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School of Mathematical Sciences

Dr Kostas Papafitsoros


Lecturer in Mathematical Data Science

Room Number: MB-117


I am a Lecturer in Mathematical Data Science at the School of Mathematical Sciences, Queen Mary University of London. Prior to that (2017-2022), I was a research scientist at the Weierstrass Institute for Applied Analysis and Stochastics, in Berlin, Germany, working in the group Nonsmooth Variational Problems and Operator Equations. During the period September 2015-August 2017, I was an Alexander von Humboldt Postdoctoral Fellow, working initially at the Mathematical Institute of Humboldt University Berlin and later at the Weierstrass Institute. I completed my PhD in 2014 at the University of Cambridge, where I was also a member of the Cambridge Image Analysis group. I stayed in Cambridge six more months after my PhD, as  an EPSRC Doctoral Prize fellow at the Department of Applied Mathematics and Theoretical Physics.

NEW: Fully funded PhD opportunity!

A sea turtle swimming in a blue ocean

I am advertising a fully-funded PhD studentship at the School of Mathematics, Queen Mary University of London under the project:

"Data-driven Image Processing Methods with Applications to Wildlife Conservation".

The aim of this PhD project is to develop deep learning-based image processing methods (e.g. super-resolution), in order to enhance low quality images of wild animals, facilitating their individual identification. The ideal candidate should possess a masters degree in applied mathematics or computer science. Familiarity with optimisation, deep learning, imaging and computer vision techniques is highly desirable. Strong programming skills (e.g. Python) are essential. Please contact me if you are interested in this opportunity.

Deadline: December 1st, 2023.

The funding is available to both UK home and international students. Please contact me for more details.


Research Interests:

My main research area is mathematical imaging, in the interface of several areas of applied mathematics, like inverse problems, variational methods, calculus of variations, functional analysis, optimisation, optimal control, numerical analysis and deep learning. I have been particularly involved in the design, analysis and application of nonsmooth energy functionals incorporated in variational regularisation methods tailored for image processing. These functionals are at one hand discontinuity-preserving often stemming from their nonsmoothness, that is, they have the ability to preserve sharp edges in the images and on the other hand, they are adaptive to the specific structure of the given data. I am interested in combining these "classical" methods with modern data-driven and deep learning approaches.

I have a second research direction which is the result of my long-term involvement in environmental conservation with special focus on marine turtles, see the corresponding section of my personal website. I consider several applications of animal photo-identification (identification of individual animals via their unique morphological characteristics e.g. patterns/features), and in the same time consider various ways to enhance and automatise this process via modern imaging techniques. I am also looking at mathematical/statistical aspects of citizen science projects related to wildlife conservation.

In summary, I am interested in:

  • Mathematical imaging
  • Inverse Problems
  • Optimisation
  • Optimal control
  • Calculus of variations
  • Functional analysis
  • Numerical analysis
  • Deep learning and neural networks
  • Wildlife conservation (marine turtles)
  • Animal photo-identification techniques
  • Mathematical/statistical aspects of citizen science 


Here are a few latest relevant publications of mine. For a complete list of publications, please visit my google scholar profile.

  • Learning regularization parameter-maps for variational image reconstruction using deep neural networks and algorithm unrolling,
    Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba, Christoph Kolbitsch, Evangelos Papoutsellis, David Schote, Clemens Sirotenko, Felix Frederik Zimmermann, Kostas Papafitsoros, SIAM Journal on Imaging Sciences (to appear), (2023), [arXiv preprint]
  • Unrolled three-operator splitting for parameter-map learning in low dose X-ray CT reconstruction,
    Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba, Christoph Kolbitsch, Evangelos Papoutsellis, David Schote, Clemens Sirotenko, Felix Frederik Zimmermann, Kostas Papafitsoros, Fully 3D, (2023), [arXiv preprint]
  • SeaTurtleID: A novel long-span dataset highlighting the importance of timestamps in wildlife re-identification,
    Kostas Papafitsoros, Lukas Adam, Vojtech Cermak, Lukas Picek, (2022) [arXiv preprint]
  • A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives,
    Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros, (2022) [arXiv preprint]
  • First-order conditions for the optimal control of learning-informed nonsmooth PDEs,
    Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros, Kathrin Völkner, (2022) [arXiv preprint]
  • A social media-based framework for quantifying temporal changes to wildlife viewing intensity: Case study of sea turtles before and during COVID-19,
    Kostas Papafitsoros, Lukas Adam, Gail Schofield, Ecological Modelling,(2023) [DOI
  • Dualization and automatic distributed parameter selection of total generalized variation via bilevel optimization,
    Michael Hintermüller, Kostas Papafitsoros, Carlos N. Rautenberg, Hongpeng Sun, Numerical Functional Analysis and Optimization, 43(8), 887-932, (2022) [DOI]
  • Bilevel training schemes in imaging for total-variation-type functionals with convex integrands,
    Valerio Pagliari, Kostas Papafitsoros, Bogdan Raita, Andreas Vikelis, SIAM Journal on Imaging Sciences, 15(4), (2022) [DOI
  • Optimization with learning-informed differential equation constraints and its applications,
    Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros, ESAIM: Control, Optimisation and Calculus of Variations, 28(3), (2022) [DOI]
  • More aggressive sea turtles win fights over foraging resources independent of body size and years of presence,
    Gail Schofield, Kostas Papafitsoros, Chloe Chapman, Akanksha Shah, Lucy Westover, Liam C.D. Dickson, Kostas A. Katselidis, Animal Behaviour, 190, 209-219, (2022) [DOI]
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