Project Proposals


Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive

Subjects
Supervisors
  1. Veronika Cheplygina
  2. Amelia Jiménez-Sánchez
  3. Pınar Tözün
  4. Bethany Chamberlain
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Julian Priest
  7. Sebastian Büttrich

Supervisor: Veronika Cheplygina

PROPOSAL

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 …
Supervisors: Veronika Cheplygina
Semester: Spring 2023
Tags: machine learning, medical imaging, data analysis, resource consumption

PROPOSAL

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
Semester: Spring 2023
Tags: machine learning, data science, medical imaging

PROPOSAL

GANs have been proposed for generation of synthetic cell image data [1], or in other words data augmentation. We want to perform a critical survey of the field of GANs as used for data augmentation and examine some alternatives. We believe that the notion that GANs are generating “new” data should be challenged; it in fact generates a variation of the data it is being fed for training …
Supervisors: Veronika Cheplygina
Semester: Spring 2023
Tags: machine learning, data science, medical imaging


Supervisor: Amelia Jiménez-Sánchez

PROPOSAL

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 2022
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning


Supervisor: Pınar Tözün

PROPOSAL

It is common to process data to clean it, filter it, restructure it, get metadata out of it, etc. before feeding the data into a data analysis or machine learning pipeline. There are many tools and libraries out there to aide with this process with different strengths and functionality (DALI, RAPIDS, HoloClean, DAPHNE, DuckDB, etc.). In this project, we would like to analyze pros/cons of some of …
Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: data preprocessing libraries, heterogeneous hardware, machine learning

PROPOSAL

In the past decade, data management community has focused on main-memory systems or main-memory-optimized systems. This focus has put the commodity memory hierarchy (DRAM and processor caches) into center when it comes to workload characterization studies. Today, with the evolution of persistent storage technologies such as NVRAM (persistent memory solution of Intel) and NVMe SSDs, data systems …
Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: workload characterization, tracing, modern storage, data-intensive systems

PROPOSAL

DAPHNE is an EU project that aims at building a data system targeting integrated data analysis pipelines across data management and processing, high-performance computing (HPC), and machine learning (ML) training and scoring. The project had its first code release back in March. This project aims at adding a profiling infrastructure for DAPHNE codebase. If you are interested in learning about …
Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: integrated data analysis pipelines, profiling big data systems

PROPOSAL

State-of-the-art machine learning models are known to be compute- and power-hungry. On the other hand, modern servers come equipped with really powerful CPU-GPU co-processors. Not all machine learning models are able to use all the available hardware resources on such servers. Workload collocation is a mechanism to increase hardware utilization when a single workload is not able to utilize all the …
Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: benchmarking, workload collocation, machine learning

PROPOSAL

Today, there are many compute- and memory-hungry data-intensive workloads from big data analytics applications to deep learning. These workloads increasingly run on shared hardware resources, which requires building hardware resource managers that can both serve the needs of workloads and utilize hardware well. Predicting the resource utilization of applications can aid such resource managers …
Supervisors: Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2022
Tags: benchmarking, hardware resource consumption estimation, machine learning


Supervisor: Bethany Chamberlain

PROPOSAL

I am open to supervising projects relating to (1) surveying neuroimaging datasets and related challenge datasets (e.g., on Kaggle), (2) using qualitative methodology to explore how researchers engage with challenge datasets, or (3) collecting data toward an existing project on conference similarity. Since these are broad ideas, you should schedule a meeting to share your own ideas or proposal(s). …
Supervisors: Bethany Chamberlain
Semester: Fall 2022
Tags: datasets, decision making, neuroimaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab

PROPOSAL

Today, there are many compute- and memory-hungry data-intensive workloads from big data analytics applications to deep learning. These workloads increasingly run on shared hardware resources, which requires building hardware resource managers that can both serve the needs of workloads and utilize hardware well. Predicting the resource utilization of applications can aid such resource managers …
Supervisors: Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2022
Tags: benchmarking, hardware resource consumption estimation, machine learning


Supervisor: Julian Priest

PROPOSAL

ITU is a partner in the Danish Student Cubesat Program, DISCO which will launch a series of small satellites into orbit, starting with DISCO 1 in 2023 and followed by DISCO2 in 2024. As part of this project ITU is installing a satellite ground station with a range of antenna rotators on the roof of Rued Langaards Vej building and the equipment has been purchased. The ground station will track the …
Supervisors: Julian Priest
Semester: Fall 2022
Tags: Satellite, Image processing, Edge, Constrained Computing, Networks, Machine Learning, Embeded, Software Defined Radio

PROPOSAL

ITU is a partner in the Danish Student Cubesat Program DISCO, which will launch a series of small satellites into orbit, starting with DISCO 1 in 2023 and followed by DISCO2 in 2024. ITU is developing a hi-res multi camera imaging payload for earth observation primarily in the Arctic. We are developing an on satellite machine learning capability using an ML coprocessor, as well as models that can …
Supervisors: Julian Priest
Semester: Fall 2022
Tags: Satellite, Image processing, Edge, Constrained Computing, Networks, Machine Learning, Embeded, Radio


Supervisor: Sebastian Büttrich

PROPOSAL

Invasive bird species can be a serious problem in cities, towns and in agriculture. The common pigeon is a very unwelcome guest on many balconies, roofs, terraces. Conventional scarecrows often show no effect, as these birds are known to be quite intelligent, and capable of learning fast. The idea is to built a sensor/camera enhanced scarecrow that - can recognize birds present within its …
Supervisors: Sebastian Büttrich
Semester: Fall 2022
Tags: IoT, ML, machineLearning, sensors, security

PROPOSAL

In LoRaWAN networks such as The Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Fall 2022
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

PROPOSAL

There is currently a lot of progress in really small, yet powerful visual machine learning / computer vision, on hardware like the OpenMV Cam H7, Arduino Portenta Vision Shield, Luxonis LUX-ESP32, Himax WE-I Plus, Arducam Pico4ML, and Raspberry Pi, and on software platforms such as TinyML or OpenMV IDE. While many popular use cases stem from fields like traffic analysis, wildlife monitoring, we …
Supervisors: Sebastian Büttrich
Semester: Fall 2022
Tags: IoT, sensors, machine learning, computer vision

PROPOSAL

Deliberately scoped very wide, this group contains a number of projects in different possible directions, from Location services via LPWAN time-of-flight and GPS/GNSS, Vessel tracking and management in fisheries, tourism and logistcs Water quality anc chemistry sensing for Aquaculure, specifically Mariculture, Wave and tidal dynamics, e.g. in energy harvesting and variations/combinations of …
Supervisors: Sebastian Büttrich
Semester: Fall 2022
Tags: Satellite, Image processing, Machine Learning, edge, constrained computing, IoT, sensors, location