Andrés Tello

Multivariate State Estimation in Drinking Water Distribution Networks

Supervisors: Huy Truong, Andrés Tello, Alexander Lazovik
Date: 2026-01-09
Type: bachelor-project/master-project/master-internship
Description:

Monitoring water distribution networks plays the main role in ensuring safe drinking water delivery to millions of residents in the urban area. Traditionally, this task relies on physics-based mathematical simulations; however, such models require a large number of parameters and frequent recalibration to maintain accuracy consistent with sensor measurements. As an alternative, recent studies have proposed data-driven approaches based on Graph Neural Networks (GNNs), which leverage pressure measurements from a limited set of sensors at known locations to infer pressure values at unmonitored nodes in the network. Building on this idea, the project extends the existing univariate method to a multivariate framework, aiming to jointly estimate multiple hydraulic quantities, including pressure, demand, flow rate, head loss, and others. The candidate is expected to have a basis of machine-learning foundation and proficiency in one of the deep learning frameworks (PyTorch, TensorFlow). Reference:
Graph Neural Networks for Pressure Estimation in Water Distribution Systems**.**

Evaluating LoRA for GNN-based model adaptation

Supervisors: Andrés Tello, Alexander Lazovik
Date: 2026-01-05
Type: bachelor-project/master-project
Description:

Foundation models have become a game-changer in several fields due to their strong generalization capabilities after some form of model adaptation, with fine-tuning being the most common approach. In this project, we aim to evaluate the effectiveness of Low-Rank Adaptation (LoRA) methods in terms of model performance, model size, and memory usage. While conventional full fine-tuning often yields high accuracy, LoRA can represent a more sustainable yet still effective alternative for model adaptation.

In this project, the student will implement a LoRA-based approach to adapt a pre-trained GNN-based model to new, unseen target datasets in the context of Water Distribution Networks (WDNs). The pre-trained model has been trained on several WDNs for pressure reconstruction, and the goal is to adapt it to make predictions on unseen WDN topologies with different operating conditions. The LoRA-based adaptation will be compared against a conventional full fine-tuning approach.

References:
LoRA: Low-Rank Adaptation of Large Language Models. Graph low-rank adapters of high regularityfor graph neural networks and graph transformers. ELoRA: Low-Rank Adaptation for Equivariant GNNs

Dataset Management

Supervisors: Huy Truong, Andrés Tello
Date: 2025-08-25
Type: bachelor-internship
Description:

Have you ever managed a large dataset? This project provides an opportunity to handle a dataset with over 8,000 downloads each month. You will reorganize the dataset by task, document it thoroughly, and create a user-friendly interface and leaderboard. The project also involves working with HPC clusters, Hugging Face libraries, and GitHub Pages for documentation. Basic Python skills and familiarity with Linux commands are required.

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.

Generalization in Graph Neural Networks