Researcher ML in Healthcare, PhD Candidate, Data Scientist.


Visiting Researcher, Machine Learning

2023 University of California, Irvine

Research stay at Stephan Mandt’s and Padhriac Smyth’s groups on applying state-of-the-art modelling to healthcare data.

  • Invited as scholar in the framework of HPI Research Center in Machine Learning and Data Science at UCI
  • Presentations of research to the groups.
  • Extensive collaboration talks with both groups.
  • Participation, presentation, and collaboration in research center reading group.
  • Visit to Stanford University for collaboration talks with the Nigam Shah’s Biomedical Data Science group.

PhD Candidate, Machine Learning

2021-now Hasso Plattner Institute

As a PhD candidate at the HPI, I am working on a project aimed at developing a predictive approach to detecting complications after surgeries in collaboration with Berlin’s biggest hospital, the Charité. The challenge I face is to combine multiple data sources and employ different AI/ML techniques to get a risk assessment for doctors to increase the survival rate.

  • Supervision
    • Master Theses: Hendrik Schmidt, 2022-2023: Benchmarking Model-agnostic Multi-source Supervised Domain Adaptation for Clinical Prediction on ICU Data (paper in progress) and YAIB. Youssef Mecky: Comparative Analysis of Explainable AI Methods Across Domains for ICU Risk Prediction Models 2023-2024 (paper in progress).
    • Master Project: Alisher Turubayev, Anna Shopova, Fabian Lange, Mahmut Kamalak, Paul Mattes, and Victoria Ayvasky, 2022-2023: Deep Learning Data Generation for Medical Prediction Systems.
    • Course: Statistics in Healthcare, 2022, 2023: Grading and Supervision of final presentation and report.
  • Talks
    • Invited talk at ETH Zürich Biomedical Informatics group about Yet another ICU Benchmark (YAIB).
    • Invited talk at UCI about PhD projects and collaboration opportunities.
    • Talk at Deutsche Forschungsdatenportal für Gesundheit (FDPG) about YAIB, invited by Mila Hardt.

Double Master in Data Science

2019-2021 EIT Digital Master School, TU Eindhoven & TU Berlin, with Honours

The EIT Digital Master programme is a selective double degree Master of Science focused on combining technical knowledge and innovation at two renowned European Technical Universities. I studied at Eindhoven University of Technology in the period of 2019-2020 and at the Technical University of Berlin during 2020-2021. This programme includes a minor in entrepreneurship and innovation and is supported by the European Institute of Innovation and Technology (which is part of the EU).

  • GPA – 8.5 [4.0/4.0] (Cum Laude) 151 ECTS
  • Thesis “A Semi-automated Training Data Generation Approach with the Human-in-the-Loop.” at the Database Systems and Information Management Group of TU Berlin
    • Under supervision of Prof. Volker Markl, Dr. Jorge-Arnulfo Quiané-Ruiz, Dr. Francesco Ventura & Dr. Zoi Kaoudi
    • Within the Agora Ecosystem and based on the DataFarm System
    • Including two peer-reviewed publications at A* conferences (and best demonstration award).
  • Awarded the EIT Digital Excellence Scholarship
  • Participant of the Honors Academy at TU/e: Honours programme of 20 ECTS for Personal Leadership
  • Including courses: Advanced Algorithms, Cloud Computing, Discrete Event Systems, Speech Analysis, Scalable Data Science, Statistical Learning Theory, Advanced Statistics, Visualization, Data Mining, Data Engineering, Process Mining and Technology Entrepreneurship
  • With Innovation Space Project in interdisciplinary team for Signify (Philips Lighting), Cyclomedia and the TU/e Intelligent Lighting Institute
  • Selected for the Semi-Professional Race Rowing team at E.S.R Thêta

Bachelor of Computer Science

2016-2019 Utrecht University, with Honours

During my time at Utrecht University, I have developed skills for problem solving and reasoning. I have taken many different courses in Computer Science and other disciplines.

  • GPA – 8.28, [4.0/4.0] (Cum Laude) 218.5 ECTS
  • Descartes Honours Programme (30 ECTS): Focused on broad academic development through lectures from prominent figures in society and projects with students from other disciplines
  • Minor in Mathematics
  • Including courses: Data Analysis and Retrieval, Algorithms, Intelligent Systems, Computational Intelligence, Optimisation & Complexity, Concurrency, Graphics, Functional Programming and Data-structures
  • Bachelor software project in automated medical reporting with semantic interpretation
  • Co-author of the “The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare” paper, written with the department of Computer Science, presented at the HICSS-53 conference.

Preporatory Scientific Education

Utrechts Stedelijk Gymnasium

This classical school (est. 1474) has offered a high standard of education to prepare me for university. I graduated with the Nature and Technology and Nature and Health profiles. I participated in the Robotics club and took Cambridge English Proficiency classes. Along with that I took Economics, German, Latin, Greek and Art history.

