Theresia Veronika Rampisela

Theresia Veronika Rampisela is a PhD student and Guest Researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning. She actively contributes to the Algorithms, Data, and Democracy (ADD) project (https://algorithms.dk/), which investigates the societal implications of algorithmic decision-making systems.

Her academic background includes a Master's degree in Computer Science from the University of Indonesia, providing foundational expertise in computational methods and data analysis. This education has enabled her transition from early work in information retrieval to specialized research in algorithmic fairness.

Rampisela's research program critically examines fairness evaluation in machine learning applications, with particular focus on recommender systems. She investigates both group and individual fairness metrics, exploring their practical implementation challenges and limitations. Her work addresses the fundamental tension between recommendation relevance and equitable treatment of diverse user populations. Earlier research included academic expert finding using semantic techniques and medical applications involving schizophrenia classification with SVMs.

Analysis of her publication trajectory reveals progressive sophistication in algorithmic fairness research. Starting with foundational work on expert finding systems, she has advanced to developing novel frameworks like Pareto optimization for balancing competing objectives in recommendation algorithms. Her publications in top venues including ACM Transactions on Recommender Systems and the ACM SIGIR Conference demonstrate growing recognition in this critical field.

• Mensa International Scholarship (2024)

Rampisela collaborates extensively with Maria Maistro, Tuukka Ruotsalo, and Christina Lioma through the ADD project. Her research has significant practical implications for technology companies developing recommendation systems and policymakers regulating algorithmic fairness. She maintains an active scholarly profile documented through her ORCID (https://orcid.org/0000-0003-1233-7690) and demonstrates growing influence with mentions across academic social platforms.