AidroLab

Courses

2023-2024

• Machine Learning for Graph Data | CS4350
• Signal Processing | CSE2220
• Modelling, Uncertainty and Data for Engineers | CEGM1000
• Data Science and Artificial Intelligence for Engineers | CEGM2003

2022-2023

• Machine Learning for Graph Data | CS4350
• Signal Processing | CSE2220
• Modelling, Uncertainty and Data for Engineers | CEGM1000

2021-2022

• Applied Machine Learning | CS4305TU
• Research Skills, Module 2.5 - Deep Learning for Hydrology and Water Resources Engineering | CIE5431
• Signal Processing | CSE2220
• Multimedia Analysis | CSE2230

Master Projects

MSc Thesis for CEG (Environmental Engineering)

For more proposals, please check your BrightSpace page and the Google Sheet link of the Dispuut Water & Environment (dispuut-we-citg@tudelft.nl | +31(0)15-2784284 | www.dispuutwaterandenvironment.com) or contact us.

Ongoing

• Optimizing the pump schedule of water distribution systems using a deep learning metamodel (Nikolaos Mertzanis)
• Modelling dike-breach floods in Dutch dike rings (Sergio Bulte)
• Hybrid Modelling in Hydrology Using a Neural Ordinary Differential Equations Approach (Jonathan Schieren)
• Small Deep Learning Models for Sewer Defect Detection (Brendan Determan)
• Improving PIV-based streamflow estimation with Deep Learning (Max Helmich)
• GAN-based rainfall nowcasting (Sven van Os)

Finished

• Assessment of Pump Failures in Rotterdam: A Five-Year Study (2016-2020): A Failure Analysis based on statistical modelling 2024 (Qiwen Zhang) 
• Diverse Explorations of Rainfall Nowcasting with TrajGRU: Mitigating Smoothness and Fading Out Challenges for Longer Lead Times 2024 (Yanghuan Zou)    
• Towards a fully distributed multivariable hydrological deep learning model with graph neural networks 2024 (Peter Nelemans)
• Characterization of plastic transport in the Saigon River: An analysis of the river stretch that crosses Ho Chi Minh City conducted in the rainy season 2024 (Francesca Lena, Edoarto Forte & Agatha Zamuner)
• Prediction of Discharges from Polders to ‘Boezem’ Canals with a Random Forest and an LSTM Model: Improving Inputs of the Decision Support System of the Hoogheemraadschap van Delfland 2024 (Josine van Marrewijk)
• Deep Learning for Geotechnical Engineering: The Effectiveness of Generative Adversarial Networks in Subsoil Schematization 2023 (Fabian Campos Montero
• A LSTM-based Generative Adversarial Network for End-use Water Modelling 2023 (Yukun Xie)
• Operational Streamflow Drought Forecasting for the Rhine River at Lobith Using the LSTM Deep Learning Approach 2023 (Jing Deng)   
• cGANs for multispectral snow extent analysis in the Alps 2023 (Adriaan Keurhorst)
• The Effect of Climate Variability on the Root Zone Storage Capacity 2023 (Nienke Tempel)
• GNNs and Beam Dynamics: Investigation into the application of Graph Neural Networks to predict the dynamic behaviour of lattice beams 2023 (Lex Niessen)
• The impact of an additional phenology model on the performance of conceptual hydrological models 2023 (Casper Pierik)
• Leak Localization in Water Distribution Networks 2023 (Zixi Meng)
• Macrolitter in Groyne Fields: Short term variability & the influence of natural processes 2023 (Jakob Grosfeld)
• Water balance-based approach to improve understanding of Drought Development: by calculating the root storage deficit 2023 (Piet Storm)
• Can fourier neural operators replicate the intrinsic predictability of spatiotemporal chaos?: for the Kuramoto-Sivashinsky system 2023 (Kevin Schuurman)         
• Do multi-year droughts increase floods? 2022 (Yang Zhao)            
• GGANet: Algorithm Unrolling for Water Distribution Networks Metamodelling 2023 (Albert Solà Roca)
• Transformer-based rainfall-runoff predictions 2023 (Kangmin Mao)
• Perceptual losses in precipitation nowcasting: Exploring limits and potential 2022 (Diewertje Dekker)
• Development of an LSTM-based methodology for burst detection in water distribution systems 2022 (Konstantinos Glynis)
• The role of water vapor observations in satellite-based rainfall information highlighted by a Deep Learning approach  2022 (Fabio Curzi)
• Predicting fluvial flood arrival times by making use of a deep learning model 2022 (Ron Bruijns)
• Using YOLOv5 for the Detection of Icebergs in SAR Imagery 2022 (Daan Hulskemper)
• Parametric design of a grid shell roof over existing buildings​, with a focus on connection design 2022 (Fiori Isufi)
• Matching streamflow river gauges with hydrologic models 2021 (Mizzi van der Ven)          
• Integrated Neural Network and Finite Element Analysis for constitutive modelling of soil 2021 (Keshav Kashichenula)            
• 3D Road Boundary Mapping of MLS Point Clouds 2021 (Qian Bai)
• Shallow Cumulus Clouds as Complex Networks 2021 (Pouriya Alinaghi)
• The variability of the rootzone storage capacity in Austria: An exploration of its controls 2021 (Bart Veenings)
• Relating groundwater heads to stream discharge by using machine learning techniques: A case study in subcatchment Chaamse Beken 2021 (Valerie Demetriades)            
• Estimating new reservoir locations with the use of a hydrological model for small holder cotton farmers in Maharashtra, India 2021 (Jente Janssen)
• Assessing Global Applicability of a Long Short-Term Memory (LSTM) Neural Network for Rainfall-Runoff Modelling 2021 (Katharina Wilbrand)
• Short-Term Water Demand Forecasting at District Level Using Deep Learning Methods 2021 (Diego Mauricio Corredor Mora)
• Applying deep learning vs machine learning models to reproduce dry spells at point scale from satellite information in a data-scarce region: the case of northern Ghana, 2021 (Panagiotis Mavritsakis)
• Exploration of Deep Learning-based Computer Vision for the Detection of Floating Plastic Debris in Waterways, 2021 (André J. Vallendar)
• Nowcasting heavy precipitation in the Netherlands: a deep learning approach, 2021 (Eva van der Kooij)
• Creation of new Extra-Tropical Cyclone fields in the North Atlantic with Generative Adversarial Networks, 2020 (Filippo Dainelli)

