Visiting address
DS - BI
University of Groningen
Nijenborgh 9
9747 AG Groningen
The Netherlands
Quick links
Follow us
TODO
Student Colloquia
Centrality-Aware Learning vs Graph Neural Networks for State Estimation in Wastewater Systems
Supervisors:
Revin Naufal Alief,
Dilek Düştegör
Date: 2026-01-05
Type: bachelor-project/student-colloquium/master-internship/master-project
Description:
Wastewater systems can naturally be represented as graphs, where nodal pressure dynamics depend on both hydraulic conditions and network topology. Recent studies have demonstrated that standard deep learning models, such as multilayer perceptrons and convolutional neural networks, can achieve high pressure prediction accuracy when augmented with node centrality metrics (e.g., degree, betweenness, and closeness). These metrics implicitly encode structural information without explicitly modeling graph connectivity. However, despite promising results, such approaches have not yet been systematically compared with Graph Neural Networks (GNNs), which explicitly learn relational dependencies and are considered the natural baseline for topology-aware learning in WDNs.
In this project, the student will investigate the extent to which centrality-aware feature engineering can serve as an alternative to GNN-based models for pressure prediction in WDNs. The project will involve a comparative study of centrality-aware neural networks and GNN architectures applied to benchmark water distribution networks under varying demand patterns and operating conditions. The analysis will focus on prediction performance, robustness to topology variations, generalization across different networks, and computational complexity.
The project scope will be adjusted according to the study level. Bachelor-level projects will focus on reproducing existing baselines and conducting a controlled comparison between centrality-aware models and a single GNN architecture. Master-level projects may extend the analysis to include cross-network transfer learning, partial observability scenarios, and ablation studies to identify which aspects of network topology are captured by centrality metrics versus explicit graph representations. The outcomes of this project aim to clarify the practical trade-offs between implicit and explicit topology encoding in machine learning models for water infrastructure systems.>
Reference: (Not exhaustive)
Centrality-Aware Machine Learning for Water Network Pressure Prediction.
Reference: (Not exhaustive)
Centrality-Aware Machine Learning for Water Network Pressure Prediction.
A Systematic Review of Graph Neural Network Applications in Wastewater Systems
Supervisors:
Revin Naufal Alief,
Dilek Düştegör
Date: 2026-01-05
Type: bachelor-project/student-colloquium/master-internship/master-project
Description:
Graph Neural Networks (GNNs) have shown strong performance in graph-related data. If we are looking at the wastewater systems, they are able to be represented as network topology which is suitable for GNN to works on. Existing studies apply GNNs to a variety of tasks, including state estimation, forecasting, anomaly detection, and sensor placement. However, the literature remains scattered, and the overview of methods, datasets, and research gaps is still lacking. In this project, the student will conduct a systematic literature review of GNN-based approaches in wastewater systems following a structured review protocol (e.g., PRISMA). The student will identify, screen, and categorize relevant studies based on application domain, learning task, data sources (real vs synthetic), and evaluation settings. Special attention will be given to limitations related to sensor availability, uncertainty handling, and generalization across networks. The outcome of the project is a structured taxonomy and comparative analysis of existing GNN-based wastewater studies, highlighting open challenges and opportunities for future research, particularly in sensor placement and digital twin development. Project depth will be adjusted to BS or MS level.
References:
Graph Neural Network Empowers Intelligent Education: A Systematic Review From an Application Perspective. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. A Comprehensive Survey on Graph Neural Networks.
Graph Neural Network Empowers Intelligent Education: A Systematic Review From an Application Perspective. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. A Comprehensive Survey on Graph Neural Networks.
Uncertainty Quantification in GNN-based Water Level Estimation
Supervisors:
Revin Naufal Alief,
Dilek Düştegör
Date: 2026-01-05
Type: bachelor-project/student-colloquium/master-internship/master-project
Description:
Graph Neural Networks (GNNs) have shown strong performance in estimating hydraulic states in partially observed wastewater networks. However, most existing approaches focus on point predictions and provide limited insight into prediction reliability, which is crucial for decision-making. In this project, the student will (1) do literature review on conformal prediction for graph-structured data, emphasizing the challenges of applying CP to graphs (e.g., exchangeability issues in node/edge settings and dependence in graph neighborhoods), and (2) implement a split conformal prediction pipeline on top of a pre-trained GNN for water level estimation in wastewater networks. Recent work such as Conformalized GNNs (CF-GNN) and conformal prediction sets for GNNs provide practical designs and evaluation protocols for graph settings. The student will evaluate uncertainty quality using empirical coverage and interval width across sensor-masking ratios and analyze how uncertainty varies across sparse sensor conditions. Project depth will be adjusted to BS or MS level.
