PhD students in interdisciplinary mathematics
Current PhD students
Ask Ellingsen
 Department: Mathematics
 Admitted: 2022
 Project description: Broadly, I do mathematical quantum physics. Specifically, my PhD project focuses on an exotic type of particle behaviour called fractional quantum statistics, and on anyons, the particles that display this behaviour. Anyons can be thought of as lying 'between' bosons and fermions, and only appear in certain twodimensional systems. We are interested in the fundamental mathematical description of anyons, as well as predicting physical properties; especially of the anyon gas.
Yoann Sohnle
 Department: Physics and Astronomy
 Admitted: 2022
Hannes Gustafsson
 Department: Chemistry  Ångström
 Admitted: 2022
David Meadon
 Department: IT
 Admitted: 2022
 Project description: My PhD project involves the spectral analysis of block Toeplitz and Toeplitzlike matrices. More specifically, to investigate using the theory of generalised locally Toeplitz (GLT) sequences and matrixless methods to study the eigenvalues and eigenvectors of different Toeplitz(like) matrices. For example, these types of matrices arise in the discretisation of differential equations and understanding their spectral properties is important for analysing existing and designing new numerical methods and solvers.
Gesina Menz
 Dept: IT
 Admitted: 2022
 Project description: My PhD project is aiming at modelling living cells in a datadriven way. Since this is a very broad and intensive task, two specific projects I am working on right now include investigating how cell signalling works mechanistically as well as exploring how blood vessel formation can be modelled. Longterm, the focus is mechanistic modelling of different aspects of cell behaviour on a population level.
Andreas Michael
 Dept: IT
 Admitted: 2021
 Project description: My PhD project is part of the larger research project INVIVE which aims to create realtime simulations of the respiratory function of an ICU patient. In my PhD I focus on the simulation of the human diaphragm. This includes creating an accurate model for muscle tissue which accounts for muscle fibre orientation and activation dynamics. Additionally, it includes numerically solving the model equations using methods based on radial basis functions.
Sanya Karilanova
 Dept: Electrical Engineering
 Admitted: 2021
 Project description: Signal Processing with Spiking Neural Networks (SNN). One aspect of this project is to research the development of efficient training and learning method using SNN as that is the current challenge regarding SNN. Furthermore, applying the developed SNN to a data produced by electronic skin.
Li Ju
 Dept: IT
 Admitted: 2021
 Project description: My PhD project focuses on federated machine learning and distributed machine learning. The aim of the project is to develop distributed optimization algorithms with better convergence for federated training, and to enhance the security of federated learning with differential privacy. With algorithmic insights, the project also aims to improve organ segmentation and tumor segmentation for radiation treatment planning with federated learning.
Swarnadip Chatterjee
 Dept: IT
 Admitted: 2021
 Project description: My PhD project is about improving the understanding of the disease and its progression, as well as enabling reliable early detection of cancer. The project will explore and develop techniques for multimodal information fusion, utilizing combinations of several imaging modalities to maximize information gain.The project combines the power of modern deep learning techniques with novel distance measures between images, to create new methods for efficient fusion of multimodal image data.
Alfred Andersson
 Dept: Cell and Molecular Biology
 Admitted: 2020
 Project description: To predict chemical properties, it is crucial to find accurate energy potential functions that describe the interactions between the atoms involved. In reality we do not know what the best representation of these would be and few improvements have been made since the first models were introduced 50 years ago. Today we have access to large datasets and we will use these to find an even more accurate representation.
Marc Fraile Fabrega
 Dept: IT
 Admitted: 2020
 Project description: Explainable deep learning methods for humanhuman and humanrobot interaction HumanHuman Interaction (HHI) relies on implicit signals, such as mimicry (copying each other's actions and displays of emotion) and synchronization (performing these displays in unison). These skills are currently lacking in social robots. This project aims to leverage advances in Deep Learning to (1) predict alignment in HHI, (2) analyse learned models to discover relevant forms of interaction, and (3) apply these models to HumanRobot Interaction (HRI).
