Lars Henry Berge Olsen

PhD student, The Alan Turing Institute Enrichment Scheme and the University of Oslo

Lars is a third-year Data Science Ph.D. student at the University of Oslo. His research aims to improve the Shapley value methodology for explaining machine learning models. Before his Ph.D., Lars completed his Master’s degree in Data Science and Bachelor’s degree in applied mathematics at the same university.

Lars’ research aims to make the decision-making process of complex machine learning methods more transparent by using explainable artificial intelligence methods, especially Shapley value explanations. He has researched methodological improvements to Shapley values, as accurate explanations are crucial. Lars continues this mission at The Alan Turing Institute and will seek collaborators to apply his improved methodologies to real-world applications. Lars hopes his work will allow us to gain knowledge from the black box models and achieve research breakthroughs.


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