📄 Publications

See my Google Scholar profile for a comprehensive list

  • R. van de Water, H. Schmidt, P. Elbers, P. Thoral, B. Arnrich, and P. Rockenschaub, ‘Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML’. arXiv, Jun. 08, 2023. Available:, accepted at the 12th International Conference on Learning Representations (ICLR) 2024.
  • B. Arnrich, E. Choi, J.A. Fries, M.B.A. McDermott, J.Oh, T.J. Pollard, N. Shah, E. Steinberg, M. Wornow, R. van de Water, (MEDS working group, alpabethized authors) An ML-oriented Interface for Medical Record Datasets,, accepted at Time Series for Health (TS4H) at ICLR 2024
  • R.van de Water, A. Winter, M. Maurer, F. Treykorn, I. Sauer, B Pfitzner, B. Arnrich, Combining Time Series Modalities to Create Endpoint-driven Patient Records, Accepted at Workshop for Data Centric ML at ICLR 2024
  • R.van de Water, A. Winter, M. Maurer, F. Treykorn, I. Sauer, B Pfitzner, B. Arnrich, Combining Hospital-grade Clinical Data and Wearable Vital Sign Monitoring to Predict Surgical Complications, Accepted at Workshop for Timeseries For Health (TS4H) at ICLR 2024
  • B. Pfitzner, M. M. Maurer, A. Winter, C. Riepe, I. M. Sauer, R. van de Water, Bert Arnrich, Differentially-Private Federated Learning with Non-IID Data For Surgical Risk Prediction, First IEEE International Conference on AI for Medicine, Health, and Care (2024)
  • A. Winter, R. van de Water et al., Advancing Preoperative Outcome Prediction: A Comparative Analysis of Machine Learning and ISEG Risk Score for Predicting 90-Day Mortality after Esophagectomy, 2023, accepted at the 141st Congress of the German Society of Surgery (2024)
  • O. Konak, R. van de Water et al., ‘HARE: Unifying the Human Activity Recognition Engineering Workflow’, accepted at MDPI Sensors 2023
  • O. Konak, A. Wischmann, R. van de Water, and B. Arnrich, ‘A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition’. arXiv, Jul. 06, 2023. doi: 10.48550/arXiv.2307.02906. accepted at iWOAR 2023
  • R. van de Water, F. Ventura, Z. Kaoudi, J.-A. Quiané-Ruiz, and V. Markl, ‘Farming your ML-based query optimizer’s food’, in 2022 IEEE 38th international conference on data engineering (ICDE), 2022, pp. 3186–3189. (Best demo award 2022)
  • R. van de Water, F. Ventura, Z. Kaoudi, J. Quiane-Ruiz, and V. Markl, ‘Farm your ML-based query optimizer’s Food!–Human-Guided training data generation–’, presented at the Conference on Innovative Data Systems Research (CIDR), 2022.
  • L. Maas, M. Geurtsen, F. Nouwt, S. Schouten, R. van de Water, S. van Dulmen, F. Dalpiaz, K. van Deemter, S. Brinkkemper ‘The Care2Report system: automated medical reporting as an integrated solution to reduce administrative burden in healthcare’, in Information technology in healthcare: IT architectures and implementations in healthcare environments, Hawaii International Conference on System Sciences (HICSS), 2020.

✨ Honours

Best Demonstration Paper Award at ICDE

Received “Best Demonstration” Award from IEEE International Conference on Data Engineering (ICDE) 2022. Elaboration of the jury: “The award committee members have chosen your demonstration unanimously both based on the relevance of the problem, the high potential of the proposed approach and the excellent presentation.” More information here, video here.

Finalist at the Bionnale Speed Lecture Award

I was a finalist at the Bionnale 2022 Speed Lecture Award. Here I presented my doctoral research topic with the topic: “Dr. Droid and the Curious Case of Complication Prevention”. The presentation focused on communicating the possibilities and challenges of predicting post-surgery complications using hetereogeneous patient data. The presentation was recorded and can be viewed here

Honors Academy Master

Selected for the Honors Academy Master: a program consisting of 20 ECTS where you orient yourself in your professional career and choose your own program of professional development.

EIT Digital Excellence Scholarship

Awarded the highest scholarship for the selective Dual Degree MSc Data Science (with minor in entrepreneurship) at the TU Eindhoven and TU Berlin.

💼 Experience

Research Assistent at the Hasso Plattner Institute Digital Health Center

Responsible for supervising Master Theses, Master Projects and grading of individual students. Performing Scientific Data Analysis and Data Engineering for the Charité Academic Hospital.

Research Assistent at German Institute of Artificial Intelligence (DFKI)

Worked on the Agora project, an open platform bringing together data, algorithms, models and computational resources.

Ambassador of the EIT Digital Master School

Representative of my Master School where I organise events, answer questions from prospective students and represent the master school in several capacities.

Education assistant for the Science Faculty of Utrecht University

Reviewed and graded code, diagrams and documentation. I developed teaching and organisational skills.

  • Managed a class of 60 students and assisted with their practical assignments
  • Supervised finals with 300 participants

Organisation committee study excursion

Organised an educational exchange to Sofia of the Descartes Honours Programme

  • Organised educational activities for 35 students and 3 supervisors
  • Developed parts of the programme curriculum and managed university funds


🔧 Skills


C#, R, Spark, Haskell, Prolog, SQL, Python (a.o Pytorch, Tensorflow, Keras, Pandas), Java, SAS, Tableau, Matlab, AWS, Google Cloud, Flink, JavaScript, Unity, OWL, HTML&CSS, Figma


Machine learning, Statistics, Linear algebra, Statistical learning, Software

Soft skills

SCRUM, Scientific & Creative writing, Scientific presenting, Entrepreneurial pitching, Design Thinking, Chairing meetings, Leading teams

🎲 Miscellaneous Education

  • UX Design Certificate, 2020-2021, TechLabs Digital Shaper Program
  • Big Data Analytics Summer School, 2020, KTH Royal Institute of Technology
  • Education Assistant Training, 2018, Faculty of Social Sciences, Utrecht University
  • Academic Writing Training, 2018, Skills Lab, Utrecht University

💬 Languages

  • Dutch - Native
  • English - Fluently - Cambridge University (2016) CPE CEFR C2 level
  • German - Professional - TU Berlin (2021) CEFR C1 level

🛝 Interests

  • 🚣Rowing
  • 🖼️Galleries and art history
  • 🚴‍♂️Race biking
  • 🏃‍♂️Running
  • 🎸Playing guitar and drums