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index.Rmd
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---
title: "Rianne M. Schouten"
subtitle: "Postdoctoral researcher"
author: |
| Local Pattern Mining | Subgroup Discovery | Hierarchical Data | Experimental Evaluation | Missing Data | Personalized Learning Analytics | Behavioral Machine Learning | AI-Human Interaction |
date: ""
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---
[Google Scholar](https://scholar.google.nl/citations?view_op=list_works&hl=nl&user=xC56bhMAAAAJ&gmla=AJsN-F4IWn_9Vw9hH7hPN1zIMfNadBL6mvmu-rPpRS6p-4Enc7fR6GrOMYyN2QEr21D5Y3Uhjtj0HPT3I7qsh3MVE1v2L8MRfjsk7W1Qop4mDunpnTRDeMYF0DmoRYmja6b7GvnmC4R3)
[LinkedIn](https://www.linkedin.com/in/rianne-schouten-5bb29491/)
[Github](https://github.com/RianneSchouten)
E-mail: [[email protected]](mailto:[email protected])
Please contact me for my research and teaching statements.
---
### Introduction
My research revolves around developing Local Pattern Mining methods (LPMs) that **extract societal relevant, reliable, interpretable patterns** from data. In particular, I work with data that does not follow the conventional row-by-column, flat-table format, such as **sequential and hierarchical data**. I set up multiple **interdisciplinary** collaborations to analyze variation in human behavior. Among others, I worked together with experts from Utrecht University, Trimbos Institute, Hospital Twente, Erasmus MC and the Turku Research Institute for Learning Analytics.
As a postdoctoral researcher, I will continue two collaborations. 1) I will work with the Turku Research Institute for Learning Analytics to investigate whether we can develop our pattern mining methods into an actual product (*valorisation*). 2) I will work with Erasmus MC to further develop methods for **analyzing and (synthetically) generating longitudinal, incomplete, medical data**. I will furthermore investigate the possibilities for utilizing Local Pattern Mining techniques in the domains of Behavioral Machine Learning, AI-Human Interaction and eXplainable AI.
In my teaching and supervision, I aim to support students in **becoming independent learners**. My teaching style connects well with the challenge-based learning paradigm, where the task of the teacher is to guide students in taking a structured approach in problem-solving and in thinking critically.
---
### Affiliations
[See here for complete Curriculum Vitae.](pdfs/ResumeRianneSchouten.pdf)
*2025 - present*: Postdoc at Eindhoven University of Technology, the Netherlands.
*2020 - 2024*: Ph.D. Candidate at Eindhoven University of Technology, the Netherlands.
Under supervision of [prof. dr. Mykola Pechenizkiy](https://www.win.tue.nl/~mpechen/) and [dr. Wouter Duivesteijn](http://wwwis.win.tue.nl/~wouter/).
Topic: *Exceptional Model Mining for Hierarchical Data*.
*2017 - 2019*: Researcher at Utrecht University, the Netherlands.
Supervised by [prof. dr. Stef van Buuren](https://scholar.google.nl/citations?user=_3y5C0UAAAAJ&hl=nl) and [dr. Gerko Vink](https://www.gerkovink.com/).
*Autumn 2016*: Staff Associate of Professor Andrew Gelman at Columbia University in the City of New York, US.
*Spring 2015*: Intern at SRON, Dutch Institute of Space Research, the Netherlands.
---
### Publications
[See Google Scholar for number of citations.](https://scholar.google.nl/citations?view_op=list_works&hl=nl&user=xC56bhMAAAAJ&gmla=AJsN-F4IWn_9Vw9hH7hPN1zIMfNadBL6mvmu-rPpRS6p-4Enc7fR6GrOMYyN2QEr21D5Y3Uhjtj0HPT3I7qsh3MVE1v2L8MRfjsk7W1Qop4mDunpnTRDeMYF0DmoRYmja6b7GvnmC4R3)
- **Schouten, R.M.** (2025) [Exceptional Model Mining for Hierarchical Data](pdfs/EMMHD.pdf). PhD thesis.
