Publications

My research focus is to help data scientists understand the models they build through visualization and eXplainable AI techniques.

Highlights

A few blog posts highlighting some of my research. You can also check out explaining.ml for more details.

Academic
StrategyAtlas
Explore
Academic
Contribution-Value Plots
Explore
Academic
ExplainExplore
Explore
Academic
Achmea fraud detection
Explore

Papers

A complete list of my academic output, including journal articles, conference papers, workshop contributions, and theses.

2026

  1. Evaluating the utility of feature importance visualizations in SHAP Best Paper
    D. Collaris, Y. Liang, M. C. Willemsen, A. Chatzimparmpas, J. J. van Wijk
    Information Visualization, 2026

2025

  1. Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task
    Y. Liang, D. Collaris, M. C. Willemsen, J. J. van Wijk
    30th International Conference on Intelligent User Interfaces (IUI), 2025
  2. Cluster-Based Random Forest Visualization and Interpretation
    M. Sondag, C. Meinecke, D. Collaris, T. von Landesberger, S. van den Elzen
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 2025
  3. LangLasso: Interactive Cluster Descriptions through LLM Explanation
    R. Buchmüller, D. Collaris, L. Meng, A. Chatzimparmpas
    1st Workshop on GenAI, Agents, and the Future of VIS, 2025

2024

  1. RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance
    D. Collaris, M. Pechenizkiy, J. J. van Wijk
    Dagstuhl Open Access Series in Informatics (OASIcs), 2024

2023

  1. LEMON: Alternative sampling for more faithful explanation through local surrogate models Frontier Prize
    D. Collaris, P. Gajane, J. Jorritsma, J. J. van Wijk, M. Pechenizkiy
    International Symposium on Intelligent Data Analysis (IDA), 2023

2022

  1. Characterizing data scientists’ mental models of local feature importance
    D. Collaris, H. J. P. Weerts, D. Miedema, J. J. van Wijk, M. Pechenizkiy
    Nordic Human-Computer Interaction Conference (NordiCHI), 2022
  2. Interactive Visualization for Interpretable Machine Learning Grant
    D. Collaris
    PhD Thesis, Eindhoven University of Technology, 2022
  3. StrategyAtlas: Strategy Analysis for Machine Learning Interpretability
    D. Collaris, J. J. van Wijk
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022

2021

  1. Comparative evaluation of contribution-value plots for machine learning understanding
    D. Collaris, J. J. van Wijk
    Journal of Visualization, 2021

2020

  1. ExplainExplore: Visual Exploration of Machine Learning Explanations Poster Prize
    D. Collaris, J. J. van Wijk
    IEEE Pacific Visualization Symposium (PacificVis), 2020
  2. Machine learning interpretability through contribution-value plots Best Short Paper
    D. Collaris, J. J. van Wijk
    International Symposium on Visual Information Communication and Interaction (VINCI), 2020

2018

  1. Instance-Level Explanations for Fraud Detection: A Case Study
    D. Collaris, L. M. Vink, J. J. van Wijk
    ICML Workshop on Human Interpretability in Machine Learning (WHI), 2018
  2. Instance-level Decision Visualization of Random Forest Models
    D. Collaris
    MSc Thesis, Eindhoven University of Technology, 2018

Awards

Recognition for my research and the projects I've contributed to.