# Supervisors in Mathematics and Applied Mathematics

MAM supervisors and potential supervisors for PhD students.

The research environment in Mathematics and Applied Mathematics MAM has strong research capacity in supervision consisting of active researchers who have been involved or plan to be more involved in supervision of co-supervision of PhD students in the future.

This list consists of current and potential supervisors and co-supevisors of PhD students in MAM with short description of there research and supervision directions and interests and also contact information. All interested in cooperation with MAM researchers or in PhD studies in MAM are wellcome to contact any of the Professors or other ressearchers in the list.

**Professors**

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**Name:** Kimmo Eriksson

**Position and Academic title:** Professor, Docent, PhD, Research group leader of the MAM research group Discrete Mathematics and modelling of behaviour and culture, subject representative for the undegraduate and research education subject Mathematics and Applied Mathematics

**E-mail address:** kimmo.eriksson@mdh.se

**Personal webpage:** https://www.mdh.se/ukk/personal/maa/ken05

**Publications:**

Google scholar: https://scholar.google.com/citations?hl=en&user=59B967IAAAAJ

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**Name:** Anatoliy Malyarenko

**Position and Academic title:** Professor, Docent, PhD, Research group leader of the MAM research group Stochastic processes, Statistics and Financial Engineering, subject direction representative for Financial engineering and Mathematical Statistics (Financial Mathematics)

**E-mail address:** anatoliy.malyarenko@mdh.se

**Personal webpage:** https://www.mdh.se/ukk/personal/maa/amo01

**Publications:**

Google scholar: https://scholar.google.com/citations?hl=en&user=qazWsYgAAAAJ

ORCID: https://orcid.org/0000-0002-0139-0747

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**Name:** Sergei Silvestrov

**Position and Academic title:** Professor, Docent, PhD, Scientific research leader for MAM (Mathematics and Applied Mathematics research environment at Mälardalen University), group leader of the MAM research group Algebra and Analysis with Applications and group leader of the MAM research group Engineering Mathematics

**E-mail address:** sergei.silvestrov@mdh.se

**Personal webpage:** https://www.mdh.se/ukk/personal/maa/ssv01

**Publications:**

Google scholar: https://scholar.google.com/citations?hl=en&user=xa5H7WoAAAAJ

**Directions of research and supervision**

My main research and supervision directions are:

1) Matrix analysis methods: Matrix equations and inequalities and their applications in control theory (automatic control), robotics, physics and related areas of science and engineering, determinants and spectral analysis of matrices and operators.

2) Discretization of differential and integral calculus, difference type operators and difference type equations and their generalizations, algebraic structure and applications in mathematics and other subjects in science and engineering.

3) Noncommutative and non-associative algebraic structures and their representations and applications: Hom-algebra structures and their connections to other parts of mathematics and to algebraic structures in mathematical physics; quantum algebras, q-deformed algebraic structures, quantum analysis (q-analysis).

4) Non-commutative geometry, operator algebras and applications (C*-algebras, Von Newman algebras; Banach algebras, normed algebras and their representations); Non-commutative analysis, representation theory and their connections to operator theory and spectral theory of operators;

5) Dynamical systems, Iterated functions systems and fractals, and actions of groups and semigroups and their interplay with operator analysis, operator algebras and algebraic structures;

*Applied areas:*

1) Engineering Mathematics and industrial mathematics applications involving matrix analysis and matrix algebra methods, analytic and algebraic methods for interpolation of data, probabilistic and statistical methods for data analysis, networks analysis methods for data analysis and data mining, ranking and information retrieval in complex big data, mathematics methods for internet and information networks and other types of networks in various application domains.

2) Stochastic processes and probability theory, especially involving matrix analysis and algebra methods, Markov chains and Markov processes and their applications; mathematical statistics and probability methods applied to analysis of data in various domains; connections of probability theory and stochastic processes to algebraic structures and algebraic methods.

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### Senior lecturers

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**Name:** Doghonay Arjmand

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:**
doghonay.arjmand@mdh.se

P**ersonal webpage in open publications info databases** **such as Google scholar, Web of Science, Research gate, DIVA, etc**

Google-scholar:

https://scholar.google.com/citations?hl=en&user=gy7Ib1YAAAAJ&view_op=list_works

Research gate:

https://www.researchgate.net/profile/Doghonay_Arjmand

**Directions of research and supervision**

I am interested in multiscale modeling, simulation, and analysis of physical problems in a broad sense. In particular, my expertise is in the following directions

1) Multiscale methods for numerical homogenization problems: This is linked to development of multiscale numerical methods for an efficient approximation of solutions of partial differential equations with highly oscillatory data modeling the heat and the wave phenomena in microscopically non-homogenous media.