- **Schouten, R.M.** (2024) [On the role of prognostic factors and effect modifiers in structural causal models](pdfs/NeurIPS_CRL_RianneSchouten_nr42.pdf). Accepted for presentation at Causal Representation Learning Workshop NeurIPS. [See here for the paper!](pdfs/NeurIPSCRL_RianneSchouten_nr42.pdf)
- Van den Berg, N. T., Broekgaarden, B. O., Mahieu Dionysia, P., Martens, J. G., Niederle, J.,
**Schouten, R.M.**, & Duivesteijn, W. (2024) [Generating MNAR missingness in image data, with
additional evaluation ofMisGAN](pdfs/BNAIC_MisGAN_MNAR.pdf). Accepted for presentation at BNAIC/BeNeLearn 2024. [See here for the presentation](pdfs/BNAIC2024_MNAR.pdf).
- **Schouten, R.M.**, Stevens, G.W.J.M., van Dorsselaer, S.A.F.M., Duinhof, E.L., Monshouwer, K., Pechenizkiy, M. & Duivesteijn, W. (2024) [Analyzing the interplay between societal trends and socio-demographic variables with local pattern mining: Discovering exceptional trends in adolescent alcohol use in the Netherlands](pdfs/Alcohol_BNAIC_2024.pdf). Accepted for presentation at BNAIC/BeNeLearn 2024. [See here for the presentation](pdfs/BNAIC2024_Alcohol.pdf).
- **Schouten, R.M.**, Duivesteijn, W., Rasanen, P, Paul, J.M., & Pechenizkiy, M. (2024) [Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression](pdfs/FUNA_ECMLPKDD_2024.pdf). In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 66-82.
- **Schouten, R.M.**, Tascau, V., Ziegler, G.G., Casano, D., Ardizonne, M., & Erotokritou M.A. (2023) [Dropping incomplete records is (not so) straightforward](pdfs/2023_Schouten_MD.pdf). In: Proceedings of the 21st International Symposium on Intelligent Data Analysis (IDA), pp. 379-391.
- Verhaegh, R.F.A., Kiezebrink, J.J.E., Nusteling, F., Rio, A.W.A, Bendicsek, M.B., Duivesteijn, W. & **Schouten, R.M.** (2022) [A Clustering-inspired Quality Measure for Exceptional Preferences Mining — Design Choices and Consequences](pdfs/2022_Verhaegh_EPM.pdf). In: Proceedings of the International Conference on Discovery Science (DS), pp. 429–444.
- **Schouten, R.M.**, Zamanzadeh, D. & Singh, P. (2022) [Pyampute: a Python library for data amputation](https://rianneschouten.github.io/pyampute/build/html/index.html). Zenodo. [https://doi.org/10.25080/majora-212e5952-03e](https://doi.org/10.25080/majora-212e5952-03e).
- Van der Haar, J.F., Nagelkerken, S.C., Smit, I.G., van Straaten, K., Tack, J.A., **Schouten, R.M.** & Duivesteijn, W. (2022) [Efficient Subgroup Discovery Through Auto-Encoding](pdfs/2022_vdHaar_AE.pdf) In: Proceedings of the 20th International Symposium on Intelligent Data Analysis (IDA), pp. 327-340.
- **Schouten, R.M.**, Duivesteijn, W. & Pechenizkiy, M. (2022) [Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)](pdfs/2022_Schouten_EMMRCS.pdf). In: Proceedings of SIAM International Conference on Data Mining (SDM), pp. 585-593.
- **Schouten, R.M.**, Bueno, M.L.P., Duivesteijn, W. & Pechenizkiy, M. (2022) [Mining Sequences with Exceptional Transition Behaviour of Varying Order using Quality Measures based on Information-Theoretic Scoring Functions](pdfs/MiningExceptionalSequences_DAMI_2022.pdf) Data Mining and Knowledge Discovery (DAMI), 36: 379-413.