Roman Mauch
 Dept: Physics and Astronomy
 Admitted: 2020
 Project description: I am working on supersymmetric quantum field theories from a more mathematical perspective, using techniques like localisation to perform exact computations. At the moment, I am particularly interested in cohomologically twisted N=2 theories in four dimensions and their relation to the AGT correspondence.
Lisanne Knijff
 Dept: Chemistry
 Admitted: 2020
 Project description: The metal oxide/electrolyte interface plays an important role in energy storage systems. However, such interfaces are difficult to model due to their large number of atoms and high complexity. The aim of this project is to develop a physically constrained atomic neural network to accurately simulate the metal oxide/electrolyte interface in order to study their electrical and mechanical properties.
Olga Sunneborn Gudnadottir
 Dept: Physics and Astronomy
 Admitted: 2019
 Project description: The LHC is the most energetic particle collider ever built, and the ATLAS experiment collects data from the collisions it produces. The data are then analysed to measure properties of elementary particles and to search for new phenomena that can help explain for example Dark Matter. In this project, a search for Dark Mesons in ATLAS data is conducted, and data science methods are developed to aid in the search.
Daniel Panizo Pérez
 Dept: Physics and Astronomy
 Admitted: 2019

String theory is a powerful frame… where, to get cosmological models that “resemble” our universe, seems a dreadfully hard task. Making use of “stringy” features, one can adapt models to overcome these difficulties. To “sandwich” a four dimensional (4D) universe between two different higher dimensional spacetime regions is the starting point of my PhD project. To understand how the “bread" attributes project down to 4D cosmos peculiarities, while turning on quantum properties, my research line.
Håkan Runvik
 Dept: IT
 Admitted: 2019
 Project description: In this project we describe biomedical systems using hybrid models, i.e. models where both continuous dynamics and discrete events are present. We assume a model structure consisting of a linear plant with input in the form of a sequence of impulses, which for some applications is determined by a feedback law. Subjects such as estimation, system identification and feedback design are considered.
Lukas Lundgren
 Dept: IT
 Admitted: 2018
 Project description: My PhD project is about highorder accurate simulation of variable density incompressible flow. One aim is to develop stabilization techniques to suppress sharp gradients present in the solution using artificial viscosity while still ensuring that the solution is highorder accurate where the solution is smooth. Another aim is to develop efficient solution algorithms that are wellsuited for largescale parallel infrastructures.
Carmina Fjellström
 Dept: Mathematics
 Admitted: 2018
 Project description: So far I’ve had different projects on mathematical modelling, so it’s a bit difficult to summarize them all into one. The first projects were on time series analysis using machine learning techniques and examining stochastic gradient descent using a manifoldlearning method. I am also currently working on projects involving the mathematics of tugofwar and corresponding operators. For my final project, the plan is to work on blockchains and cryptocurrencies, although the problem to be tackled is still to be discussed.
Rebekka Müller
 Dept: Mathematics
 Admitted: 2017
 Project description: In my PhD project I develop stochastic models for processes in molecular evolution. More precisely, I study the interplay of mutation, natural selection and chance, and their relative contribution to evolution. The aim is to provide theoretical tools that help to design and interpret empirical estimation of molecular signatures of e.g. natural selection, divergence or demography. Generally, from such theoretical models one can gain conceptual understanding of evolution.
Esteban Velez
 Dept: Chemistry
 Admitted: 2017
Michael Weiss
 Dept: Geo Sciences
 Admitted: 2017
 Project description: My research is concerned with the development
and implementation of an efficient and effective 3D forward modelling algorithm based on the spectral element method for geophysical applications in controlledsource electromagnetics. One of the core aspects focuses on the investigation of iterative solution methods and suitable as well as efficient preconditioning techniques to solve the linear system of equation as costeffective as possible.