References: Uncertainty Quantification over Graph with Conformalized Graph Neural Networks. Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks.
References: Uncertainty Quantification over Graph with Conformalized Graph Neural Networks. Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks.
Sparse Sensor Placement for Graph-Based State Estimation
Supervisors:
Revin Naufal Alief,
Dilek Düştegör
Date: 2026-01-05
Type: bachelor-project/student-colloquium/master-internship/master-project
Description:
This project studies sparse sensor placement in large networked systems to support accurate state estimation using graph neural networks (GNNs). The focus is on identifying critical sensor locations using graph-theoretic properties and invariants, and evaluating their impact on learning-based estimation performance. Students will implement and compare sensor placement strategies and assess their effectiveness under limited sensing. Project depth will be adjusted to BS or MS level.
Graph Neural Networks for Sensor Placement: A Proof of Concept towards a Digital Twin of Water Distribution Systems.
INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks
Graph Neural Networks for Evaluating the Reliability and Resilience of Infrastructure Systems: A Systematic Review of Models, Applications, and Future Directions
Wastewater Systems Benchmark Dataset Development
Supervisors:
Revin Naufal Alief,
Dilek Düştegör
Date: 2026-01-05
Type: bachelor-project/student-colloquium/master-internship/master-project
Description:
This project aims to develop an academic benchmark dataset for wastewater systems to support reproducible research. The work includes a literature review, followed by dataset creation through merging existing data or simulating diverse operating scenarios. A key outcome is a modular, automated data-generation pipeline with clear documentation, suitable for data-driven modeling and analysis tasks.
Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks
Benchmarking dataset for leak detection and localization in water distribution systems
LeakDB: A Benchmark Dataset for Leakage Diagnosis in Water Distribution Networks
Benchmarking Dataset for Leak Detection and Localization in Water Distribution Systems
Conditional planning an overview of approaches
Supervisors:
Heerko Groefsema
Date: 2025-02-05
Type: student-colloquium
Description:
References:
Peot, M. A., & Smith, D. E. (1992, January). Conditional nonlinear planning. In Artificial Intelligence Planning Systems (pp. 189-197). Morgan Kaufmann. Blythe, J. (1999). An overview of planning under uncertainty. Artificial intelligence today, 85-110. Rintanen, J. (1999). Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research, 10, 323-352. Karlsson, L. (2001, January). Conditional progressive planning under uncertainty. In IJCAI (pp. 431-438).
Peot, M. A., & Smith, D. E. (1992, January). Conditional nonlinear planning. In Artificial Intelligence Planning Systems (pp. 189-197). Morgan Kaufmann. Blythe, J. (1999). An overview of planning under uncertainty. Artificial intelligence today, 85-110. Rintanen, J. (1999). Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research, 10, 323-352. Karlsson, L. (2001, January). Conditional progressive planning under uncertainty. In IJCAI (pp. 431-438).
Verifying the data perspective of business processes
Supervisors:
Heerko Groefsema
Date: 2025-02-05
Type: student-colloquium
Description:
References:
Groefsema, H., Beest, N.R.T.P.v., Governatori, G. (2022). On the Use of the Conformance and Compliance Keywords During Verification of Business Processes. In: Business Process Management Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 458. Springer. H. Groefsema, N.R.T.P. van Beest, A. Armas-Cervantes, Efficient conditional compliance checking of business process models, Computers in Industry, Volume 115, 2020. Alin Deutsch, Richard Hull, Fabio Patrizi, and Victor Vianu. 2009. Automatic verification of data-centric business processes. In Proceedings of the 12th International Conference on Database Theory (ICDT '09). N. van Beest, H. Groefsema, A. Cryer, G. Governatori, S. C. Tosatto and H. Burke, Cross-Instance Regulatory Compliance Checking of Business Process Event Logs, in IEEE Transactions on Software Engineering, vol. 49, no. 11, pp. 4917-4931, Nov. 2023.
Groefsema, H., Beest, N.R.T.P.v., Governatori, G. (2022). On the Use of the Conformance and Compliance Keywords During Verification of Business Processes. In: Business Process Management Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 458. Springer. H. Groefsema, N.R.T.P. van Beest, A. Armas-Cervantes, Efficient conditional compliance checking of business process models, Computers in Industry, Volume 115, 2020. Alin Deutsch, Richard Hull, Fabio Patrizi, and Victor Vianu. 2009. Automatic verification of data-centric business processes. In Proceedings of the 12th International Conference on Database Theory (ICDT '09). N. van Beest, H. Groefsema, A. Cryer, G. Governatori, S. C. Tosatto and H. Burke, Cross-Instance Regulatory Compliance Checking of Business Process Event Logs, in IEEE Transactions on Software Engineering, vol. 49, no. 11, pp. 4917-4931, Nov. 2023.
