DrMarijanBeg

Summary

Computers have become amazingly powerful to help us tackle some of the most challenging puzzles that are too complicated to be solved using conventional methods. As a computational physicist, I am attempting to understand the world by delegating the hard work to computers. As a Teaching Fellow in Computational Data Science, I teach and mentor the next generation of scientists and engineers to solve future challenges by applying novel computational approaches.

Research

The main focus of my research is computational magnetism. I develop open-source finite-element, finite-difference, and Monte Carlo simulation tools to investigate magnetism and magnetic materials on a nano-scale. I work on designing and implementing Finmag and Fidimag simulation open-source packages. I use simulations to explore magnetic topological quasi-particles, such as skyrmions and Bloch points, with promising properties to transform how we store and process data. By aiming to make computational workflows more effective and reproducible, I developed Ubermag - a Python interface that exposes different computational backends to Python’s ecosystem and integrates them into Jupyter. I participate in experiments and analyse big experimental data at the European X-Ray Free-Electron Laser Facility. I have been part of the EPSRC Programme Grant on Skyrmionics and the OpenDreamKit Horizon 2020 Research Infrastructure Project. I have collaborated with and supported scientists across discipline boundaries through various projects. Besides computational magnetism, research software engineering, and data analysis, my research interests include spintronics, simulation and modelling, complex systems, domain-specific languages, reproducibility, open science, and higher education.

I am an Editorial Board Member in condensed matter physics for Scientific Reports (published by Nature Portfolio) and Review Editor for Frontiers in Physics (published by Frontiers) open-access journals. In addition, I have served as a reviewer for Nature Portfolio, American Physical Society, Institute of Physics (Trusted Reviewer status since 2021), Royal Society of Chemistry, American Institute of Physics, American Chemical Society, IEEE, Elsevier, Frontiers, and Wiley journals. I am a member of the Institute of Physics and the IEEE. I have (co-)authored over 40 publications and developed over 16 open-source software packages. Furthermore, I have delivered eight invited talks and eight tutorials and participated with over 94 contributions at international conferences, where I have won three awards. More details about my research output are available on the following public profiles:
Google Scholar, ORCID, publons, Scopus, LinkedIn,
arXiv, Research Gate, loop

Teaching

I teach and supervise students in MSc Applied Computational Science and Engineering, MSc Environmental Data Science and Machine Learning, and MSc Geo-Energy with Machine Learning and Data Science postgraduate courses at Imperial College London. Through teaching, content development and design, supervision, assessment and feedback, I am involved in Modern Programming Methods, Numerical Programming in Python, Big Data Analytics, and Applying Computational/Data Science modules. I co-coordinate the Individual Research Project module (MSc Dissertations) across all three MSc courses.

I am a Deputy Postgraduate Senior Tutor, a member of the Mitigating Circumstances Board, and a member of the Board of Examiners. I am a personal tutor, supporting my tutees on their postgraduate journey. I received a Postgraduate Certificate in University Learning and Teaching (PGCert ULT) from Imperial College London and a Fellowship of the Higher Education Academy (Advance HE). I am working part-time towards my Postgraduate Diploma in University Learning and Teaching (PGDip ULT), and I am a member of the CHERS-CLCC Education Committee at Imperial College London. In addition, I am a Mental Health First Aider (MHFAider®) with the awarded qualification by the Royal Society for Public Health. I contribute to designing and developing open-source auto-assessment and auto-grading tools for computational thinking, such as PyBryt and generative-AI-assisted feedback generation.

Before joining Imperial College London, I taught various computational and data science modules and supervised PhD students at the University of Southampton and the University of Hamburg. Besides, I design and deliver workshops on computational science, computational magnetism, and reproducibility, supporting researchers in adopting the best computational and data science practices in their research.

Biography

• 2023: PGCert in University Learning and Teaching, Imperial College London, UK
• since 2023: Fellowship of the Higher Education Academy, Advance HE, United Kingdom
• since 2021: Teaching Fellow in Computational Data Science, Imperial College London, UK
• since 2021: Visiting Fellow, University of Southampton, UK
• 2019 - 2021: Senior Research Fellow, University of Southampton, UK
• 2017 - 2019: Computational Scientist, European X-Ray Free-Electron Laser Facility, Germany
• 2017 - 2019: Visiting Fellow, University of Southampton, UK
• 2016 - 2017: Research Fellow, University of Southampton, UK
• 2016: PhD in Complex Systems Simulation, University of Southampton, UK
• 2011: MSc in Nanoelectronics and Nanotechnology, University of Southampton, UK
• 2010: BSc (Hons) in Electrical and Computer Engineering, University of Novi Sad, Serbia

Publications

Journals

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