Lisette’s research interests lie at the intersection between computational social science, network science, and applied machine learning. Her research examines the extent to which the structure of networks biases the outcomes of network-based ranking and recommendation algorithms. One goal is to propose fairer outcomes with or without algorithm interventions. She will also use high resolution poverty maps using multimodal data (e.g., satellite images and mobility networks) in order to understand the limitations of data and propose interventions (e.g., when data is missing) to accurately predict wealth within and across countries.
Lisette did her PhD and was a research assistant at the Computational Social Science department at GESIS Leibniz Institute for the Social Sciences. For her doctoral thesis, she investigated the effects of network structure on ranking, network inference, and human navigation.She has been a PostDoc at the Complexity Science Hub from 2022 to 2026.
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