Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
Deep learning changed the landscape of many applications like computer vision, natural language processing, etc. On the other hand, deep learning require gigantic computing power offered by modern hardware. As a result data scientists rely on powerful hardware resources offered by shared high-performance computing (HPC) clusters or the cloud. Due to the long-running times of deep learning …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2023
Tags: machine learning systems, checkpointing, scheduling, resource management
Traditionally solid-state drives (SSDs) does not give the users the ability to control the data placement on the SSD. This often leads to suboptimal performance and lowers SSD lifetime, since SSDs internally don’t allow in-place updates. The updated disk pages are written elsewhere and the old versions have to be garbage collected. This poses problems if data with different lifetimes and …
Supervisors:
Pınar Tözün
Semester: Fall 2023
Tags: SSDs, data management systems, modern storage
Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors:
Pınar Tözün, Robert Bayer
Semester: Fall 2023
Tags: resource-constrained hardware, data management, ML model updates, tinyML
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 …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2023
Tags: benchmarking, data management, data visualization
Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors:
Pınar Tözün, Robert Bayer
Semester: Fall 2023
Tags: resource-constrained hardware, data management, resource management, tinyML
Deep learning changed the landscape of many applications like computer vision, natural language processing, etc. On the other hand, deep learning require gigantic computing power offered by modern hardware. As a result data scientists rely on powerful hardware resources offered by shared high-performance computing (HPC) clusters or the cloud. Due to the long-running times of deep learning …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2023
Tags: machine learning systems, checkpointing, scheduling, resource management
Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors:
Pınar Tözün, Robert Bayer
Semester: Fall 2023
Tags: resource-constrained hardware, data management, ML model updates, tinyML
Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors:
Pınar Tözün, Robert Bayer
Semester: Fall 2023
Tags: resource-constrained hardware, data management, resource management, tinyML
The DISCO-2 satellite will have accelerated machine learning capability based on the inclusion of a Coral TPU ML accelerator module. This will allow images taken by the satellite to be analaysed on satellite using a variety of ML models, with only select images sent back to Earth. This approach allows for more flexibility in image aquisition and saves downlink bandwidth which is very constrained …
Supervisors:
Julian Priest, Robert Bayer
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
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 …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2023
Tags: benchmarking, data management, data visualization
There is pressure on hospitals to implement AI systems which promise to improve diagnoses and save time for the doctors. One use-case could be related to the automation of protocoling based on a physician referral. Currently, this requires a referral letter from a physician who has examined a patient and evaluates that there is a need for additional imaging studies. In this case, the physician …
Supervisors:
Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis
Machine learning models, especially larger models that are used in for example image or text datasets, can be expensive to train. During development models are usually trained multiple times for example to optimize hyperparameters, which can result in a large carbon footprint.
This project specifically focuses specifically on medical data. There are some recent efforts, for example by Selvan et …
Supervisors:
Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, resource consumption
There have been several situations where machine learning classifiers, trained to diagnose a particular disease (for example, lung cancer from chest x-rays), overfit on hidden features within the data. Examples include gridlines, surgical markers or evidence of treatment or text present in the images (see references for examples). This causes the classifier to fail on other type of images. …
Supervisors:
Veronika Cheplygina, Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embeded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Julian Priest, Sebastian Büttrich
Semester: Fall 2023
Tags: satellite, LoRa, cubesat, IoT, embeded, electronics
ITU is a partner of the Danish Student Cubesat Program, DISCOSAT. We launched our first satellite DISCO-1 into Low Earth Orbit in April 2023 and we will launch a second DISCO-2 in 2024. In this project you will gain experience with automating live satellite operations and communications, completing a groundstation at the Rued Langaards Vej site for use with both satellites.
The DISCO satellite …
Supervisors:
Julian Priest
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
The DISCO-2 satellite is an Earth observation satellite in collaboration with the Arctic Research Center in Aarhus and is designed to complement ground based field studies in Greenland. The satellite instrument consists of 2 high quality visible light and 1 infrared cameras, as well as and attitude control system and coral TPU ML coprocessor.
In this project you will develop software to control …
Supervisors:
Julian Priest
Semester: Fall 2023
Tags: satellite, climate change, image processing, ML, csp, embedded, space
The DISCO-2 satellite will have accelerated machine learning capability based on the inclusion of a Coral TPU ML accelerator module. This will allow images taken by the satellite to be analaysed on satellite using a variety of ML models, with only select images sent back to Earth. This approach allows for more flexibility in image aquisition and saves downlink bandwidth which is very constrained …
Supervisors:
Julian Priest, Robert Bayer
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embeded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Julian Priest, Sebastian Büttrich
Semester: Fall 2023
Tags: satellite, LoRa, cubesat, IoT, embeded, electronics
Geospatial data refers to information that is tied to specific geographic locations on the Earth’s surface. It includes both the location coordinates (such as latitude, longitude, and, potentially, altitude) and attribute data associated with those locations. Geospatial data is categorized into two types: raster and vector.
Vector data represents geographic features as points, lines, and …
Supervisors:
Eleni Tzirita Zacharatou
Semester: Fall 2023
Tags: spatial data analysis, data science, data loading, GIS file formats, geospatial data
Concept Bottleneck Models [1] are designed to leverage high-level concepts. They revisit the classic idea of first predicting concepts that are providing at training time, and then using these concepts to predict the label. By construction, it is possible to intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging
A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning
There have been several situations where machine learning classifiers, trained to diagnose a particular disease (for example, lung cancer from chest x-rays), overfit on hidden features within the data. Examples include gridlines, surgical markers or evidence of treatment or text present in the images (see references for examples). This causes the classifier to fail on other type of images. …
Supervisors:
Veronika Cheplygina, Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such …
Supervisors:
Dovile Juodelyte
Semester: Fall 2023
Tags: transfer learning, deep learning, medical imaging