Explaining Graph Neural Networks
Supervisors:
Andrés Tello
Date: 2025-02-01
Type: student-colloquium
Description:
References:
Wang, X., & Shen, H. W. GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries. In The Twelfth International Conference on Learning Representations. Müller, P., Faber, L., Martinkus, K., & Wattenhofer, R. (2024). GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Lu, S., Mills, K. G., He, J., Liu, B., & Niu, D. (2024). GOAt: Explaining graph neural networks via graph output attribution. arXiv preprint arXiv:2401.14578. Wang, X., & Shen, H. W. (2022). Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks. arXiv preprint arXiv:2209.07924.
Wang, X., & Shen, H. W. GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries. In The Twelfth International Conference on Learning Representations. Müller, P., Faber, L., Martinkus, K., & Wattenhofer, R. (2024). GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Lu, S., Mills, K. G., He, J., Liu, B., & Niu, D. (2024). GOAt: Explaining graph neural networks via graph output attribution. arXiv preprint arXiv:2401.14578. Wang, X., & Shen, H. W. (2022). Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks. arXiv preprint arXiv:2209.07924.
Generalization in Graph Neural Networks
Supervisors:
Andrés Tello
Date: 2025-02-01
Type: student-colloquium
Description:
References:
Kanatsoulis, C., & Ribeiro, A. Counting Graph Substructures with Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Liu, H., Feng, J., Kong, L., Liang, N., Tao, D., Chen, Y., & Zhang, M. (2023). One for all: Towards training one graph model for all classification tasks. arXiv preprint arXiv:2310.00149. Lee, H., Yoon, K., 2023. Towards better generalization with flexible representation of multi-module graph neural networks. Transactions on Machine Learning Research. Yu, J., Liang, J., & He, R. (2023). Mind the Label Shift of Augmentation-based Graph OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11620-11630).
Kanatsoulis, C., & Ribeiro, A. Counting Graph Substructures with Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Liu, H., Feng, J., Kong, L., Liang, N., Tao, D., Chen, Y., & Zhang, M. (2023). One for all: Towards training one graph model for all classification tasks. arXiv preprint arXiv:2310.00149. Lee, H., Yoon, K., 2023. Towards better generalization with flexible representation of multi-module graph neural networks. Transactions on Machine Learning Research. Yu, J., Liang, J., & He, R. (2023). Mind the Label Shift of Augmentation-based Graph OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11620-11630).
Multimodality Graph Foundation Models
Supervisors:
Huy Truong
Date: 2025-01-21
Type: student-colloquium
Description:
References:
Lam, Hoang Thanh, et al. "Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery." *arXiv preprint arXiv:2306.12802* (2023). Xia, Lianghao, Ben Kao, and Chao Huang. "Opengraph: Towards open graph foundation models." *arXiv preprint arXiv:2403.01121* (2024). Ektefaie, Yasha, et al. "Multimodal learning with graphs." *Nature Machine Intelligence* 5.4 (2023): 340-350.
Lam, Hoang Thanh, et al. "Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery." *arXiv preprint arXiv:2306.12802* (2023). Xia, Lianghao, Ben Kao, and Chao Huang. "Opengraph: Towards open graph foundation models." *arXiv preprint arXiv:2403.01121* (2024). Ektefaie, Yasha, et al. "Multimodal learning with graphs." *Nature Machine Intelligence* 5.4 (2023): 340-350.
Test-Time Training
Supervisors:
Huy Truong
Date: 2025-01-21
Type: student-colloquium
Description:
References:
Sun, Yu, et al. "Test-time training with self-supervision for generalization under distribution shifts." International conference on machine learning. PMLR, 2020. Liu, Yuejiang, et al. "Ttt++: When does self-supervised test-time training fail or thrive?." Advances in Neural Information Processing Systems 34 (2021): 21808-21820. Liang, Jian, Ran He, and Tieniu Tan. "A comprehensive survey on test-time adaptation under distribution shifts." *International Journal of Computer Vision* (2024): 1-34. Behrouz, Ali, Peilin Zhong, and Vahab Mirrokni. "Titans: Learning to Memorize at Test Time." arXiv preprint arXiv:2501.00663 (2024).
