About me
I am a PhD candidate at University of Amsterdam, focusing on applying topology, algebra, and geometry in machine learning.
Interests
- Representation Learning
- Geometric Deep Learing
- Topological Machine Learning
- Non-Euclidean Geometry
Education
MSc in Machine Learning, 2023
KTH Royal Institute of Technology
BSc in Mathematics and Computing, 2021
Universidad Politecnica de Madrid
![Robustness of persistent homology: the Stability Theorem [in Spanish]](/publication/upm-68578/featured_huf21bda10eaf3d7e5dc5a49a9034381a3_662679_2bbc1f8cd011da490a4b2c36464552c8.webp)
Timeline
PhD Candidate
AMLab @ UvA
Under the supervision of Erik Bekkers (University of Amsterdam) and co-supervision of Daniël Pelt (University of Leiden), we will develop techniques for collaborative human-computer image annotation of training sets for deep learning tasks. These techniques will suggest relevant annotations to the human annotator, will indicate inconsistencies in the human annotations, and will use concepts from geometric deep learning to handle shapes of image annotations.
Research Engineer
Division of Robotics, Perception and Learning @ KTH
Projects under the supervision of Danica Kragic:
- I delved into Geometric Deep Learning and Lie groups while working on a project that involved devising path-finding algorithms on learned equivariant representations through class-pose decomposition.
- I also explored Manifold Learning techniques, employing probabilistic models to understand data shapes, following the methodology of Georgios Arvanitidis (2021).
- Lastly, I worked on a project that proposes a method to recover the underlying hierarchies within hyperbolic embeddings.
Trainee Internship
Medical Data Analytics Laboratory @ UPM
Projects under the supervision of Ernestina Menasalvas Ruiz:
- Application of information recognition algorithms in clinical notes.
- Development of new techniques for the recognition of metrics, doses and numbers.
- Fine-tuning a BERT model in breast and lung cancer’s clinical notes.