Prof. Dr. Yves Staudt
PhD in Actuarial Science
Lecturer
Institute for Data Analysis, Artificial Intelligence, Visualization, and Simulation (DAViS)
Telefono
Specializzazione
Data Science, Tourism 4.0, statistics, Machine Learning, Artificial Intelligence, Internet of Things, Deep learning, Data Analytics, data visualisation, Insurance Mathematics
Attivo/a nei corsi di studio
Digital Business Management, EMBA Digital Technology and Operations, Computational and Data Science, MSc Data Visualization, Artificial Intelligence in Software Engineering, Information Science
Prof. Dr. Yves Staudt is Professor of Applied Data Science at the Institute for Data Analysis, Artificial Intelligence, Visualization and Simulation (DAViS) at FHGR. He holds a PhD in actuarial science and combines a solid statistical foundation with applied machine learning and deep learning.
His research focuses on areas where data leads to better decisions: healthcare (patient pathways, digitising oncology guidelines, clinical NLP, computer vision in cardiology), insurance (ratemaking, price and claims modelling) and tourism (data-driven pricing, simulating visitor flows). His methodological interests include explainable AI (XAI), data-centric machine learning and the use of large language models. Design thinking, customer integration and customer relationship management (CRM) play an important role in his work.
He teaches machine learning and deep learning at Bachelor, Master and Executive MBA level. He uses an inverted classroom approach and sketchnotes, and combines design thinking with data science so that students apply methods to real problems. Originally from Luxembourg, he works in German, French, English and Luxembourgish. In his spare time he enjoys being out in nature and taking photographs.
Curriculum vitae
Prof. Dr. Yves Staudt is Professor of Applied Data Science at the Institute for Data Analysis, Artificial Intelligence, Visualization and Simulation (DAViS) at FHGR. He holds a PhD in actuarial science and combines a solid statistical foundation with applied machine learning and deep learning.
His research focuses on areas where data leads to better decisions: healthcare (patient pathways, digitising oncology guidelines, clinical NLP, computer vision in cardiology), insurance (ratemaking, price and claims modelling) and tourism (data-driven pricing, simulating visitor flows). His methodological interests include explainable AI (XAI), data-centric machine learning and the use of large language models. Design thinking, customer integration and customer relationship management (CRM) play an important role in his work.
He teaches machine learning and deep learning at Bachelor, Master and Executive MBA level. He uses an inverted classroom approach and sketchnotes, and combines design thinking with data science so that students apply methods to real problems. Originally from Luxembourg, he works in German, French, English and Luxembourgish. In his spare time he enjoys being out in nature and taking photographs.
