CV

Education

• B.S. in Liberal Arts and Sciences, Amsterdam University College, 2021
• M.S. in Artificial Intelligence, University of Amsterdam, 2023
• Ph.D in Safety in Conversational AI, University of Amsterdam, 2027 (expected)

Work experience

• Nov 2022 - May 2023: Teaching Assistent
  • University of Amsterdam
  • 3 first-year courses in the MSc AI program: Natural Language Processing 1, Knowledge Representation and Reasoning, and Automated Planning.
  • Duties included: Teaching tutorial sessions, assisting students with their assignments, and answering their questions. Grading of assignments and exams.
• Jan 2020 - Jun 2021: Data scientist Intern
  • Machine Learning Programs
  • Duties included: Writing literate programming documents analyzing data sets, and model performance. Incorporating domain knowledge on car insurance. Training machine learning models for premium prediction and propensity to claim. Supervising groups of undergraduates during their one month internship projects.

Publications

MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs

Neplenbroek, V., Bisazza, A. and Fernández, R., 2024. MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs. In the first Conference on Language Modeling (COLM) 2024.

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Bavaresco, A., Bernardi, R., Bertolazzi, L., Elliott, D., Fernández, R., Gatt, A., Ghaleb, E., Giulianelli, M., Hanna, M., Koller, A. and Martins, A.F., 2024. LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks. arXiv preprint arXiv:2406.18403.

[Re] Replication study of ‘Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling’

Neplenbroek, V., Perdijk, S., and Prins, V. 2022. [Re] Replication study of ’Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling.’ ReScience C 8, 2, #29.