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E-mail: r.m.schouten@tue.nl

Please contact me for my research and teaching statements.


Introduction

Methods: Exceptional Model Mining | Local Pattern Mining | Subgroup Discovery

Type of Data: Non-IID | Hierarchical | Sequential | Time Series | Longitudinal

Application domains: Personalized Learning Analytics | e-Health | Medicine

My research revolves around developing local pattern mining methods for extracting societal relevant, interpretable patterns from non-IID data, such as discovering subgroups of patients with exceptional responses to treatment.My next steps include developing extensions for multi-modal data, for online learning and for integrating domain knowledge and user feedback.


Affiliations

See here for complete Curriculum Vitae.

2025 - present: Post-doc at TU/e.

2020 - 2024: Ph.D. candidate at TU/e.

Supervisors: Prof. Mykola Pechenizkiy, Dr. Wouter Duivesteijn.

Topic: Exceptional Model Mining for Hierarchical Data.

2017 - 2019: Missing Data Researcher at Utrecht University.

Supervisors: Prof. Stef van Buuren, Dr. Gerko Vink.

2016: Staff Associate of Professor Andrew Gelman at Columbia University in the City of New York, US. Duration: 2 months.

2015: Intern at SRON, Dutch Institute of Space Research, the Netherlands. Duration: 4 months.


Awards and recognition

  • Recognition as Excellent reviewer Research Track ECML PKDD 2024

  • Award for Excellent course evaluation Research Topics in Data Mining 2022/2023

  • Award for Excellent course evaluation Research Topics in Data Mining 2021/2022


Grants and funding

Year Call Type Title Together with
2024 Take-off Phase 1 NWO €40k Feasibility study Integration of Local Pattern Mining in Digital Assessment Tools Prof. Mykola Pechenizkiy
2024 ECML PKDD Proceedings Chair €500 reimbursement of entree ticket Proceedings Chair Dr. Wouter Duivesteijn
2022 AI for Health EWUU Alliance EUR 45k seed money Better Imputation by Generative Adversarial NeTworks (BIGANT) Prof. Stef van Buuren, Dr. Gerko Vink, Hanne Oberman, Prof. Mykola Pechenizkiy, Prof. Cassio de Campos, Rianne Schouten, Daniel Oberski, Dr. Thomas Debray, Prof. Fred van Eeuwijk

Research visits

Year Where? Who? Why? Duration
2025 Ghent University Prof. Tijl de Bie, Prof. Jefrey Lijfijt To collaborate on Pattern mining and fairness 1 day
2025 Bielefeld University Prof. Barbara Hammer To collaborate on Concept drift, XAI and User interaction 1 week
2024 Radboud University Nijmegen Dr. Marcos L.P. Bueno To provide a guest-lecture in the AI for Healthcare course 1 day
2016 Columbia University Prof. Andrew Gelman To collaborate on Evaluating missing data methods, together with Dr. Gerko Vink 2 months

Publications

See Google Scholar.


Domain collaborations

Year Organization Type of collaboration
2025-now AlgebraKit Answering domain questions using EMM
2022-now Turku Research Institute for Learning Analytics Answering domain questions using EMM
2022-now Dutch south west Psoriatic Arthritis Registry, Erasmus MC Solving MD problems + Answering domain questions using EMM
2020-now Biomedical Systems and Signals Group, Utwente + Hospital Twente Using
2021-2024 Netherlands Institute of Mental Health and Addiction Answering domain questions using EMM
2017-2019 University Medical Center Utrecht Statistics consultant

Teaching

At Eindhoven University of Technology, my teaching track record is:

Year Course Level Activities
24/25 Research Topics in Data Mining MSc Responsible for track: Empirical Challenges in Data Mining
22/23 Research Topics in Data Mining MSc Taught 2 lectures about Missing Data, supervised 4 groups of students during research project
21/22 Research Topics in Data Mining MSc Taught 2 lectures about Missing Data, supervised 7 groups of students during research project
20/21 Foundations of Data Mining MSc Taught 2 lectures about Missing Data, provided answers in weekly Q&A, developed practice questions for exam, administrative activities

“I took a lot of courses last year, but I like your instructions the most. It is not only because of your professional knowledge, but also because of your personality of being kind, patient, responsible.” (Jin Ouyang, Master student, 2022)

At Utrecht University, my teaching track record is:

Year Course Level Activities
2016 Survey Research: Statistical Analysis and Estimation Advanced MSc Organization,Tutoring exercise classes
2016 Survey Research: Design, Implementation and Data Processing Advanced MSc Organization,Tutoring exercise classes
2015 Survey Research: Statistical Analysis and Estimation Advanced MSc Organization,Tutoring exercise classes
2015 Survey Research: Design, Implementation and Data Processing Advanced MSc Organization,Tutoring exercise classes

