See here for complete Curriculum Vitae
E-mail: r.m.schouten@tue.nl
Please contact me for my research and teaching statements.
My research revolves around developing Local Pattern Mining methods (LPMs) that extract societal relevant, interpretable patterns from data. In particular, I focus on developing methods that extract reliable and robust patterns, from data that does not follow the conventional row-by-column, flat-table format, such as sequential and hierarchical data.
I enjoy working in inter- and multidisciplinary teams. In some collaborations, domain experts consult me to support the process of doing statistically sound and trustworthy analyses. Generally, their problems relate to missing data. In other collaborations, I further develop pattern mining techniques to enable domain experts to analyze variation in human behavior. For instance, together with medical experts, we discovered subgroups of patients with deviating blood glucose fluctuations. Furthermore, together with policy advisers, we discovered subgroups of adolescents with deviating trends in alcohol use.
In my teaching and supervision, I aim to support students in becoming independent learners. My 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.
See here for complete Curriculum Vitae.
2020 - Present: Ph.D. Candidate at Eindhoven University of Technology, the Netherlands. Under supervision of prof. dr. Mykola Pechenizkiy and dr. Wouter Duivesteijn. Dissertation is submitted. Ph.D. defense scheduled for 16 Jan. 2025. Topic: Exceptional Model Mining for Hierarchical Data.
2017 - 2019: Researcher at Utrecht University, the Netherlands. Supervised by prof. dr. Stef van Buuren and dr. Gerko Vink.
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.
See Google Scholar for number of citations.
Schouten, R.M. (2024) On the role of prognostic factors and effect modifiers in structural causal models. Accepted for presentation at Causal Representation Learning Workshop NeurIPS. See here for the paper!
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. Accepted for presentation at BNAIC/BeNeLearn 2024. See here for the presentation.
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. Accepted for presentation at BNAIC/BeNeLearn 2024. See here for the presentation.
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. 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. 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. 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. Zenodo. 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 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). 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 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 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 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 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 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 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 In: Proceedings SPIE 9905, Space TElescopes and Instrumentation 2016: Ultraviolet to Gamma Ray, 99055v. DOI: 10.1117/12.2231627
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)
| Year | Call | Type | Title | Together with |
|---|---|---|---|---|
| 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 |
| 2024 | ECML PKDD | €500 reimbursement of entree ticket | Proceedings Chair | Together with dr. Wouter Duivesteijn |
Ongoing: reached interview phase, scheduled for Nov. 2024:
| 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 |
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 |
|---|---|---|---|
| 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 |
| 21/22 | Research Topics in Data Mining | MSc | Taught 2 lectures about Missing Data, supervised 7 groups of students during research project |
| 22/23 | Research Topics in Data Mining | MSc | Taught 2 lectures about Missing Data, supervised 4 groups of students during research project |
| 24/25 | Research Topics in Data Mining | MSc | Responsible for track: Empirical Challenges in Data Mining |
“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 |
|---|---|---|---|
| 2015 | 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 |
| 2016 | Survey Research: Design, Implementation and Data Processing | Advanced MSc | Organization,Tutoring exercise classes |
| 2016 | Survey Research: Statistical Analysis and Estimation | 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)
These students completed their projects, (partly) under my supervision, many with high grades.
| Year | Student | Type | Topic |
|---|---|---|---|
| 20/21 | Bart van Dooren | MSc Thesis with Philips | Predicting Cardiovascular Risk with Objective Physical Activity Measurements, supervision together with prof.dr. Mykola Pechenizkiy |
| 21/22 | Mats Verbraak | Research Proposal | Handling Missing Data in the Prediction Domain using Multiple Imputation |
| 21/22 | Isabel van den Heuvel | Research Proposal | Equivalence Testing for Developing Fair Machine Learning Algorithms, supervision together with Hilde Weerts |
| 21/22 | Varun Kamat | Internship at Signify | Recommendation Tool for Component Database |
| 21/22 | Mika van Loon | MSc Thesis | Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining, 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 |
| 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 |
These students are currently doing their project, (partly) under my supervision:
| Year | Student | Type | Topic |
|---|---|---|---|
| 23/24 | Bart Slenders | MSc Thesis | Beam Pollution in Exceptional Model Mining, supervision together with dr. Wouter Duivesteijn |
| 24/25 | Haoqi Guo | MSc Thesis | Visualization of Counterfactual Explanations, supervision together with prof.dr. Mykola Pechenizkiy |
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.
ampute in R-package miceR-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.
parlMICEFor 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.
All information can be found in the github repo or in the vignette.
pyampute: the first Python library for data
amputationLibrary 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 pyamputegit clone https://github.com/RianneSchouten/pyampute.git
pip install ./pyampute| Year | Occasion | Type | Topic | Link to materials |
|---|---|---|---|---|
| 2024 | Course AI for Health, at Radboud University Nijmegen | Invited guest-lecture | 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 |
| 2021 | EAISI Eindhoven | Contribution to seminar | Towards a better understanding of exceptional lifestyle behaviour | |
| 2021 | ECMLPKDD | Poster at conference | Mining Sequences with Exceptional Transition Behaviour of Varying Order | link |
| 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 |