Elisabeth Wetzer
 Dept: IT
 Admitted: 2017
 Project description: My research focuses on general method development in image processing, in particular for biomedical applications. This includes texture recognition, shape description, image registration, classification, image retrieval, and currently representation learning and equivariant networks for multimodal data. Applications of my research include screening programs for early oral cancer detection, or registration of images captured by different microscopy modalities to combine the information of these different imaging techniques.
David Widmann
 Dept: IT
 Admitted: 2017
 Project description: My research focuses around uncertainty quantification of probabilistic models. More broadly, I work on statistical inference, mathematical modeling, scientific machine learning, and probabilistic programming languages. Recently, I have been studying the analysis and evaluation of calibration, and developed new calibration measures, estimators, and tests based on reproducing kernel Hilbert spaces which are available also as software libraries for Julia, Python, and R. Additionally, I actively maintain many Julia packages related to my research interests.
Linnéa Gyllingberg
 Dept: Mathematics
 Admitted: 2016
 Project description: My research focuses on developing and analysing mathematical models that are used to describe different types of biological interactions. From selfpropelled particle models capturing the collective motion of fish schools to models in mathematical neuroscience describing the interactions between neurons to individualbased models of ecological interactions.
Eva Breznik
 Dept: IT
 Admitted: 2016
Former PhD students
Mathew Magill
 Dep: Physics and Astronomy
 Admitted: 2017
 Project: Aspects of vacuum moduli in string theory
Marcus Westerberg
 Dept: Mathematics
 Project: Prostate cancer incidence, treatment and mortality: Empirical longitudinal registerbased studies and methods for handling missing data
Ylva Rydin
 Dept: IT
 Project: Finite Difference Methods for TimeDependent Wave Propagation Problems
Kristiina Ausmees
 Dept: IT
 Projekt: Methodology and Infrastructure for Statistical Computing in Genomics: Applications for Ancient DNA
Fredrik Wrede
Akshay Krishna Ammothum Kandy
 Dept: Chemistry
 Project: Linear models for multiscale materials simulations: Towards a seamless linking of electronic and atomistic models for complex metal oxides
Yevgen Ryeznik
 Dept: Mathematics
 Project: Optimal adaptive designs and adaptive randomization techniques for clinical trials
Zahedeh Bashardanesh
 Dept: Cell and molecular biology
 Project: Effect of Macromolecular Crowding on Diffusive Processes
Daniah Tahir
 Dept: Mathematics
 Project: Multitrait Branching Models with Applications to Species Evolution
Jakob Spiegelberg
 Dept: Physics and Astronomy
 Project: Blind Source Separation in Electron Microscopy
Yu (Ernest) Liu
 Dept: Mathematics (Externally financed)
 Project: From nonlife, to life, to a variety of life
Fredrik Hellman
 Dept: Information Technology
 Project: Numerical Methods for Darcy Flow Problems with Rough and Uncertain Data
Natalia Zabzina
 Dept: Mathematics
 Project: Cooperativity Mechanisms and Collective Decisionmaking
Beatriz Villarroel
 Dept: Physics and Astronomy
 Project: The Formation of Active Galaxies as Inferred from Advanced Statistical Methods
Martin Almquist
 Dept: Information Technology
Arianna Bottinelli
 Dept: Mathematics
 Project: Modelling Animal Collective Behaviour and Decision Making
Marta Leniec
 Dept: Mathematics
 Project: Pricing defaultable contingent claims in enlarged filtrations
Radoslav Kozma
 Dept: Ecology and Genetics
 Project: A biomathematical approach to the plumage colour differences in the Willow grouse
Anel Mahmutovic
 Dept: Cell and Molecular Biology
 Project: Quantitative Modelling and Simulation of Reactiondiffusion Processes
Shyam Ranganathan
 Dept: Mathematics
 Project: Development Space: Datadriven dynamical systems models for studying human development
Boris Granovskiy
 Dept: Mathematics (Externally financed)
 Project: Modeling Collective Decision Making by Animal Groups
Qi Ma
 Dept: Mathematics
 Project: Stochastic models of aggregation and network formation
Daniel Strömbom
 Dept: Mathematics
 Project: Collective Motion from Local Attraction