Oleksii Bielikh defended his Master Thesis. Congrats!

Oleksii Bielikh defended his Master thesis in Complex Adaptive Systems  at Chalmers University of Technology on October 2017.

Thesis title: Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity

Thesis advisor: Giovanni Volpe

One of the current frontiers in neurosciences is to understand brain connectivity both in healthy subjects and patients. Recent studies suggest that brain connectivity measured with graph theory is a reliable candidate biomarker of neuronal dysfunction and disease spread in neurodegenerative disorders. Widespread abnormalities in the topology of the cerebral networks in patients correlate with a higher risk of developing dementia and worse prognosis.

In order to recognize such abnormalities, brain network graph measures should be compared with the corresponding measures calculated on random graphs with the same degree distribution. However, creating a random graph with prescribed degree sequence that has number of nodes of magnitude of 105 is a recognized problem. Existing algorithms have a variety of shortcomings, among which are slow run-time, non-uniformity of results and divergence of degree distribution with the target one.

The goal of this thesis is to explore the possibility of finding an algorithm that can be used with very large networks. Multiple common algorithms were tested to check their scaling with increasing number of nodes. The results are compared in order to find weaknesses and strengths of particular algorithms, and certain changes are offered that speed up their runtimes and/or correct for the downsides. The degree distributions of the resulting random graphs are compared to those of the target graphs, which are constructed in a way that mimics some of the most common characteristics of brain networks, namely small-worldness and scale-free topology, and it is discussed why some of the models are more appropriate than others in this case. Simulations prove that the majority of algorithms are vastly inefficient in creating random large graphs with necessary limitations on their topology, while some can be adapted to showcase to a certain extent promising results.

Simon Nilsson defended his Master Thesis. Congrats!

Simon Nilsson defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 14 June 2017.

Thesis title: Collective Dynamics in a Complex Environment

Thesis advisor: Giovanni Volpe

Collective behaviour is a phenomenon that often occurs in systems of many interacting individuals. Common macroscopic examples of collective behaviour are flocks of birds, swarms of insects and crowds of people. On the microscopic scale, it is often observed in so-called active systems, constituted by self-propelled particles, also known as active particles. Motile bacteria or synthetic microswim- mers are among the most commonly studied active particles.

The potential applications of collective behaviour and understanding thereof encompass multiple disciplines, ranging from robotics and medicine to algorithms, like ant colony optimization. However, the apparent complexity makes under- standing an intimidating task. Despite this, simple models have proven successful in capturing the defining characteristics of such systems.

This thesis examines a well-known model of active matter and expands it to incorporate necessary components to explore the effects a complex environment has on this pre-existing model. Additionally, a new model is proposed and explored in purely active systems as well as in complex environments. Simulations show that a phase transition between a gaseous state and the formation of metastable clusters occurs as the level of orientational noise decreases. Furthermore, they show that this model describes the formation of metastable channels in a crowded environment of passive particles.

Falko Schmidt starts his PhD

Falko Schmidt starts his PhD at the Physics Department of the University of Gothenburg on 1 January 2017.

He has a Master degree from the Physics Department of Leipzig University with a Master thesis on the realisation of a microscopic critical engine.

He will now work on his PhD thesis on the experimental study of critical fluctuations and critical Casimir forces.

Aykut Argun starts his PhD

Aykut Argun starts his PhD at the Physics Department of the University of Gothenburg on 1 December 2017.

He has a Master degree from the Physics Department of Bilkent University with a Master thesis on the experimental study of thermodynamics in active baths.

He will now work on his PhD thesis on the experimental study of nanothermodynamics.

Saga Helgadottir joins the Soft Matter Lab

Saga Helgadottir joins the Soft Matter Lab on 28 November 2017 as a PhD student at the Physics Department of the University of Gothenburg.

She has a Master degree in Physics from Chalmers University of Technology with a Master thesis on the study of the effect of plasma on biofilms.

She will work on he PhD thesis on the realisation of hybrid microswimmers and the study of bacterial dynamics in complex and crowded environments.

Jalpa Soni joins the Soft Matter Lab

Jalpa Soni from the bioNaP lab at the Indian Institute of Science Education and Research Kolkata, India, joined the Soft Matter Lab on 1 June 2016 as a postdoctoral researcher.

Her PhD thesis, “Quantitative Mueller matrix polarimetry in biophotonics and nanoplasmonics”, deals with understanding the interaction of polarized
light in various biophotonic and plasmonic systems. She studied both fundamental effects such as understanding spin-orbit interaction (SOI) of light and polarization dependent beam shifts as well as various practical applications involving biological systems.

At the Soft Matter Lab, she will work on a project related to the realisation of a microscopic heat engine using optical tweezers and noisy electric fields.