2) Atomistic-Continuum coupling methods in micromagnetism: This aims at coupling the atomistic description of the magnetism to the continuum equations at nano to micro scales. Research in this direction aims at an efficient simulation and design of the future high-tech magnetic recording devices with improved recording capacities.

3) Uncertainty quantification: Physical problems in nature often suffer from uncertainties either due to a lack of knowledge in data, or since the problem in hand is intrinstically random. The main goal is to determine the effect of the randomness in the outputs of interests. One typical example for this would be to determine how the thermal properties of a composite material (used e.g., in the car and airplane industries) are influenced by the microscopic randomness, which is present in the composite.

4) High order time-stepping methods for ordinary differential equations (ODEs): In a numerical approximation of time-dependent problems, the error coming from the time-stepping accumulates over time and deteriorates the accuracy of the approximate solution. High order methods are useful, in particular, when one is interested in solving physical problems over long time scales.

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**Name: **Masood Aryapoor

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:** masood.aryapoor@mdh.se

**Publications: **

**Directions of research and supervision:**

My main research and supervision directions are:

Mathematics (more theoretical directions):

1) Noncommutative algebra

2) Algebraic geometry and its applications

3) Non-commutative algebraic geometry

4) Algebraic combinatorics

Applied Mathematics:

1) Integer linear programming and its applications in combinatorics;

2) Population dynamics.

**Name:** Linus Carlsson

**Position and Academic title:** Senior lecturer, Docent, PhD

**E-mail address:** linus.carlsson@mdh.se

**Publications:**

Google scholar: https://scholar.google.se/citations?user=D2DD9bcAAAAJ

**Directions of research and supervision:**

My main research and supervision directions are:

Partial Differential equations: stochastic, non-linear, non-local, parabolic, and hyperbolic.

Complex analysis: Analytic manifolds, Banach algebras.

Mathematical biology:

1) Model building in physiologically structured population models.

2) Model building in tree formations.

3) Stability analysis of numerical methods used to solve non-local, non-linear PDEs

4) Catastrophic event analysis of stochastic PDEs in connection to physiologically structured population models

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**Name:** Christopher Engström

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:** christopher.engstrom@mdh.se

**Publications**

ResearchGate: https://www.researchgate.net/profile/Christopher_Engstroem

**Directions of research and supervision:**

My main research and supervision directions are:

1) Network analysis and applications to networks-based data in applied subjects such as computer science and biology. Mainly methods for classification, ranking and clustering on graphs and related applications.

2) Applications of data analysis, mainly related to regression, classification or clustering of data.

3) Graph random walks, Markov chains and related graph centrality measures such as PageRank and methods to compute or update results efficiently, for very large graphs or as the graph changes as well as generalizations and modifications of these methods.

4) Matrix analysis and methods related to non-negative matrices and Markov chains.

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**Name:** Lars Hellström

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:** lars.hellstrom@mdh.se

**Publications:**

DIVA: http://mdh.diva-portal.org/smash/person.jsf?pid=authority-person:30759

**Directions of research and supervision:**

Non-commutative algebra, normal forms, bases, term rewriting and Dimond lemmas for algebras, operads, props, networks and computer algorithms;

Computational algebra, graphs, networks and algorithms in computer science.

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**Name:** Fredrik Jansson

**Position and Academic title:** Senior lecturer, PhD

**Email:** fredrik.jansson@mdh.se

**Personal webpage:** http://www.fredrik.name/

**Directions of research and supervision:**

My main research and supervision directions are:

Mathematical modelling of human behaviour, societal outcomes and evolutionary dynamics

I have a broad cross-disciplinary interest in the mathematical modelling and data analysis of human behaviour and its effects on cultural change and society at large. I would be happy to supervise related projects, for example on testing assumptions about mechanisms and evaluating their predictions, or by inferring social patterns from available datasets.

Testing assumptions can include abstraction and mathematical formalisation of theory, and developing models, such as dynamical systems, different games, diffusion models, and other evolutionary dynamics. This can also give rise to, for example, interesting combinatorial problems. Inferring social patterns can, among other things, make use of network theory, methods for analysing big data, and the development of new inferential methods.

Different areas of mathematics will be involved, depending on the nature of the research question, but typical areas may include, but are not limited to, game theory, dynamical systems, graph theory, combinatorics, evolutionary algorithms, complex systems, probability theory, stochastic processes and statistics.

