👩🏽‍💻 PhD Candidate in ML applied to Computational Biology   |   🎓Graduated in Physics

🌐 Check out my webpage for more details about my work!

• Benchmark of inductive and transductive node embedding models for drug–target interaction prediction.
• Generalization studies that revealed data leakage issues of transductive models that employed node2vec.
• Design of a novel (biologically-drive) negative subsampling technique.
• This work was published at Nature Machine Intelligence ’25. And Oral at NeurIPS ’23 Workshop.

• Ultrafast GNN-based method.
• Better performance and several orders of magnitude faster than state-of-the-art models.
• Strong generalization across different dataset sizes.
• Preserves biological information in node embeddings.
• This work was published in Bioinformatics '24.

• Programming Languages: Python, R, bash, latex.
• ML/DL Frameworks: PyTorch, PyTorch Geometric, scikit-learn
• Chemical informatics: RDKit, ChEMBL, PubChem API
• Others: Git, Docker, HPC.