Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embedded 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:
Sebastian Büttrich
Semester: Fall 2024
Tags: satellite, LoRa, cubesat, IoT, embedded, electronics
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embedded 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:
Sebastian Büttrich
Semester: Fall 2024
Tags: IoT, LoRa, LoRaWAN, satellites
Optical fiber is the backbone of the internet’s communication, e.g. in the form of submarine fiber cables. It can also be employed as a sensor device, by means of combined opto-acoustic methods such as Distributed acoustic sensing (DAS) or State of Polarisation (SoP) sensing. Fiber is cabapble of sensing all kinds of vibrational/acoustic events, from animal sounds over seismic activity to …
Supervisors:
Sebastian Büttrich
Semester: Fall 2024
Tags: fiber, acoustics, audio, machine learning", DAS, SOP
Spectral learning priority is a useful tool in analyzing a model’s focus during training, it describes how a model may understand a given image from the spectrum perspective. For example, to distinguish cats and tortoises, learning to recognize their shapes would be enough, such embedding will result in higher learning priority at low frequencies representing shapes; while learning to …
Supervisors:
Yucheng Lu, Veronika Cheplygina
Semester: Fall 2024
Tags: Spectral analysis, Image classification, Medical imaging
The DISCO-2 project is driven by students and aims to develop and deploy a 3-unit CubeSat into low Earth orbit. Its mission focuses on conducting Earth observations over Greenland and supporting various research objectives. The satellite has three cameras onboard: infrared, wide-angle, and standard (main camera). Due to the limitations of the imaging hardware and the challenging conditions on the …
Supervisors:
Yucheng Lu, Julian Priest
Semester: Fall 2024
Tags: Image enhancement, Image processing, Machine learning
Spectral learning priority is a useful tool in analyzing a model’s focus during training, it describes how a model may understand a given image from the spectrum perspective. For example, to distinguish cats and tortoises, learning to recognize their shapes would be enough, such embedding will result in higher learning priority at low frequencies representing shapes; while learning to …
Supervisors:
Yucheng Lu, Veronika Cheplygina
Semester: Fall 2024
Tags: Spectral analysis, Image classification, Medical imaging
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
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 2024
Tags: machine learning, data science, medical imaging
The DISCO-2 project is driven by students and aims to develop and deploy a 3-unit CubeSat into low Earth orbit. Its mission focuses on conducting Earth observations over Greenland and supporting various research objectives. The satellite has three cameras onboard: infrared, wide-angle, and standard (main camera). Due to the limitations of the imaging hardware and the challenging conditions on the …
Supervisors:
Yucheng Lu, Julian Priest
Semester: Fall 2024
Tags: Image enhancement, Image processing, Machine learning
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 …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: benchmarking, data management, data visualization
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 2024
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 2024
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 2024
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 2024
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 2024
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 2024
Tags: resource-constrained hardware, data management, resource management, tinyML
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 …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: benchmarking, data management, data visualization
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 2024
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 2024
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 2024
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 2024
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 2024
Tags: resource-constrained hardware, data management, resource management, tinyML
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
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 2024
Tags: machine learning, data science, medical imaging
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
Are you interested in working with a big data open source project?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For a general overview …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: big data, database, cross-platform data processing, open source, Apache
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model (typically a regression model) that estimates the runtime of a query …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: machine learning, database, query optimization, ranking
Are you interested in working with a big data open source project and help the environment?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: big data, database, cross-platform data processing, open source, Apache
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model that estimates the runtime of a plan. However, to build such a model …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: machine learning, training data, query optimizer