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

2020-2021

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

2019-2020

• Signal Processing | CSE2220

Master Projects

Ongoing

• Adaptive Learning on graphs, Elvin Isufi, Alex Jeleniewski (2023/2024) 
• Graph Learning for Multi-Sensor Radar, Elvin Isufi, Radu Gaghi (2023/2024) 
• Graph Neural Networks for Renewable Energy, Elvin Isufi, Rodrigo Revilla Llaca (2023/2024) 
• Hybrid Modelling in Hydrology Using a Neural Ordinary 
• Differential Equations Approach, Riccardo Taormina, Jonathan Schieren (2023/2024) 
• Graph Neural Networks for Predicting Dike-breach floods, Riccardo Taormina, Sergio Bulte (2023/2024) 
• Small Deep Learning Models for Sewer Defect Detection, Riccardo Taormina, Brendan Determan (2023/2024) 
• Improving PIV-based streamflow estimation with Deep Learning, Riccardo Taormina, Max Helmich (2023/2024) 
• GAN-based rainfall nowcasting, Riccardo Taormina, Sven van Os (2023/2024) 
• Optimizing the pump schedule of water distribution systems using a deep learning metamodel, Riccardo Taormina, Nikolaos Mertzanis (2022/2023) 

Finished

• GGANet: Algorithm Unrolling for Water Distribution Networks Metamodelling, Riccardo Taormina, Albert Roca Solà (2023/2024) 
• Sparse & Interpretable Graph Attention Networks, Elvin Isufi, Titus Naber (2023/2024) 
• An Experimental Assessment of the Stability of Graph Contrastive Learning, Elvin Isufi, Siert Sebus (2023/2024) 
• Bayesian Contrastive Learning on Topological Structures, Elvin Isufi, Alex Mollers (2023/2024) 
• Assessment of Pump Failures in Rotterdam: A Five-Year Study (2016-2020): A Failure Analysis based on statistical modelling, Riccardo Taormina, Qiwen Zhang (2023/2024) 
• Diverse Explorations of Rainfall Nowcasting with TrajGRU: Mitigating Smoothness and Fading Out Challenges for Longer Lead Times, Riccardo Taormina, Yanghuan Zou (2023/2024) 
• Towards a fully distributed multivariable hydrological deep learning model with graph neural networks, Riccardo Taormina, Peter Nelemans (2023/2024) 
• 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., Riccardo Taormina, Francesca Lena (2023/2024) 
• 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., Riccardo Taormina, Edoarto Forte (2023/2024) 
• 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., Riccardo Taormina, Agatha Zamuner (2023/2024) 
• 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, Riccardo Taormina, Josine van Marrewijk (2023/2024) 
• The Hierarchical Subspace Iteration Method for Computing Vibration Modes of Elastic Objects, Elvin Isufi, Julian van Dijk (2023/2024) 
• A System for Model Diagnosis centered around Human Computation, Elvin Isufi, Ziad Ahmad Saad Soliman Nawar (2023/2024) 
• Automatic feature discovery: A comparative study between filter and wrapper feature selection techniques, Elvin Isufi, Andrei Mân?stireanu (2023/2024) 
• Encoding methods for categorical data: A comparative analysis for linear models, decision trees, and support vector machines, Elvin Isufi, Andrei Udil? (2023/2024) 
• Filtering Knowledge: A Comparative Analysis of Information-Theoretical-Based Feature Selection Methods, Elvin Isufi, Kiril Vasilev (2023/2024) 
• Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques, Elvin Isufi, Florena Bu?e (2023/2024) 
• Perceptual losses in precipitation nowcasting, Riccardo Taormina, Diewertje Dekker (2022/2023) 
• Development of an LSTM-based methodology for burst detection in water distribution systems, Riccardo Taormina, Konstantinos Glynis (2022/2023) 
• The role of water vapor observations in satellite-based rainfall information highlighted by a Deep Learning approach, Riccardo Taormina, Fabo Curzi (2022/2023) 
• Predicting fluvial flood arrival times by making use of a deep learning model, Riccardo Taormina, Ron Bruijns (2022/2023) 
• Using YOLOv5 for the Detection of Icebergs in SAR Imagery, Riccardo Taormina, Daan Hulskemper (2022/2023) 
• Assessing the applicability of Transformer-based architectures as rainfall-runoff models, Riccardo Taormina, Kangmin Mao (2022/2023) 
• Simplicial Unrolling Elastic Net for Edge Flow Signal Reconstruction, Elvin Isufi, Chengen Liu (2022/2023) 
• From Clicks to Conscious Choices: Investigating the Effects of Carbon Footprint Data in E-Commerce Recommender Systems, Elvin Isufi, Sneha Lodha (2022/2023) 
• Graph Reqularized Tensor Decomposition for Recommender Systems, Elvin Isufi, Rohan Chandrashekar (2022/2023) 
• Pure Cold Start Recommendation by Learning on Stochastically Expanded Graphs, Elvin Isufi, Simon Dahrs (2022/2023) 
• Nudging Towards Sustainable Choices via Recommender Systems, Elvin Isufi, Raoul Kalisvaart (2022/2023) 
