Machine Learning Engineer

Led the CLARIFY project to Accenture's Greater than Award Finals in the Inspiring Growth category, managing technology transfer for a neuro-symbolic query system on biomedical knowledge graphs;

Developed systems for relapse prediction and completed the TechStar 2023 leadership program;

Created a prototype REST API for AmpliGraph 2, demonstrated in client workshops;

Contributed to the EU Commission's CLARIFY H2020 project by delivering a client pilot, collaborating with 11 partners, and authoring deliverables;

Proposed four patent ideas, serving as lead author for two;

Co-supervised a PhD intern on interpretable Gene-Disease Prediction with GraphML;

Served as Virtual Buddy for a new joiner and participated in interview panels;

Presented at conferences such as Sketching in Hardware 2022, ESSEC Business School, and EIT Digital Alumni Annual Meeting on XAI and knowledge graphs;

Co-presented the COLING-22 Tutorial on Knowledge Graph Embeddings for NLP and authored a Medium Labs blog post on XAIl;

Conducted machine learning research on explainable AI for knowledge graph embedding models in precision medicine oncology applications;

Developed ExamplE, a novel explanation approach for link prediction, leading to a patent application and a Proof of Concept deployed at Hospital Puerta del Hierro for the CLARIFY H2020 project;

Designed experiments for lung cancer relapse prediction and contributed to the development of AmpliGraph 1.4;

Co-authored three deliverables to the EU Commission and submitted three patent ideas;

Led a human-based evaluation for a consumer goods project, resulting in client presentations and a conference paper;

Achieved runner-up status in the Accenture Hackathon: Al4Insurance and participated in the Eco Innovation Challenge;

Collaborated with the Human Insight Lab on various initiatives.

Began as a Working Student and received promotions, changing roles while working in a team responsible for developing a component test framework for 5G components (R&D Python Software Developer);

Collaborated on time synchronization in Base Transceiver Stations, with main tasks including web application development with machine learning support in Python, hosted in the cloud (R&D Embedded Software Engineer);

Contributed by writing new features in C++, fixing software bugs, unit-testing, documenting, and employing best practices of Object-Oriented Programming (OOP) and Test-Driven Development (TDD).