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About the authors

• Tom Schraepen

  Tom worked at Bruegel as a Future of Work and Inclusive Growth Consultant until May 2023. He obtained his BSc in Business Engineering and his MSc in Applied Economics from KU Leuven. He wrote his master’s thesis in the field of innovation economics.

  At Bruegel, he mainly works on the Future of Work and Inclusive Growth project. He researches the twin transition, the digital economy and labour market inequality. 

  Tom is a Belgian citizen. He is fluent in Dutch and English, and advanced in French.

• Laura Nurski

  Laura Nurski was a non-resident fellow at Bruegel until 2024. She is a Research Expert at the Centre of Expertise for Labour Market Monitoring at the Faculty of Business and Economics of KU Leuven. She leads the development of an integrated labour market prediction model that identifies future skill needs in the Flemish labour market.

  While residing at Bruegel in the past, she led the Future of Work and Inclusive Growth project which analyses the impact of technology on the nature, quantity and quality of work, welfare systems and inclusive growth.

  Before joining Bruegel, she investigated the impact of job design and organisation design on wellbeing and productivity at work. This inherently multidisciplinary domain has left her with a broad social science background, encompassing psychology, sociology and economics. As a former data scientist in the financial and retail sector, Laura is passionate about data and technology. She is also a skilled statistical programmer, survey developer and open-source aficionado.

  Laura holds a Ph.D. in Industrial Organization, a M.Sc. in Economics and a M.A. in Business Engineering from KU Leuven.

• Michal Krystyanczuk

  Michal is an experienced Data Scientist whose goal is to enable the use of Artificial Intelligence to make an impact on society.

  Michal has been regularly acting as a consultant on multiple AI-related projects for companies from different sectors: pharmaceuticals, marketing, and finance. He is specialized in Deep Learning and Big Data techniques for various AI tasks such as natural language processing, pattern recognition, recommender systems, credit scoring, or hedging strategies optimization. He managed numerous semantic data projects for global brands such as Mulberry, BNP Paribas, Groupe SEB, Publicis, or Abbott.

  Personally, Michal is an enthusiast of Cognitive Computing and Information Retrieval from unstructured data (text, image, and video).