- IJsselhof R, Duchateau S, **Schouten R.M.**, Slieker M, Hazekamp M & Schoof P. (2020) [Long-Term Follow-Up of Pericardium for the Ventricular Component in Atrioventricular Septal Defect Repair](https://pubmed.ncbi.nlm.nih.gov/33164688/) World Journal for Pediatric and Congenital Heart Surgery, 11(6): 742-747.
- IJsselhof R.J., Duchateau S.D.R., **Schouten R.M.**, Freund, M.W., Heuser, J., Fejzic, Z., Haas, F., Schoof, P.H. & Slieker, M.G. (2019) [Follow-up After Biventricular Repair of the Hypoplastic Left Heart Complex](https://academic.oup.com/ejcts/article-abstract/57/4/644/5607276) European Journal of Cardiothoracic Surgery, 57(4): 644-651.
- **Schouten R.M.**, Lugtig, P. & Vink, G. (2018) [Generating missing values for simulation purposes: A multivariate amputation procedure](https://www.tandfonline.com/doi/full/10.1080/00949655.2018.1491577) Journal of Statistical Computation and Simulation, 88(15): 1909-1930.
- **Schouten, R.M.** and Vink, G. (2021) [The dance of the mechanisms: How observed information influences the validity of missingness assumptions](https://journals.sagepub.com/doi/full/10.1177/0049124118799376) Sociological Methods \& Research, 50(3): 1243-1258.
- Kappen, I.F.P.M., Bittermann, G.K.P., **Schouten, R.M.**, Bittermann, D., Etty, E., Koole, R., Kon, M., Van der Molen, M. & Breugem, C.C. (2017) [Long-term mid-facial growth of patients with a unilateral complete cleft of lip, alveolus and palate treated by two-stage palatoplasty: cephalometric analysis](https://www.researchgate.net/publication/308181181_Long-term_mid-facial_growth_of_patients_with_a_unilateral_complete_cleft_of_lip_alveolus_and_palate_treated_by_two-stage_palatoplasty_cephalometric_analysis) Clinical Oral Investigations, 21: 1801-1810.
- de Vries, C.P., **Schouten, R.M.**, Van der Kuur, J., Gottardi, L., & Akamatsu, H. (2016) [Microcalorimeter pulse analysis by means of principle component decomposition](https://arxiv.org/abs/1607.00834) In: Proceedings SPIE 9905, Space TElescopes and Instrumentation 2016: Ultraviolet to Gamma Ray, 99055v. DOI: 10.1117/12.2231627
---
### 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
- Performance bonus for 2021
> "Beside your overall excellent performance in your PhD research and EDIC project execution, you did an excellent job in project management of EDIC, and in setting up new successful collaborations. You helped a lot with the Research Topics in Data Mining course, and supervision of students." (Prof. dr. Mykola Pechenizkiy)
---
### 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|Together with prof.dr. Mykola Pechenizkiy|
|2024|ECML PKDD|€500 reimbursement of entree ticket|Proceedings Chair|Together with dr. Wouter Duivesteijn|
|2022|AI for Health EWUU Alliance|EUR 45k seed money|Better Imputation by Generative Adversarial NeTworks (BIGANT)|prof. dr.Stef van Buuren, dr. Gerko Vink, Hanne Oberman, prof. dr. Mykola Pechenizkiy, prof. dr. Cassio de Campos, **Rianne Schouten**, prof. dr. Daniel Oberski, dr. Thomas Debray, prof. dr. Fred van Eeuwijk|
---
### Teaching
To further develop myself as a teacher, I am participating in the University Teaching Qualification (UTQ) Training Program.
