José R. Pareja Monturiol
I studied Mathematics (2016–2020) at Universidad Complutense de Madrid (UCM), where I specialized in Computer Science during my final year, developing a strong interest in machine learning. During this time, I worked at the Department of Statistics and Operations Research (UCM) under the supervision of María del Mar Fenoy Muñoz through a collaboration grant, conducting research on fractional Brownian motion and its applications to the modelling of cardiac diseases.
I then pursued an M.Sc. in Computational Statistics at UCM while working as a researcher at the Department of Mathematical Analysis and Applied Mathematics under the supervision of David Pérez García. During this period, I built a strong foundation in privacy-preserving machine learning and began learning about tensor networks, which shaped my subsequent research interests.
I later completed a PhD at the Instituto de Ciencias Matemáticas (ICMAT) as part of the Quantum Information Theory group (mathQI), supervised by David Pérez García and Alejandro Pozas Kerstjens. My research focused on the use of tensor network models for machine learning and their applications to privacy-preserving methods, while also exploring related topics such as trainability and implementation of tensor network models, as well as broader applications including model compressibility, interpretability, and robustness. During this period, I also spent time as a visiting researcher at the Perimeter Institute for Theoretical Physics, working with Roger Melko and the PIQuIL group.
I am currently a postdoctoral researcher at UCM, continuing to work at the intersection of machine learning and tensor networks, with a particular focus on tensorization / low-rank decompositions, as well as further developments in privacy-preserving and related machine learning methods.
I then pursued an M.Sc. in Computational Statistics at UCM while working as a researcher at the Department of Mathematical Analysis and Applied Mathematics under the supervision of David Pérez García. During this period, I built a strong foundation in privacy-preserving machine learning and began learning about tensor networks, which shaped my subsequent research interests.
I later completed a PhD at the Instituto de Ciencias Matemáticas (ICMAT) as part of the Quantum Information Theory group (mathQI), supervised by David Pérez García and Alejandro Pozas Kerstjens. My research focused on the use of tensor network models for machine learning and their applications to privacy-preserving methods, while also exploring related topics such as trainability and implementation of tensor network models, as well as broader applications including model compressibility, interpretability, and robustness. During this period, I also spent time as a visiting researcher at the Perimeter Institute for Theoretical Physics, working with Roger Melko and the PIQuIL group.
I am currently a postdoctoral researcher at UCM, continuing to work at the intersection of machine learning and tensor networks, with a particular focus on tensorization / low-rank decompositions, as well as further developments in privacy-preserving and related machine learning methods.