“Rianne was a first class assistant at our summer school courses. Not only was all material prepared extremely punctual and without errors, she also got very high student evaluations. I can wholeheartedly recommend Rianne!” (Prof. dr. Edith de Leeuw, 2016)


Supervision

Current students:

Start Student Type Topic
May 2025 Abdullahi Farah MSc Thesis
Aug 2024 Lieke van den Biggelaar PhD Research Exceptional Model Mining with Time Series Data
Nov 2023 Emmanuel C. Chukwu PhD Research Counterfactual Explanations in Time Series Classification

Alumni:

Year Student Type Topic
24/25 Haoqi Guo MSc Thesis Improving Diversity and Feasibility of Counterfactual Explanations, supervision together with prof.dr. Mykola Pechenizkiy
23/24 Lieke van den Biggelaar MSc Thesis with Catharina Hospital Discovering subgroups of patients with exceptional Atrium Fibrillation based on ECGs, supervision together with dr. Wouter Duivesteijn
22/23 Victoria Tascau MSc Thesis with DEPAR/Erasmus Medical Centrum Handling Missing Values in Longitudinal Medical Data, supervision together with dr. Wouter Duivesteijn
21/22 Mika van Loon MSc Thesis Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining, supervision together with dr. Wouter Duivesteijn
21/22 Varun Kamat Internship at Signify Recommendation Tool for Component Database
21/22 Isabel van den Heuvel Research Proposal Equivalence Testing for Developing Fair Machine Learning Algorithms, supervision together with Hilde Weerts
21/22 Mats Verbraak Research Proposal Handling Missing Data in the Prediction Domain using Multiple Imputation
20/21 Bart van Dooren MSc Thesis with Philips Predicting Cardiovascular Risk with Objective Physical Activity Measurements, supervision together with prof.dr. Mykola Pechenizkiy

Community service

I reviewed for top-level data mining conferences and statistical journals (DAMI, ECML PKDD, EWAF, JRSSB, SiM, BimJ). I was recognized as an excellent reviewer at ECML PKDD 2024. I served as Proceedings Chair at ECML PKDD 2024.


Software development

1. ampute in R-package mice

library(mice)
?ampute

R-function ampute is the implementation of a multivariate amputation procedure: a method for generating missing data in complete datasets. With ampute, it is straightforward to generate missing values in multiple variables, with different missing data proportions and varying underlying missingness mechanisms. Read the article or the vignette to learn more.

2. parlMICE

For large datasets or when you want to impute with a large number of imputations, multiple imputation with mice in R-package mice may have a long run time. As a solution, Gerko Vink and I created wrapper function parlMICE, which allows for a parallel run of mice.

The function is now part of package mice under the name parlmice.

library(mice)
?parlmice

All information can be found in the github repo or in the vignette.

3. pyampute: the first Python library for data amputation

Library pyampute provides the multivariate amputation methodology to the Python community, and it does more. It has improved default settings, allows for a combined MAR+MNAR mechanism, for custom probability functions and since it is compatible with scikit-learn’s fit and transform paradigm, seamless integration in data processing pipelines becomes easy.

Find plenty of examples in pyampute’s documentation. Davina’s presentation at SciPy22 can be found here.

Install using pip or from source:

pip install pyampute
git clone https://github.com/RianneSchouten/pyampute.git
pip install ./pyampute

Invited presentations

Year Occasion Type Topic Link to materials
2025 Research visit @ Bielefeld University Presentation Exceptional Model Mining for Hierarchical Data
2025 Info-topic about my PhD research News Exceptional Model Mining for Hierarchical Data link
2024 AI for Healthcare Invited guest-lecture at Radboud University Nijmegen Exceptional Model Mining
2021 EAISI Eindhoven Contribution to seminar Towards a better understanding of exceptional lifestyle behaviour
2019 Workshop R-Ladies Amsterdam Invited presentation at seminar Developed and presented a workshop about analysis of missing values, evaluation and implementation of missing data methods link
2018 ICT Open Contribution to 1-day conference Handling Missing Data in Data Science link
2018 European Women in Technology Masterclass at conference Dealing with missing data in R: Amputation or Imputation? presentation and exercises
2018 sat-R-Day Contribution to 1-day conference Missing data link
2018 Data Science Hackathon By invitation Developed and lead a missing data challenge link
2017 Amst-R-Dam Contribution to seminar How to use R-function ampute to generate missing values in complete datasets article and documentation
2017 UseR!2017 Contribution to conference Introduction to multivariate amputation with ampute