Master's thesis projects
If you are enrolled in a master's program and interested in writing your thesis in collaboration with the UiT Machine Learning Group, we propose some interesting projects you can get involved with. You may also contact directly one of the group members if you wish to work on a topic that is not listed here.
Deep learning aided quantification in PET imaging
Positron emission tomography (PET) is a medical imaging technique that visualises the distribution of an injected radioactive tracer in living subjects. Medical imaging with PET plays an important role in the detection, staging, and treatment response assessment of many diseases, including cancer, neurological and cardiovascular conditions, as well as inflammation and infection.
Mutual information and deep learning
Contact person: Shujian Yu (UiT, shujian.yu@uit,no)
Graph neural networks
Graph neural networks are very powerful tools but have some limitations, like over-smoothing and over-squashing (https://towardsdatascience.com/over-smoothing-issue-in-graph-neural-network-bddc8fbc2472, https://towardsdatascience.com/over-squashing-bottlenecks-and-graph-ricci-curvature-c238b7169e16 ). The project will explore the causes of these limitations and try to propose solutions.
Recommended prerequisites: Knowledge of machine learning and signal/image processing. Good programming skills (Python).
Contact person: Benjamin Ricaud (email@example.com)
Hidden Markov Model Time Series Segmentation
Hidden Markov model remains the most used model for time series segmentation. Its main advantage, discrete hidden states and observations is also its main limitation. Of course, there have been several works extending to model to more versatile distributions. Our objective will be to train a recurrent network to segment time series by enforcing sparsity of the latent representation. All that in an unsupervised manner of course. We will investigate what does the model "naturally" extract and eventually how to guide it. Possible applications include medical data, electrical usage, NLP, and many others.
Recommended prerequisites: FYS-3012, FYS-3033
Contact person: Ahcene Boubekki (firstname.lastname@example.org)
Population counting using Drone Images for Marine Surveys
Marine surveys require use of valuable resources (expert's time and boats). UiT in collaboration with Norwegian Polar Institute and University of Southern Denmark is working towards developing a solution for performing population counting based on images captured from flying a drone. The initial plan is to develop a supervised learning based methodology for detecting the number of porpoises in an image. Later on, the plan is to further develop the framework to accommodate for other similar mammal species (with fewer training samples).
Prerequisites: FYS-2021, FYS-3033
Contact person: Puneet Sharma (email@example.com)
Safe AI using Bayesian Deep Learning
Current decision support tools are usually designed by using expert knowledge or data driven techniques. However, these methods are mostly dependent on the high level of understanding of the subject or a dataset with unrealistic high quality to achieve optimal or desired performances. Many real-world problems are highly complex, which require new techniques that can model uncertainties and making decisions based on the availability and quality of data. Approaches toward building a personalized decision support tools include developing a prediction model of the risk and outcome, or deriving safe and effective data driven decision algorithms. With the development of artificial intelligence, deep learning has been used extensively in modelling and prediction. The combination of deep learning with Bayesian inferencing allows information and uncertainties to be accurately estimated from the training data. The AI agent needs to be designed carefully such that it can safely explore the environment and propose actions that are both risk-averse and robust. Integrating deep learning, Bayesian inferencing with reinforcement learning framework will bring great opportunities to solve the problem and contribute toward a safe AI.
Background: A background in Bayesian inference, deep learning and reinforcement learning would be ideal, but a general background in machine learning and statistical methodology will be sufficient. Good programming skills are required.
Contact persons: Fred Godtliebsen, UiT Machine Learning Group and Phuong Ngo, Norwegian Centre for E-health Research
 Ngo, P. and Godtliebsen, F., “Data-Driven Robust Control Using Reinforcement Learning,” 2020. [Online]. Available: https://arxiv.org/pdf/2004.07690.pdf.
Robustness and transferability in CNNs
The issue of investigating robustness and transferability in convolutional neural networks (CNNs) has long been studied concomitantly with the success of a variety of architectures. The motivation for studying this problem lies in the fact that CNNs typically perform poorly when the testing dataset distribution significantly differs from the training set and have been shown to pick up spurious correlations in the training data (essentially getting "fooled").
This project aims to investigate mechanisms to quantify such spurious correlations and evaluate the models robustness and aims to shed more light on an extremely active area of research in deep learning.
FYS-3012 Pattern Recognition, FYS-3033 Deep Learning. Good programming skills.
Michael Kampffmeyer (firstname.lastname@example.org)
Rwiddhi Chakraborty (email@example.com)