Sun, Yu, et al. "Test-time training with self-supervision for generalization under distribution shifts." International conference on machine learning. PMLR, 2020. Liu, Yuejiang, et al. "Ttt++: When does self-supervised test-time training fail or thrive?." Advances in Neural Information Processing Systems 34 (2021): 21808-21820. Liang, Jian, Ran He, and Tieniu Tan. "A comprehensive survey on test-time adaptation under distribution shifts." *International Journal of Computer Vision* (2024): 1-34. Behrouz, Ali, Peilin Zhong, and Vahab Mirrokni. "Titans: Learning to Memorize at Test Time." arXiv preprint arXiv:2501.00663 (2024).
Cluster Scheduling for DLT workloads
Supervisors:
Kawsar Haghshenas,
Mahmoud Alasmar
Date: 2025-01-27
Type: student-colloquium
Description:
References:
Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel et al. "Gandiva: Introspective Cluster Scheduling for Deep Learning." In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 595-610. 2018. Weng, Q., Yang, L., Yu, Y., Wang, W., Tang, X., Yang, G., & Zhang, L. (2023). Beware of Fragmentation: Scheduling {GPU-Sharing} Workloads with Fragmentation Gradient Descent. In 2023 USENIX Annual Technical Conference (USENIX ATC 23) (pp. 995-1008). Zhang, X., Zhao, H., Xiao, W., Jia, X., Xu, F., Li, Y., ... & Liu, F. (2024). Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling. arXiv preprint arXiv:2408.08586. Lai, F., Dai, Y., Madhyastha, H. V., & Chowdhury, M. (2023). {ModelKeeper}: Accelerating {DNN} training via automated training warmup. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23) (pp. 769-785).
Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel et al. "Gandiva: Introspective Cluster Scheduling for Deep Learning." In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 595-610. 2018. Weng, Q., Yang, L., Yu, Y., Wang, W., Tang, X., Yang, G., & Zhang, L. (2023). Beware of Fragmentation: Scheduling {GPU-Sharing} Workloads with Fragmentation Gradient Descent. In 2023 USENIX Annual Technical Conference (USENIX ATC 23) (pp. 995-1008). Zhang, X., Zhao, H., Xiao, W., Jia, X., Xu, F., Li, Y., ... & Liu, F. (2024). Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling. arXiv preprint arXiv:2408.08586. Lai, F., Dai, Y., Madhyastha, H. V., & Chowdhury, M. (2023). {ModelKeeper}: Accelerating {DNN} training via automated training warmup. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23) (pp. 769-785).
Estimating Deep Learning GPU Memory Consumption
Supervisors:
Kawsar Haghshenas,
Mahmoud Alasmar
Date: 2023-12-11
Type: student-colloquium
Description:
References:
Yanjie Gao, Yu Liu, Hongyu Zhang, Zhengxian Li, Yonghao Zhu, Haoxiang Lin, and Mao Yang. "Estimating GPU memory consumption of deep learning models." In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 1342-1352. 2020. Haiyi Liu, Shaoying Liu, Chenglong Wen, and W. Eric Wong. "TBEM: Testing-Based GPU-Memory Consumption Estimation for Deep Learning." IEEE Access, 10, pp.39674-39680. 2022. Lu Bai, Weixing Ji, Qinyuan Li, Xilai Yao, Wei Xin, and Wanyi Zhu. "Dnnabacus: Toward accurate computational cost prediction for deep neural netw." arXiv preprint arXiv:2205.12095. 2022. Yanjie Gao, Xianyu Gu, Hongyu Zhang, Haoxiang Lin, and Mao Yang. "Runtime performance prediction for deep learning models with graph neural network." In IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 368-380. 2023.
Yanjie Gao, Yu Liu, Hongyu Zhang, Zhengxian Li, Yonghao Zhu, Haoxiang Lin, and Mao Yang. "Estimating GPU memory consumption of deep learning models." In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 1342-1352. 2020. Haiyi Liu, Shaoying Liu, Chenglong Wen, and W. Eric Wong. "TBEM: Testing-Based GPU-Memory Consumption Estimation for Deep Learning." IEEE Access, 10, pp.39674-39680. 2022. Lu Bai, Weixing Ji, Qinyuan Li, Xilai Yao, Wei Xin, and Wanyi Zhu. "Dnnabacus: Toward accurate computational cost prediction for deep neural netw." arXiv preprint arXiv:2205.12095. 2022. Yanjie Gao, Xianyu Gu, Hongyu Zhang, Haoxiang Lin, and Mao Yang. "Runtime performance prediction for deep learning models with graph neural network." In IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 368-380. 2023.