People

Fellows

Alberto Aleta

Post-doctoral Scholar

I am a Postdoctoral Researcher at ISI Foundation in Turin, Italy. My research interests lie in the broad area of complex systems, network science, and data science. In particular, I love all the aspects that have to do with data, from obtention to visualization. As such, it is hard for me to focus on one single field. Nevertheless, I would say that because of my physics background, I am particularly interested in unraveling the mechanisms that lead to the data. That is, rather than simply focusing on finding correlations and making uninformed predictions, my aim is to understand the dynamical process that leads to it. I have collaborated in projects of very diverse disciplines from epidemiology to online videogames, but during the current pandemic my work has mostly focused on studying the spreading of COVID-19. In the context of multilayer networks, my work has centered on disentangling the effect of different human interactions on the spreading of infectious diseases, as well as leveraging the information encoded in these networks for link prediction problems. Besides, I am currently trying to incorporate complex systems techniques in general – and multilayer networks in particular – in the context of nutrition and sustainability research.

Abdullah Alrhmoun

PhD Student

I’m a PhD candidate in network science at the Department of Network and Data Science at Central European University in Vienna and hold a master’s degree in biomedical engineering. I’m primarily interested in the real-world use of data analytics, including understanding complex systems through big data, text mining of extremist content, and developing tools to detect fake imagery. Currently, I’m researching and testing social and political bots and how they interact with humans as part of an ecosystem. I use multiplex networks to represent the complex system of humans and bots, and I research how this representation facilitates misinformation diffusion. I’m also exploring computational propaganda, disinformation, and online extremism. Publications - Decoding hate: using experimental text analysis to classify terrorist content. https://bit.ly/3bPsyb5 - Mapping The Extremist Narrative Landscape In Afghanistan https://bit.ly/3hR55ds

Changqing Cheng

Faculty (professor or researcher)

My research focus is on data-driven modeling, control and optimal design of complex dynamical systems, particularly the dynamic networks. The recent leap forward in sensing and communication has generated the big data, and data analytics provides an unprecedented opportunity to transform the way we monitor and control a variety of complex systems. I have investigated on multiplex network representation of sensing data in process monitoring of such complex systems. I am also interested in the surrogate modeling and optimal design for multi-layer networked systems (e.g., power grid and communication systems) to enhance the resilience against large-scale perturbations.

Guilherme Ferraz de Arruda

Post-doctoral Scholar

The researcher has been working in nonlinear dynamics and stochastic processes on top of complex networks and higher-order structures. More specifically, on epidemic/rumor spreading and social contagion processes in single and multilayer networks and hypergraphs. He has focused on the theoretical and numerical methods developed for this study, formally defining the dynamical process and then validating and extending the theoretical results using numerical experiments and Monte Carlo simulations. Throughout the researcher's past works, spreading processes (disease, rumor, and social contagion models) were distinguished according to the temporal assumptions and its inherent mathematical assumptions, i.e., by distinguishing continuous-time and the discrete-time cellular automata approaches. Each formalism was studied using the appropriate tools, including the heterogeneous mean-field, the quenched-mean field, and the pair quenched mean-field approaches, for the continuous-time and discrete-time Markov chains for the cellular automata-like processes. Among other interests and using these formalisms, the researcher was concerned about the impact of heterogeneity in the dynamical parameters, which is essential for more realistic models. Despite dynamics, he has also worked with the structural characterization of single and multilayer networks, mainly through spectral theory.

Lluc Font-Pomarol

PhD Student

I was born in Barcelona in 1994. In 2017 I obtained a degree in Physics at the Universitat de Barcelona. In 2018 I obtained a master's degree in Modelling for Science and Engineering at the Universitat Autònoma de Barcelona. During my master's thesis I worked on modelling railway networks under the supervision of Prof. Albert Díaz-Guilera and Dr. Luce Prignano. In March 2019 I moved to Tarragona to start my PhD thesis at SeesLab under the supervision of Marta Sales-Pardo and Roger Guimerà. Since then, I worked on judicial decisions, studying, from a network science perspective, how they interrelate through citations to case law and to law, among others, connections that can be seen as the most elementary mechanisms underlying the process of legal reasoning and the judiciary process.

Arsham Ghavasieh

PhD Student

I'm mostly interested in understanding the complex systems from the perspective of theoretical physics. I use equilibrium and non-equilibrium statistical mechanics to investigate the macroscopic properties of highly interconnected systems, aiming to provide a framework to explain the emergent phenomena. Remarkably, many complex systems exhibit multilayer structures, often represented as multilayer networks. That's why I think multilayer networks play an important role in complex phenomena.

Pegah Hozhabrierdi

PhD Student

I am a philomath who aspires to be a polymath one day. Thanks to the wide range of topics that had kept my interest over the years, from philosophy and psychology to science and engineering, I have found the research in the interdisciplinary fields of science to be the most fulfilling. Currently, working on my Ph.D. thesis, I use the theoretical knowledge in network science to study social behavior patterns that emerge from empirical observations. Real-world multilayer social networks, virtual such as Twitter or physical such as human contact networks, are my main source of obtaining such observations. Using a range of analytical tools, from big data analysis to machine learning, I detect network dynamics that impact the formation and diffusion of beliefs and echo chambers. Furthermore, I study the degree to which the resilience of the network depends on these dynamics. Driven by my interdisciplinary experience with multilayer networks, I also aim at bridging the existing gap between theoretical science of multilayer networks (mainly from physics and computational biology) and empirical studies that involve graph machine learning algorithms (mainly in the field of computer science).

Márton Karsai

Faculty (professor or researcher)

Márton Karsai, PhD, Habil., Associate professor in the Department of Network and Data Science at the Central European University. He is a physicist trained network scientist with research interest in human dynamics, computational social science, and data science, especially focusing on systems with heterogeneous dynamics, spatial and temporal networks, socioeconomic systems and social and biological contagion phenomena.

Alessandra Urbinati

PhD Student

Alessandra Urbinati obtained her Bachelor Degree in Mathematical Engineering at Politecnico di Milano and his Master Degree in Stochastics and Data Science at the University of Torino. Her research interests include complex networks, machine learning, and data analytics. In particular, she is interested in analyzing the unfolding over time of complex phenomenon characterized by many aspects which interact in non-linear ways, which can be modeled by means of multilayer network. Among these phenomena, she has worked on human migration and opinion dynamics.

Sara Venturini

PhD Student

Sara Venturini is interested in the fields of Operational Research and Optimization. She is currently working on the computational analysis of complex networks and their applications to the real world. She is interested also to study more complex structures like multilayer networks, to take into account different type of information, simplicial complexes and hypergraphs, because pairwise interactions can not be enough to represent some situations. She studies both the fundamental nature of networked systems as well as the applications of these principles to real systems. Her research is motivated by a desire to contribute to the growing community dedicated to understanding networks by discovering their properties and applying them on real world systems.

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