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.

PET is quantitative, in the sense that it allows not only visualisation but also non-invasive quantification of regional tracer uptake. Specifically, with dynamic PET imaging, it is possible to fully assess the time-dependent tracer distribution in the body. This allows quantification of biological processes, such as glucose metabolism or blood flow, by using tracer kinetic modelling. Tracer kinetic modelling, however, requires accurate determination of an arterial input-function (AIF), i.e. the tracer time-activity curve in blood.

The gold-standard AIF is obtained by measuring the time-dependent FDG radioactivity concentration in arterial blood through invasive blood sampling. This procedure is complex, time-consuming, and potentially painful with risk for complications.

The aim of this Master´s project is to develop novel methodology for arterial input function prediction, building upon state-of-the-art deep learning methods. In addition, the project will focus on developing interpretability and uncertainty methods to explain outcomes and the variability in the predictions. Both preclinical (mice) and clinical (human) PET data will be explored in the project.


Prerequisites:

Contact persons

Luigi Luppino (luigi.t.luppino@uit.no)

Samuel Kuttner (samuel.kuttner@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 (benjamin.ricaud@uit.no)

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 (ahcene.boubekki@uit.no)

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 (puneet.sharma@uit.no)

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

Reference: 

[1] Ngo, P. and Godtliebsen, F., “Data-Driven Robust Control Using Reinforcement Learning,” 2020. [Online]. Available: https://arxiv.org/pdf/2004.07690.pdf.

Precise Uncertainty Quantification in Dynamic Line Rating Forecasts

Description

In this master's thesis, the student will focus on developing a model to forecast line temperature, a crucial factor in Dynamic Line Rating (DLR) for energy grids. The model will utilize sensor data (specifically line current) and weather data, including both historical and forecasted information sourced from YR APIs. The data will be pre-processed and provided to the student. The key challenge is to not only accurately forecast line temperatures but also to precisely quantify the associated uncertainties.


Objective

The main objective is to create a model that accurately forecasts line temperature by using sensor and weather data. Emphasis will be placed on quantifying and controlling the forecast's uncertainty.


Approach

Model Development: Apply machine learning techniques with a particular focus in uncertainty quantification.

Uncertainty Analysis: Develop methods to rigorously quantify the uncertainty bounds in line temperature forecasts and their subsequent influence on DLR predictions.


Potential Impact

This project will significantly enhance DLR in energy grid management by providing more accurate and reliable line temperature forecasts. Such advancements will facilitate better decision-making, improving the efficiency and dependability of power distribution networks.


Prerequisites

A solid understanding of machine learning, deep learning, and statistical analysis. Proficiency in programming, ideally in Python. Background knowledge in energy systems or meteorological data analysis is an advantage but not essential.


Contact Person

Puneet Sharma (puneet.sharma@uit.no)

Industry partner: Avju Solutions, Jonas Mørch-Lampe (jml@avju.com)