Photo of George Baravdish

George Baravdish

Senior Associate Professor, Head of Unit

Presentation

Senior associate professor of applied mathematics at the Unit of Physics, Electronics and Mathematics (FEM) at the Department of Science and Technology (ITN), Linköping University.

Since 2000, I have been the director of studies and in 2006 I was appointed head of the mathematics unit at ITN. I received my PhD in 1995 under the supervision of Professor V. G. Maz'ya at the Department of Mathematics (MAI) at LiU in the field of Ill-posed and Inverse Problems. During my time at LiU, I have been the main supervisor for two doctoral students and co-supervisor for two more. I have been both the main applicant and co-applicant for grants for 15 different research projects.

Publications

2024

George Baravdish, Tomas Johansson, Lukáลก Malý, Olof Svensson (2024) Brain Tumour Evolution Backwards in Time via Reaction-Diffusion Models and Sobolev Regularisation Modelling and Computational Approaches for Multi-scale Phenomena in Cancer Research: From Cancer Evolution to Cancer Treatment Continue to DOI
George Baravdish, Yuanji Cheng, Olof Svensson (2024) On a new singular and degenerate extension of the p-Laplace operator Nonlinear Analysis, Vol. 244, Article 113553 Continue to DOI

2023

George Baravdish, Tomas Johansson, Olof Svensson, W. Ssebunjo (2023) Identifying a response parameter in a model of brain tumour evolution under therapy IMA Journal of Applied Mathematics, Vol. 88, p. 378-404 Continue to DOI

2020

George Baravdish, Yuanji Cheng, Olof Svensson, Freddie Åström (2020) Generalizations of p-Laplace operator for image enhancement: Part 2 Communications on Pure and Applied Analysis, Vol. 19, p. 3477-3500 Continue to DOI
Scott Fowler, Gabriel Baravdish, George Baravdish (2020) 3D Imaging of Sparse Wireless Signal Reconstructions via Machine Learning ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) Continue to DOI

My research

My research deals with ill-posed and inverse problems that took off at the turn of the century when well-posed problems were defined, i.e. problems that have a unique solution and depend continuously on the input data. This means that ill-posed problems are difficult to solve because even though they describe physical situations with a unique solution, they are numerically unstable. Many inverse problems are also ill-posed. Inverse problems are when the output is given but the model or input is unknown. As computers have become more powerful in recent decades, research on these problems has also taken off. Ill-posed and inverse problems in mathematics can be found in complex analysis, functional analysis, differential equations, and linear algebra.

In application areas, they can be found in machine learning, signal processing, computer vision, control engineering, meteorology, geophysics, optics, and nuclear physics. Some of the classic questions that have been studied intensively are to reconstruct an initial temperature for a heat conduction equation given the temperature at a later time. Another well-known problem is the analytic continuation of harmonic solutions, i.e. solving the Cauchy problem for the Laplace equation.

Ongoing projects

Right now I am working on these projects and have various collaborations:

Websites for ongoing projects

Additional research

In addition to the  projects that can be read more about via the links above, my research has the following focus:

Image processing

I have collaborated with several different researchers for a long time to study mathematical models in image processing and computer vision where we have proposed (a) Various optimization models for image enhancement (b) Generalization of the p-Laplace operator for noise reduction.

Telecommunications

Here I have studied the problem of reconstructing 3D objects using Wi-Fi signals, AI, and Compressed Sensing.

Inverse and Ill-Posed Problems: Theory and Algorithms

This project constitutes the mathematical core of my research areas that I have continued to develop and generalize both theory and methods applied to ODE and PDE, which are the mathematical models in the research areas listed above.

My profile on Google Scholar
ORCID

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Research in Mathematics at ITN

Mathematical research is conducted in several areas such as inverse problems with applications in machine learning, medicine, and image processing. As well as computational science, harmonic analysis, integrable systems, and potential theory.

Organisation