Keywords: Mathematical modelling; Data analysis; Discrete mathematics; Cultural evolution; Mathematical psychology; Mathematical sociology; Game theory; Computational social science

Closest Mathematics Subject Classification (MSC): 91 Applied mathematics: Game theory, economics, social and behavioural sciences

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**Name:** Karl Lundengård

**Position and Academic title:** Licentiate, PhD student, Lecturer

**E-mail address:** karl.lundengard@mdh.se

**Personal webpage**

https://www.mdh.se/ukk/personal/maa/kld02

**Publications**

DIVA: http://mdh.diva-portal.org/smash/person.jsf?pid=authority-person:30810

**Directions of research and supervision**

My main research and supervision directions are:

Mathematics:

- Extreme points of the Vandermonde determinant on various surfaces and related applications in curve fitting.
- Phenomenological modelling of electrostatic discharge current in electromagnetic compatibility.
- Comparison of mathematical models of mortality rate with respect to various applications.

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**Name:** Ying Ni

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:** ying.ni@mdh.se

**Publications:**

DIVA: http://mdh.diva-portal.org/smash/person.jsf?pid=authority-person:30573

**Directions of research and supervision:**

My main research and supervision directions are:

Applied Mathematics - applied probability and stochastic processes

1) Financial mathematics involving advanced stochastic modelling and option pricing.

2) Data analysis using mathematical statistics and data-mining tools in various domains, particularly in financial engineering.

3) Asymptotic expansion and perturbation methods applied to various problems

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**Name:** Milica Rancic

**Position and Academic title:** Senior lecturer, PhD

**E-mail address: **milica.rancic@mdh.se

**Personal webpage:** https://www.linkedin.com/in/milicarancic/

**Directions of research and supervision:**

My main research and supervision directions are:

Applied Mathematics:

1) Engineering Mathematics and applications in electromagnetic compatibility EMC and computational electromagnetics CEM (antenna theory, grounding systems, lightning and electrostatic discharge modelling)

2) Mathematical statistics and probability applied in actuarial mathematics (mortality rates modelling and forecasting)

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**Name:** Siyang Wang

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:** siyang.wang@mdh.se

**Directions of research and supervision**

My main research and supervision directions are in the field of computational mathematics, especially numerical methods for partial differential equations that model a wide variety of phenomena in engineering and sciences. More specific topics include:

1) High-order finite difference methods for wave propagation problems

Acoustic and elastic wave equation

Summation-by-parts (SBP) finite difference methods

Stability and accuracy analysis

Local and adaptive mesh refinement

2) Discontinuous Galerkin methods for wave propagation problems

Wave propagation in coupled acoustic-elastic medium

Energy conserving and upwind discretization

Hybrid methods

3) Multiscale methods for fluid flows and waves

Darcy flows and acoustic waves with rough data

Finite element methods

Numerical upscaling by the localized orthogonal decomposition (LOD) method

In addition, I am also interested in efficient implementations of numerical methods for partial differential equations.

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**Name:** Thomas Westerbäck

**Position and Academic title:** Senior lecturer, PhD

**E-mail address:**
thomas.westerback@mdh.se

**Directions of research and supervision**

P**ersonal webpage in open publications info databases** **such as Google scholar, Web of Science, Research gate, DIVA, etc**

https://scholar.google.com/citations?user=eJ4qv90AAAAJ&hl=sv

https://www.researchgate.net/profile/Thomas_Westerbaeck

**Directions of research and supervision**

My main research and supervision directions are:

Mathematics (more theoretical directions):

Algebraic and enumerative combinatorics, and algebra. In particular:

1) The theory of matroids, polymatroid and generalizations thereof. For example the theory of

(i) cyclic flats,

(ii) combinatorial polynomials such as the Tuttes polynomial and specializations there of,

(iii) duality theorems,

(iv) representability.

2) Connections of matroids, polymatroid and generalizations thereof with different parts of mathematics. For example, connections to

(i) different algebraic structures such as matrices, modules over finite Frobenius rings, finite modules, finitely generated modules, Clifford algebra,

(ii) connections to combinatorial objects such as partially ordered sets, Young tableaux, hypergraphs,

(iii) connections to entropy.

3) Using 1) and 2) above to unify abstract concepts between different parts of mathematics, and vice versa use theories of different parts of mathematics to develop the theory of matroids, polymatroid and generalizations thereof.

Applied Mathematics:

1) Algebraic and enumerative combinatorics, algebra, and applications of these areas to coding, communication and information theory, and computer science. In particular applications of matroid theory, polymatroid theory and generalizations thereof to

(i) distributed data storage for large data networks such as data centers, peer-to-peer networks, wireless networks, cloud storage,

(ii) network coding,

(iii) LRC-codes, batch codes, Quasi-uniform codes,

(iv) codes over different algebraic structures and weights,

(v) machine learning over big data,

(vi) applications to quantum computing.

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