Members

Anne Brugård is a PhD Research Fellow at the University of Oslo. Her research lies in algebraic geometry, in particular extensions of Arithmetically Cohen-Macaulay varieties of codimension 3 and 4.

Daria Barjaktarevic is a Ph.D. student at the Department of Computer Science, Norwegian University of Science and Technology. She is currently involved in the project “InterOpt: Deep integration between machine learning approaches and renewable energy optimization”. The focus of her work is machine learning enhanced solvers of Mixed Integer Programs and Decision Focused Learning.

I'm a PhD candidate at the Department of Mathematics and Statistics and the Machine Learning Group at UiT and work on uncertainty estimation in deep learning for object classification with a focus on subsea mapping applications. The main goal of my PhD project is to develop fast uncertainty estimation methods that can be used in real-time settings, while still producing accurate and reliable estimates. Another topic we want to investigate in this project is how synthetic data can be used to understand and improve uncertainty estimation in deep learning-based classification tasks. I have a B.Sc. and M.Sc. degree in physics from Friedrich-Schiller University, Jena, Germany. Before starting my PhD, I spent several years as a research scientist in the private sector, working with hyperspectral imaging systems and applied data analysis for remote sensing and industry applications. My research interests lie within signal and image processing, machine learning and Bayesian uncertainty estimation.

My name is Elling, and I am a PhD student at NTNU. My background is from applied mathematics, where I focused on spatial statistics and numerical methods. Currently, I work on spatio-temporal modeling and machine learning, with applications to geophysical data. A particular passion is writing computationally efficient code and experimenting with modern frameworks and languages. Hopefully, the Mathesis program will be a good way to meet and learn from other researchers with similar interests.

Cardiovascular disease is the world’s leading health challenge, and HubMOL project on the impact of CoA metabolism on cardiac electrical activity is both scientifically fascinating and advancing clinical understanding and improving patient care. I am excited to contribute to this research, as it aligns with my background and research interests in cardiac modeling, and fits perfectly with my long-term ambition to advance computational modeling for better healthcare.

My earlier work with FEniCS and cardiac modeling also motivated me to join Simula, given its leading role in FEniCS development and strong expertise in the cardiac domain. Outside of research, I enjoy running and soccer, and I am eager to learn skiing and swimming during my time in Norway.

I recently defended my PhD in cryptography, focusing on the cryptanalysis of symmetric primitives for advanced protocols. I have a background in pure mathematics, with a particular interest in algebra, and my doctoral work combined rigorous mathematical methods with practical questions in modern cryptography. In recent years, my interests have expanded beyond research itself toward research coordination, project management, and administrative processes related to academic environments. I am especially motivated by supporting research teams, contributing to strategic development, and engaging in outreach and popular science communication to make complex topics more accessible.

I am a PhD student at UiB at the Selmer center. My research is in cryptography, where I mainly work with algorithms in symmetric cryptography and boolean fuctions. My background is from mathematics and algorithms.

I'm a PhD student in the machine learning group at the University of Bergen. I want to understand the specific mechanisms that take place inside trained neural networks as they perform tasks, i.e. mechanistic interpretability. To do this, I'm currently investigating techniques based on sparse dictionary learning.

I am a PhD candidate at Simula UiB in Bergen, working in post-quantum cryptography. My research focuses on efficient and secure implementations of post-quantum algorithms. More broadly, I am interested in the intersection of cryptography, hardware security, and the practical deployment challenges of post-quantum systems.

I hold a Master of Science degree in Data Engineering and Analytics from the Technical University of Munich (TUM). Passionate about leveraging artificial intelligence in the medical domain, I have contributed to numerous AI research projects in healthcare and medicine, gaining extensive experience across various fields. Currently I am doing my PhD at UiT on advance representation learning techniques, with a particular emphasis on developing novel similarity measures and clustering methods. My research explores how relationships, extracted from complex datasets such as spatial omics, can capture intricate dependencies beyond pairwise interactions, offering a richer understanding of medical and biological data. Given the challenges posed by varying structures and labelled samples, my work aims to incorporate underlying semantic relationships through knowledge embeddings and non-Euclidean geometries. By leveraging these advanced techniques, seeking to develop more meaningful similarity measures that enhance the analysis of medical and healthcare data, ultimately contributing to improved predictive modelling and decision-making in clinical and biomedical applications.

I am a PhD student in algebraic geometry, working on questions related to degenerations and mirror symmetry, particularly within the framework of the Gross-Siebert program. I am currently studying the relationship between conifold transitions and mirror symmetry. More broadly, I am interested in algebraic geometry and its interactions with related areas, and I look forward to fruitful discussions within the Mathesis network.