- Data Engineering – I, 2022, Uppsala University
- Operating Systems, 2021, Umeå University
- Script Programming in Python, 2020, 2021, Uppsala University
- Programming Bridging Course, C programming language, 2019, 2020, Uppsala University
- Programming Bridging Course, Python programming language, 2018, Uppsala University
- Project Course in Computational Science 2017, 2019, Uppsala University
- Numerical Optimization, 2017, 2021, Uppsala University
- Machine Learning, Fall 2013, 2014, 2015, Ghent University
Mattias Åkesson, now Data Engineer at Scaleout Systems AB
Title: Learning Phantom Dose Distribution using Regression Artificial Neural Network
Abstract: Before a radiation treatment on a cancer patient can get accomplished the treatment planning system (TPS) needs to undergo a quality assurance (QA). The QA consists of a pre-treatment (PT-QA) on a synthetic phantom body. During the PT-QA, data is collected from the phantom detectors, a set of monitors (transmission detectors) and the angular state of the machine. The outcome of this thesis project is to investigate if it is possible to predict the radiation dose distribution on the phantom body based on the data from the transmission detectors and the angular state of the machine. The motive for this is that an accurate prediction model could remove the PT-QA from most of the patient treatments. Prediction difficulties lie in reducing the contaminated noise from the transmission detectors and correctly mapping the transmission data to the phantom. The task is solved by modeling an artificial neuron network (ANN), that uses a u-net architecture to reduce the noise and a novel model that maps the transmission values to the phantom based on the angular state. The results show a median relative dose deviation ~ 1%.
Mona Mohamed Elamin, now now Software Engineer at Tink
Title: Machine Learning for the Cloud: Modeling Cluster Health using Usage Parameters
Abstract: Cloud computing platforms lie at the very heart of today’s mobile and web-based applications. Cloud service providers must satisfy computational performance agreed through service level agreements (SLA) and simultaneously keep their operational costs, clusters health, and other cloud parameters within acceptable ranges in order to achieve business success. Using traditionally available monitoring tools is not sufficient to understand in depth how these different factors affect each other. Therefore, intelligent systems able to predict operational parameters from the usage behavior of a cloud data center can potentially be beneficial. This project aims to develop an algorithmic approach that models the relationship between cloud usage parameters such as CPU and memory usage and the cloud cluster’s health parameters such as temperature. Neural network models are trained using data from different machines, and experimental results show that the models deliver promising results in terms of modeling machines’ health parameters using usage parameters.
Iliam Barkino, now Deep Tech VC at Industrifonden
Title: Summary Statistic Selection with Reinforcement Learning
Abstract: Multi-armed bandit (MAB) algorithms could be used to select a subset of the k most informative summary statistics, from a pool of m possible summary statistics, by reformulating the subset selection problem as a MAB problem. This is suggested by experiments that tested five MAB algorithms (Direct, Halving, SAR, OCBA-m, and Racing) on the reformulated problem and comparing the results to two established subset selection algorithms (Minimizing Entropy and Approximate Sufficiency). The MAB algorithms yielded errors at par with the established methods, but in only a fraction of the time. Establishing MAB algorithms as a new standard for summary statistics subset selection could therefore save numerous scientists substantial amounts of time when selecting summary statistics for approximate bayesian computation.