• Deep Learning for Geotechnical Engineering: The Effectiveness of Generative Adversarial Networks in Subsoil Schematization, Riccardo Taormina, Fabian Campos Montero (2022/2023) 
• Quantum to Transport: Modeling Transport Properties of Aqueous Potassium Hydroxide by Machine Learning Molecular Force Fields from Quantum Mechanics, Riccardo Taormina, Jelle Lagerweij (2022/2023) 
• A LSTM-based Generative Adversarial Network for End-use Water Modelling, Riccardo Taormina, Yukun Xie (2022/2023) 
• Operational Streamflow Drought Forecasting for the Rhine River at Lobith Using the LSTM Deep Learning Approach, Riccardo Taormina, Jing Deng (2022/2023) 
• cGANs for multispectral snow extent analysis in the Alps, Riccardo Taormina, Adriaan Keurhorst (2022/2023) 
• The Effect of Climate Variability on the Root Zone Storage Capacity, Riccardo Taormina, Nienke Tempel (2022/2023) 
• GNNs and Beam Dynamics: Investigation into the application of Graph Neural Networks to predict the dynamic behaviour of lattice beams, Riccardo Taormina, Lex Niessen (2022/2023) 
• The impact of an additional phenology model on the performance of conceptual hydrological models, Riccardo Taormina, Casper Pierik (2022/2023) 
• Leak Localization in Water Distribution Networks, Riccardo Taormina, Zixi Meng (2022/2023) 
• Macrolitter in Groyne Fields: Short term variability & the influence of natural processes, Riccardo Taormina, Jakob Grosfeld (2022/2023) 
• Water balance-based approach to improve understanding of Drought Development: by calculating the root storage deficit, Riccardo Taormina, Piet Storm (2022/2023) 
• APDUDS, Riccardo Taormina, Max Lange (2022/2023) 
• Improving APDUDS, Riccardo Taormina, Jip Steiger (2022/2023) 
• Self-Supervised Few Shot Learning: Prototypical Contrastive Learning with Graphs, Elvin Isufi, Ojas Shirekar (2022/2023) 
• Hardware-based implementations in Side-Channel Analysis: A comparison study of DL SCA attacks against HW and SW AES and a novel methodology, Elvin Isufi, Wolf Bubberman (2022/2023) 
• Assessing Global Applicability of a Long Short-Term Memory (LSTM) Neural Network for Rainfall-Runoff Modelling, Riccardo Taormina, Katharina Wilbrand (2021/2022) 
• Short-term Water Demand Forecasting at a District Level Using Deep Learning Techniques, Riccardo Taormina, Diego Mauricio Corredor Mora (2021/2022) 
• 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, Riccardo Taormina, Panagiotis Mavritsakis (2021/2022) 
• Exploration of Deep Learning-based Computer Vision for the detection of floating plastic debris in waterways, Riccardo Taormina, Andé J. Vallendar (2021/2022) 
• Nowcasting heavy precipitation in the Netherlands: a deep learning approach, Riccardo Taormina, Eva van der Kooij (2021/2022) 
• Deep Statistical Solver for Distribution System State Estimation, Elvin Isufi, Benjamin Habib (2021/2022) 
• Do multi-year droughts increase floods?, Riccardo Taormina, Yang Zhao (2021/2022) 
• Automatic Generation of Water Distribution Systems, Riccardo Taormina, Dimitri Tijdeman (2021/2022) 
• Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions, Elvin Isufi, Glenn van den Belt (2021/2022) 
• Improving cell type matching across species in scRNA-seq data using protein embeddings and transfer learning, Elvin Isufi, Kirti Biharie (2021/2022) 
• Tikhonov and Sobolev regularisers compared to user-based KNN collaborative filtering, Elvin Isufi, Sérénic Monté (2021/2022) 
• Total Variation Regularisation for Item KNN Collaborative Filtering: Performance Analysis, Elvin Isufi, Lars van Blokland (2021/2022) 
• The Performance of Total Variation Regularizer for User Collaborative Filtering, Elvin Isufi, Karolis Mari?nas (2021/2022) 
• Item-Item Collaborative Filtering via Graph Regularization, Elvin Isufi, Melle Jansen (2021/2022) 
• Impact of seismic wave length to detect high-magnitude earthquakes via deep learning, Elvin Isufi, Gancho Georgiev (2021/2022) 
• How long before strike can we predict earthquakes with an LSTM neural network?, Elvin Isufi, Amaury Charlot (2021/2022) 
• Impact of Focal Depth on Short-Term Earthquake Prediction using Deep Learning, Elvin Isufi, Pijus Krisiuk?nas (2021/2022) 
• Predicting Micro-Earthquakes with Deep Neural Networks, Elvin Isufi, Kevin Zhu (2021/2022) 
• Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM, Elvin Isufi, Xiangyu Du (2021/2022) 
• Earthquake Prediction: A MLP & SVM Comparison, Elvin Isufi, Daniel van den Akker (2021/2022) 
• Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction, Elvin Isufi, Maikel Houbaer (2021/2022) 