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:
|Year|Student|Type|Topic|
|----|------|-------|-------------------|
|Since 2023|Emmanuel C. Chukwu|PhD Candidate|Counterfactual Explanations in Time Series Classification|
|24\/25|Haoqi Guo|MSc Thesis|Clustering of Counterfactual Explanations, supervision together with prof.dr. Mykola Pechenizkiy|
Alumni:
|Year|Student|Type|Topic|
|----|------|-------|-------------------|
|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|
---
### Reviewing activities
I reviewing for journals and conferences such as Data Mining and Knowledge Discovery (DAMI), Machine Learning (ML), ECML PKDD, Journal of Statistical Society, and more.
I was recognized as an **excellent reviewer** at ECML PKDD 2024.
---
### Software development
#### 1. `ampute` in R-package **mice**
```{r, eval = FALSE}
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](pdfs/ArticleAmpute.pdf) or [the vignette](https://rianneschouten.github.io/mice_ampute/vignette/ampute.html) 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`.
```{r, eval = FALSE}
library(mice)
?parlmice
```
All information can be found [in the github repo](https://github.com/RianneSchouten/parlMICE) or in the [vignette](https://www.gerkovink.com/parlMICE/Vignette_parlMICE.html).
#### 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](https://rianneschouten.github.io/pyampute/build/html/index.html). Davina's presentation at SciPy22 can be found [here](https://www.youtube.com/watch?v=iI0gx52ucMA&t=25560s).
Install using pip or from source:
```
pip install pyampute
```
```
git clone https://github.com/RianneSchouten/pyampute.git
pip install ./pyampute
```
---
### Presentations
|Year|Occasion|Type|Topic|Link to materials|
|----|------|------|------------|----|
|2025|Info-topic about my PhD research|News|Exceptional Model Mining for Hierarchical Data|[link](https://www.cursor.tue.nl/en/background/2025/januari/week-3/home-stretch-discovering-unusual-patterns-in-data/)|
|2024|AI for Healthcare|Invited guest-lecture at Radboud University Nijmegen|Exceptional Model Mining|to appear|
|2024|PhD Forum IDA 2024|Contribution|Redudancy Metrics in Exceptional Model Mining|to appear|
|2021|Neglected Assumptions in Causal Inference (NACI) workshop at ICML|Contribution|Understanding the Role of Prognostic Factors and Effect Modifiers in Heterogeneity of Treatment Effect using a Within-Subjects Analysis of Variance|[link](pdfs/NACI_Poster_RMSchouten.pdf)|
|2021|EAISI Eindhoven|Contribution to seminar|Towards a better understanding of exceptional lifestyle behaviour||
|2021|ECML PKDD|Poster at conference|Mining Sequences with Exceptional Transition Behaviour of Varying Order|[link](pdfs/PosterSatelliteEvent_RMSchoutenetal.pdf)|
|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](https://github.com/RianneSchouten/workshops/tree/master/R-Ladies%20Workshop)|
|2018|ICT Open|Contribution to 1-day conference|Handling Missing Data in Data Science|[link](pdfs/ictopen2018_RianneSchouten.pdf)|
|2018|European Women in Technology|Masterclass at conference|Dealing with missing data in `R`: Amputation or Imputation?|[presentation and exercises](https://github.com/RianneSchouten/workshops/tree/master/WomenInTechnology)|
|2018|sat-R-Day|Contribution to 1-day conference|Missing data|[link](pdfs/SatRdayRianneSchouten.pdf)|
|2018|Data Science Hackathon|By invitation|Developed and lead a missing data challenge|[link](pdfs/Data_Science_Hackathon_Rianne_Schouten.pdf)|
|2017|Amst-R-Dam|Contribution to seminar|How to use R-function `ampute` to generate missing values in complete datasets|[article](pdfs/ArticleAmpute.pdf) and [documentation](https://rianneschouten.github.io/mice_ampute/vignette/ampute.html)|
|2017|UseR!2017|Contribution to conference|Introduction to multivariate amputation with `ampute`||