Simulation of Nanopore sequencing (with generative models)
The aim of this project is to develop a model that can simulate (generate) Nanopore data. Specifically, we would like to develop a model that can capture and...
Open projects
2021
Running projects
2021
Deep generative model for simulation and interpretation of miRNA
Lack of interpretability is a key obstacle in adoption of ML models. Recent work on pre-miRNA detection has received good results. Interpreting the behavior ...
Aspect-based Few-shot classification (Meta-learning)
The goal of this project is to extend the capabilities of meta-learning models towards aspects-based meta-learning with Psychometric testing.
2020
Trackster Pruning at the CMS High-Granularity Calorimeter
Processing high dimesional graph data from particle collisions in LHC
Simulation of bubbles with front tracking and deep generative models [Under construction]
Simulation of bubbles with graph neural networks
Finished projects
2021
Language modeling for improving maintenance services for medical devices
The goal of this project is to assist service engineers during remote maintenance activities of medical systems. When a system exhibits a problem, service en...
2020
ML for Scanning Electron Microscopy with Polarization Analysis
The goal of this project is developing machine learning models that can support the analysis of Scanning Electron Microscopy with Polarization Analysis (SEMP...
2019
Aligned classification of sequences (Umbrella topic)
The overall goal of this project is to develop a model for aligned sequence classification.
Real time monitoring of the quality of 3D printed products with computer vision (Signify)
At Signify, we are investigating the application of vision-based solutions to prevent delamination during the 3D printing process. Several cameras capturing ...
Improving automated visual quality control of 3D-printed products (Signify)
At Signify we are investigating and implementing automated visual quality control of our 3D-printed products in order to improve our quality control and to l...
Machine learning models for chaotic systems
Recent evidence shows that neural networks can quantify properties of non-linear chaotic physical systems, such as fluid turbulence, far better than the curr...
2017
Solving the Train Unit Shunting Problem with Deep Reinforcement Learning
Project of Evertjan Peer
Learning to orient from video pixels
Deep learning in unsupervised settings for computer vision in robotics
Textual Data Feature Evaluation – Scalable Framework Exploration
MSc project of Yue Wang
Daily activity tracking with recurrent neural networks
Project of Jeroen v/d Ven
Active learning in latent space
Project of Loek Tonnaer
Using semantic role labels for...
Project of Puck Mulders
Medical Image Analysis with deep generative models
Project of Rodrigo Mendoza
Pose estimation with deep convolutional neural networks
Project of Boshen Lyu
Evolutionary training of deep neural networks
Project of Marijn Knippenberg
Optimizing performance of a localization model by transfer learning using a novel autoencoder-like architecture
Project of Christoforos Boukouvalas
Dimensionality reduction of epigenetic data using Deep Autoencoders
Project of Sako Arts
Internship - Live Sleep Stage classification with CNN’s and RNN’s
Project of Sako Arts
Fault Detection in Protein Microarrays Using Deep-Learning-Based Image Analysis
Project of Christopher Ankomah