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 two-dimensional 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 Toeplitz-like matrices. More specifically, to investigate using the theory of generalised locally Toeplitz (GLT) sequences and matrix-less 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 data-driven 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. Long-term, 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 real-time 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 human-human and human-robot interaction Human-Human 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 Human-Robot 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 high-order 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 high-order accurate where the solution is smooth. Another aim is to develop efficient solution algorithms that are well-suited for large-scale parallel infrastructures. 

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

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 self-propelled particle models capturing the collective motion of fish schools to models in mathematical neuroscience describing the interactions between neurons to individual-based models of ecological interactions.

Former PhD students

David Widmann

Elisabeth Wetzer

Carmina Fjellström

Eva Breznik

Michael Weiss

Mathew Magill

Marcus Westerberg

Ylva Rydin

Kristiina Ausmees

Fredrik Wrede

Akshay Krishna Ammothum Kandy

Yevgen Ryeznik

Zahedeh Bashardanesh

Daniah Tahir

Jakob Spiegelberg

Yu (Ernest) Liu

Fredrik Hellman

Natalia Zabzina

Beatriz Villarroel

Martin  Almquist

  • Dept: Information Technology

Arianna Bottinelli

Marta Leniec

Radoslav Kozma

Anel Mahmutovic

Shyam Ranganathan

Boris Granovskiy

Qi Ma

Daniel Strömbom

Last modified: 2023-06-26