Supervision

A summary of the available projects and the theses I have supervised or co-supervised.

Available Theses

MSc/BSc
Open
  1. Evaluating the Impact of Data Quality in Medical Applications.
  2. Early Detection of Diseases using Federated Learning.
  3. Interactive Interface for Automated Time Series Data Repair.
  4. Comparing AutoML Model Selection using ImputeGAP.

Ongoing Theses

PhD Thesis
PhD
  1. Quentin Nater: Scalable Data Cleaning Systems for Extreme Large Time Series.
Master Thesis
MSc
  1. Time Series Imputation using Large Language Models (Maurice Amon)
  2. Synthetic Data Generation of Medical Audio Time Series (Matej Kutirov)
  3. Extensive Comparison of Data Transformation Techniques for Graph Time Series Imputation (Flavien Buron).

Completed Theses

PhD Thesis
PhD
  1. [2025] Abdelouahab Khelifati: A Holistic Approach for Time Series Management: Unifying Data Storage and Data Processing. Co-supervised with Philippe Cudré-Mauroux.
  2. [2024] Zakhar Tymchenko: Unifying Upstream and Downstream Time Series Data Cleaning . Co-supervised with Philippe Cudré-Mauroux.
  3. [2022] Ines Arous : Human-AI Collaborative Approaches for Open-Ended Data Curation. Co-supervised with Philippe Cudré-Mauroux and Jie Yang. Dimitris N. Chorafas Award for Best Thesis in Computer Science.
  4. [2019] Artem Lutov : Unsupervised and Parameter-Free Clustering of Large Graphs for Knowledge Exploration and Recommendation . Co-supervised with Philippe Cudré-Mauroux.
Master Thesis
MSc
  1. [2025] MUSED: Multimodal Unsupervised Streaming Event Detection (Andy Keller).
  2. [2025] Prediction of Environmental Hazards using Data Fusion (Dana Rim Ghousson).
  3. [2024] ImputeGAP: A Python library for Time Series Imputation Techniques (Quentin Nater).
  4. [2024] Empirical Evaluation of Multimodal Data Fusion Approaches for Event Detection (Martin Poplawski).
  5. [2024] A Comprehensive Analysis and Evaluation of Lossless Compression Techniques for Time Series Data (Dardana Jaha).
  6. [2023] A Lightweight Meta-Learning Framework to Asses the Validity of Model Selection in Time Series Forecasting (Mirko Bristle).
  7. [2023] Visualizing Time Series Recovery using ImputeBench (Brian Schweigler).
  8. [2023] Evaluating Link Prediction for Emerging Nodes in Dynamic Networks (Manuel Mondal).
  9. [2023] Repairing Anomalies in Time Series Data (Luca Althaus).
  10. [2022] Evaluating the Impact of Imputation on Time Series Tasks (Zakhar Tymchenko).
  11. [2021] A Configuration-Free Repair of Time Series (Guillaume Chacun).
  12. [2021] Multiclass Classification of Open-ended Answers (Louis Müller).
  13. [2021] Benchmark of Time Series Management Systems using Analytical Queries (Gabriela-Carmen Voroneanu).
  14. [2021] Evaluating Text Classification Models on Multilingual Documents (Julia Eigenmann).
  15. [2020] Correlation-based Anomaly Detection in Time Series (Adrian Hänni).
  16. [2019] Incremental Enrichment of Taxonomies using Transfer Learning (Mili Biswas).
  17. [2018] Trend Prediction on Fashion Data (Ahana Malik).
  18. [Ongoing] Extensive Comparison of GNN Data Transformation for Time Series Imputation (Flavien Buron).
  19. [2017] Real-Time Centroid Decomposition of Streams of Time Series (Oliver Stapleton.
  20. [2017] Modeling the Evolution of Fashion Trends using Matrix Factorization Techniques (Leutrim Kaleci ).
  21. [2016] Implementation of Centroid Decomposition Algorithm on Big Data Platforms - a Comparison Between Spark and Flink (Qian Liu).
  22. [2015] Online Anomaly Detection over Big Data Streams (Laura Rettig (MSc)). JAACS Best Thesis Award and the Faculty Award for Best Thesis in Theoretical Sciences.
  23. [2014] Decomposition of Time Series Subsequences (Jonathan Nagel)
  24. [2023] Evaluating the Optimality of Centroid Decomposition (Eszter Börzsönyi).
  25. [2022] Visualization and Analysis of Hydrological Data (Michał Kołtonik).
Bachelor Thesis
BSc
  1. [2024] Automatic Parametrization of Time Series Imputation Techniques (Lucien Gremaud).
  2. [2023] Evaluation of Neural Networks Imputation using ImputeBench (Flavien Buron).
  3. [2023] An Empirical Comparison of Time Series Forecasting Techniques (Jonathan Bernhard).
  4. [2022] Evaluating the Impact of Time Series Features on Downstream Tasks (Jana Stojanovic)
  5. [2021] Comparison of Synthetic Time Series Data Generation Techniques (Jonas Fontana).
  6. [2018] Reducing Polarization in Social Media by Balancing Content Exposure (Manuel Mondal).
  7. [2017] Empirical Comparison of Incremental Matrix Decomposition Techniques (Zakhar Tymchenko).