MSc Thesis for EEMCS (Intelligent Systems)

For more proposals, please check MSc thesis projects.

Ongoing

• Adaptive Learning on graphs (Alex Jeleniewski)
• Graph Learning for Multi-Sensor Radar (Radu Gaghi)
• Graph Neural Networks for Renewable Energy (Rodrigo Revilla Llaca)

Finished

• An Experimental Assessment of the Stability of Graph Contrastive Learning 2024 (Siert Sebus)
• The Hierarchical Subspace Iteration Method for Computing Vibration Modes of Elastic Objects 2024 (Julian van Dijk)
• A System for Model Diagnosis centered around Human Computation 2024 (Ziad Ahmad Saad Soliman Nawar)
• From Clicks to Conscious Choices: Investigating the Effects of Carbon Footprint Data in E-Commerce Recommender Systems 2023 (Sneha Lodha)
• Pure Cold Start Recommendation by Learning on Stochastically Expanded Graphs 2023 (Simon Dahrs)
• Nudging Towards Sustainable Choices via Recommender Systems 2023 (Raoul Kalisvaart)
• Self-Supervised Few Shot Learning: Prototypical Contrastive Learning with Graphs 2023 (Ojas Shirekar)
• Hardware-based implementations in Side-Channel Analysis: A comparison study of DL SCA attacks against HW and SW AES and a novel methodology 2023 (Wolf Bubberman)
• Simplicial Unrolling ElasticNet for Edge Flow Signal Reconstruction 2023 (Chengen Liu)
• Sparse & Interpretable Graph Attention Networks 2023 (Titus Naber)
• Bayesian Contrastive Learning on Topological Structures 2023 (Alex Möllers)
• GGANet: Algorithm Unrolling for Water Distribution Networks Metamodelling 2023 (Albert Solà Roca)
• Deep Statistical Solver for Distribution System State Estimation 2022 (Benjamin Habib)
• Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions 2022 (Glenn van den Belt)
• Improving cell type matching across species in scRNA-seq data using protein embeddings and transfer learning 2022 (Kirti Biharie)
• Accelerating Axial-Symmetrical Nebulae Visualization and Reconstruction 2021 (Nouri Khalass)
• Side-Channel Analysis with Graph Neural Networks, M. Sc. thesis, TU Delft, 2021 (V. de Bruijn)
• Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, M. Sc. thesis, TU Delft, 2020 (M. Pocchiari)
• Identifying Author Fingerprints in Texts via Graph Neural Networks, M. Sc. thesis, TU Delft, 2020 (T. Sipko)
• Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction, M. Sc. thesis, TU Delft, 2020 (M. Yang)
• Graph-Adaptive Activation Functions for Graph Neural Networks, M. Sc. thesis, TU Delft, 2020 (B. Iancu)
• Graph-Time Convolutional Neural Network: Learning from Time-Varying Signals Defined on Graphs, M. Sc. thesis, TU Delft, 2020 (G. Mazzola)
• Automatic Depth Matching for Petrophysical Borehole Logs 2020 (A. Garcia Manso)
• Visually grounded fine-grained speech representations learning 2020 (Tian Tian)
• Applying Machine Learning to Learn System Dynamics Models for Urban Systems 2020 (Rukai Yin)
• Active Semi-Supervised Learning For Diffusions on Graphs 2020 (Biswadeep Das)
• Interpreting Information of Deep Neural Networks for Profiled Side Channel Analysis 2020 (Marius Pop)              
• Blind Graph Topology Change Detection: A Graph Signal Processing approach 2020 (Ashvant Mahabir)