https://dasya.itu.dk/supervisor/ties-robroek/ Recent content in Ties Robroek on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-attribution/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-attribution/ Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We want to do this by investigating and using data attribution methods, as these techniques can help explain the effect of input data on the predictions of the model. https://dasya.itu.dk/for-students/proposals/archive/pito-data-selection/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-selection/ Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, which is part of a larger collaboration, we would like to expand our investigation of state-of-the-art data selection mechanisms from a hardware requirements and resource-efficiency perspective in addition to their impact on model accuracy. https://dasya.itu.dk/for-students/proposals/archive/pito-radt/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-radt/ Observing how well machine learning systems utilize hardware resources is a crucial preliminary step to improve system performance and reduce hardware waste. To do such observations, one has to collect a lot of monitoring data on hardware behavior through experiments. In our group, we have recently built a framework to aid the management of such monitoring data efficiently, called Resource-Aware Data Systems Tracker. There is a variety of work to be done on this framework to improve it. https://dasya.itu.dk/for-students/proposals/archive/pito-radtduckdb/ Thu, 23 Nov 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-radtduckdb/ Observing how well data-intensive systems utilize hardware resources is a crucial preliminary step to improve system performance and reduce hardware waste. To do such observations, one has to collect a lot of monitoring data on hardware behavior through experiments. In our group, we have recently built a framework to aid the management of such monitoring data efficiently, called Resource-Aware Data Systems Tracker. Currently, this framework uses Postgres data management system to keep the experimental data.