oviedo. May 31 - June 2, 2023. Oviedo, Spain.
• (2023) Sáinz-Pardo Díaz, J. López García, Á. (2023). Title: “Application of federated learning to medical imaging scenarios”. AIHUB CSIC Summer School. CaixaForum Macaya, Barcelona, Spain. July 4-8, 2023.
• (2022) Sáinz-Pardo Díaz, J. López García, Á. (2022). Title: “pyCANON: A Python library to check the level of anonymity of a dataset”. 2nd Inria-DFKI European Summer School on Artificial Intelligence 2022 (IDESSAI 2022). Saarbrücken, Germany. August 29 - September 2, 2022.

Software

• (2024) Sáinz-Pardo Díaz, J., López García, Á. (2024). ANJANA. DOI: https://zenodo.org/records/11186382.
• (2024) Sáinz-Pardo Díaz, J., López García, Á. (2022). pyCANON. DOI: https://doi.org/10.20350/digitalCSIC/15280.

Teaching:

• (2024) Teaching: Master in Data Science. International University Menéndez Pelayo (UIMP) and University of Cantabria (UC). Subject: Security, privacy and legal aspects. 7 sessions (2 hours each).
• (2024) Final master thesis supervision. Title: “Comparison of distributed machine learning techniques applied to openly available medical data”. Author: Marco Antonio Melgarejo Aragón. Directors: Judith Sáinz-Pardo Díaz and Álvaro López García.
• (2023) Final master thesis supervision. Title: “Building a Python library for anonymizing sensitive data”. Author: Esmeralda Madrazo Quintana. Directors: Álvaro López García and Judith Sáinz-Pardo Díaz.
• (2023) Final master thesis supervision. Title: “Analyzing the performance of machine learning models on anonymized data”. Author: Carmen Marcos Sánchez de la Blanca. Directors: Judith Sáinz-Pardo Díaz and Álvaro López García.
• (2023) Teaching: Master in Data Science. International University Menéndez Pelayo (UIMP) and University of Cantabria (UC). Subject: Security, privacy and legal aspects. 5 sessions (2 hours each).
• (2022) Teaching: Master in Data Science. International University Menéndez Pelayo (UIMP) and University of Cantabria (UC). Subject: Security, privacy and legal aspects. 4 sessions (2 hours each).