• How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?, Elvin Isufi, Irtaza Hashmi (2021/2022) 
• Parametric design of a grid shell roof over existing buildings?, with a focus on connection design, Elvin Isufi, Fiori Isufi (2021/2022) 
• Investigation of focal epilepsy using graph signal processing, Elvin Isufi, Gaia Zin (2021/2022) 
• Assessing the Capability of Multimodal Variational Auto-Encoders in Combining Information From Biological Layers in Cancer Cells, Elvin Isufi, Bram Pronk (2021/2022) 
• Benchmarking VAE latent features in downstream tasks for cancer related predictions, Elvin Isufi, Boris van Groeningen (2021/2022) 
• The Effect of Different Initialization Methods on VAEs for Modeling Cancer using RNA Genome Expressions, Elvin Isufi, Ivo Kroskinski (2021/2022) 
• Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment, Elvin Isufi, Armin Korki? (2021/2022) 
• Finding disentangled representations using VAE, Elvin Isufi, Raymond d'Anjou (2021/2022) 
• Creation of new Extra-Tropical Cyclone fields in the North Atlantic with Generative Adversarial Networks, Riccardo Taormina, Filippo Dainelli (2020/2021) 
• Side-channel analysis with graph neural networks, Elvin Isufi, Vasco de Bruijn (2020/2021) 
• Matching streamflow river gauges with hydrologic models, Riccardo Taormina, Mizzi van der Ven (2020/2021) 
• Integrated Neural Network and Finite Element Analysis for constitutive modelling of soil, Riccardo Taormina, Keshav Kashichenula (2020/2021) 
• 3D Road Boundary Mapping of MLS Point Clouds, Riccardo Taormina, Qian Bai (2020/2021) 
• Shallow Cumulus Clouds as Complex Networks, Riccardo Taormina, Pouriya Alinaghi (2020/2021) 
• The variability of the rootzone storage capacity in Austria: An exploration of its controls, Riccardo Taormina, Bart Veenings (2020/2021) 
• Relating groundwater heads to stream discharge by using machine learning techniques: A case study in subcatchment Chaamse Beken, Riccardo Taormina, Valerie Demetriades (2020/2021) 
• Mathematical framework to understand better the behavior of the graphs convolutional neural network to random perturbations, Alejandro Ribeiro, Gao Zhan (2020/2021) 
• Combining frequency information and the unsupervisedW-Net model for wheat head detection, Elvin Isufi, Ivo Chen (2020/2021) 
• Improving the Performance of Object Counting Using Training Images in the Frequency Domain, Elvin Isufi, Dani Rogmans (2020/2021) 
• Using frequency information to improve accuracy of object detectors, Elvin Isufi, Petar Ulev (2020/2021) 
• Injecting prior frequency information in DETR for wheat head detection, Elvin Isufi, Alin Prundeanu (2020/2021) 
• Accelerating Axial-Symmetrical Nebulae Visualization and Reconstruction, Elvin Isufi, Nouri Khalass (2020/2021) 
• Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, Elvin Isufi, Matteo Pocchiari (2019/2020) 
• Identifying Author Fingerprints in Texts via Graph Neural Networks, Elvin Isufi, Tomas Sipko (2019/2020) 
• Graph-Adaptive Activation Functions for Graph Neural Networks, Elvin Isufi, Bianca Iancu (2019/2020) 
• Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, Elvin Isufi, M. Pocchaiari (2019/2020) 
• Automatic Depth Matching for Petrophysical Borehole Logs, Elvin Isufi, A. Garcia Manso (2019/2020) 
• Visually grounded fine-grained speech representations learning, Elvin Isufi, Tian Tian (2019/2020) 
• Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction, Elvin Isufi, Maosheng Yang (2019/2020) 
• Graph-Time Convolutional Neural Network: Learning from Time-Varying Signals defined on Graphs, Elvin Isufi, Gabriele Mazzola (2019/2020) 
• Applying Machine Learning to Learn System Dynamics Models for Urban Systems, Elvin Isufi, Rukai Yin (2019/2020) 
• Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Issa Hanou (2019/2020) 
• Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Gwennan Smitskamp (2019/2020) 
• Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Marijn de Schipper (2019/2020) 
• Active Semi-Supervised Learning For Diffusions on Graphs, Elvin Isufi, Biswadeep Das (2019/2020) 
• Interpreting Information of Deep Neural Networks for Profiled Side Channel Analysis, Elvin Isufi, Marius Pop (2019/2020) 
• Blind Graph Topology Change Detection: A Graph Signal Processing approach, Elvin Isufi, Ashvant Mahabir (2019/2020) 
• Can fourier neural operators replicate the intrinsic predictability of spatiotemporal chaos?: for the Kuramoto-Sivashinsky system, Riccardo Taormina, Kevin Schuurman () 

• Estimating new reservoir locations with the use of a hydrological model for small holder cotton farmers in Maharashtra, India, Riccardo Taormina, Jente Janssen ()