This winter, the Big Statistics group of the Epidemiology and Data Science department, Amsterdam University Medical Centers, will host a single-day workshop on explainable machine learning (xML), featuring renowned international speakers.

Date: February 4th 2026
Time: 9:30 – 16:30 (CEST)
Location: IJ-zaal of RDC ADORE building, Van der Boechorststraat 6B, 1007 MB Amsterdam
Registration deadline: January 15th 2026
Maximum capacity: 60 people
Cost: free
Description:
Complex machine learning models are increasingly applied to medical data because of their strong predictive performance and ability to capture interactions and nonlinear relationships. However, their complexity makes it difficult to gain insight in the application at hand. Explainable machine learning (xML) provides a framework of techniques that help researchers (1) understand how models arrive at their predictions and (2) identify which variables are important.

In health care, explainability is particularly relevant. Insight into why predictions are made may generate new biomedical understanding and may ultimately support clinical decision-making. However, the typically small sample sizes in medical studies pose challenges for both model fitting and interpretation.

This workshop is intended to researchers with a methodological/statistical background who are interested in applying state-of-the-art explainable ML methods to health-related data. The program features a mix of theory and applications, with a particular focus on Shapley values and their recent methodological developments. Other xML approaches will also be discussed.

Provisional programme:

09.15h – 09.30h Coffee + Welcome
09.30h – 10.05h Marjolein Fokkema, Leiden University
10.05h – 10.30h Jeroen Goedhart, Amsterdam UMC
10.30h – 10.55h Angel Reyero Lobo, Institut de Mathématiques de Toulouse
10.55h – 11.15h Break
11.15h – 11.50h Ioan Gabriel Bucur, Radboud University
11.50h – 12.25h Konstantinos Sechidis, Novartis
12.25h – 13.40h Lunch (provided)
13.40h – 14.30h Marvin Wright + Sophie Langbein, Leibniz Institute for Prevention Research and Epidemiology – BIPS
14.30h – 15.15h Martin Jullum, Norwegian Computing Center (Software demonstration for Shapley values)
15.15h – 15.35h Break
15.35h – 16.10h Giovanni Cinà, Amsterdam UMC
16.10h – 16.45h Kjersti Aas, Norwegian Computing Center

Specific topics include:
– Shapley values for causal interpretations
– xML methods for tree-based learners and survival analysis
– Computational aspects of Shapley values
– Applications in omics

Organizing Committee: Mark van de Wiel [mark.vdwiel@amsterdamumc.nl], Giorgio Spadaccini [g.spadaccini@amsterdamumc.nl], and Jeroen Goedhart [j.m.goedhart@amsterdamumc.nl]

Sponsors:

-Amsterdam Public Health research institute (APH)
-VVSOR Biometrics / IBS Dutch region (BMS-ANed)
-Dep. Epidemiology & Data Science of Amsterdam UMC

Registration: