Manlio De Domenico | |
---|---|
Born | Manlio De Domenico 1984 Italy |
Nationality | Italian |
Citizenship | Italian |
Alma mater | University of Catania (Ph.D) |
Scientific career | |
Fields | network science, multilayer networks, complex adaptive systems |
Institutions | University of Padua Fondazione Bruno Kessler University of Rovira i Virgili MPIPKS University of Birmingham Scuola superiore di Catania |
Manlio De Domenico is an Italian physicist and complex systems scientist, currently Professor of Physics at the University of Padua and previously at the Fondazione Bruno Kessler in Trento (Italy). In 2014 he has co-founded the Mediterranean School of Complex Networks, [1] and in 2019 he has contributed to found the Italian Chapter of the Complex Systems Society. [2]
The focus of his research is on complex adaptive systems and big data analysis, where he is best known for his theoretical and computational work in network science, statistical physics and nonlinear dynamics of multilayer systems. [3]
He was born in Messina in 1984. He got his Ph.D in Nuclear and Astroparticle physics from the University of Catania and the Scuola Superiore di Catania in 2012, proposing a data-driven model for the propagation of Ultra-High Energy Cosmic Rays (UHECR) in a magnetized Universe and a multiscale approach to analyze their anisotropic distribution at Earth, [4] with visiting scholarships at the Institute for Nuclear Theory [5] of the University of Washington and the Institut de physique nucléaire d'Orsay.
He held postdoctoral positions (2012-2013) at the School of Computer Science of the University of Birmingham (UK), and (2013-2016) at the University of Rovira i Virgili (Spain). In 2016 he has been a visiting scholar at the Max Planck Institute for the Physics of Complex Systems. From 2016 to 2018 he hold the “Juan de la Cierva” senior fellowship at the University of Rovira i Virgili. Since 2018 he directs the Complex Multilayer Networks (CoMuNe) Lab [6] founded at the Fondazione Bruno Kessler.
He has published more than 150 scientific papers, [7] with interdisciplinary contributions in computational social science, network epidemiology, network neuroscience, network medicine and systems biology. Notable works include the tensorial formulation of multilayer structure and dynamics, [3] [8] [9] applications to community structure [10] and coupling of human behavior with epidemic spreading, [11] network geometry, [12] [13] network entropy for multiscale analysis of the interplay between structure and dynamics, [14] percolation and network robustness to perturbations, [15] [16] [17] Infodemic. [18] [19]
His collaborators include Alex Arenas, Sylvie Briand, Guido Caldarelli, James Cronin, Shlomo Havlin, Vito Latora, Yamir Moreno, Mason Porter, Steven Strogatz and Alan Andrew Watson.
A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations, an ecosystem, a living cell, and ultimately the entire universe.
Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality.
Statistical finance, is the application of econophysics to financial markets. Instead of the normative roots of finance, it uses a positivist framework. It includes exemplars from statistical physics with an emphasis on emergent or collective properties of financial markets. Empirically observed stylized facts are the starting point for this approach to understanding financial markets.
Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and affiliate of the Computer Science Department at ETH Zurich.
Human dynamics refer to a branch of complex systems research in statistical physics such as the movement of crowds and queues and other systems of complex human interactions including statistical modelling of human networks, including interactions over communications networks.
In condensed matter physics, a time crystal is a quantum system of particles whose lowest-energy state is one in which the particles are in repetitive motion. The system cannot lose energy to the environment and come to rest because it is already in its quantum ground state. Because of this, the motion of the particles does not really represent kinetic energy like other motion; it has "motion without energy". Time crystals were first proposed theoretically by Frank Wilczek in 2012 as a time-based analogue to common crystals – whereas the atoms in crystals are arranged periodically in space, the atoms in a time crystal are arranged periodically in both space and time. Several different groups have demonstrated matter with stable periodic evolution in systems that are periodically driven. In terms of practical use, time crystals may one day be used as quantum computer memory.
Christopher Roy Monroe is an American physicist and engineer in the areas of atomic, molecular, and optical physics and quantum information science, especially quantum computing. He directs one of the leading research and development efforts in ion trap quantum computing. Monroe is the Gilhuly Family Presidential Distinguished Professor of Electrical and Computer Engineering and Physics at Duke University and is College Park Professor of Physics at the University of Maryland and Fellow of the Joint Quantum Institute and Joint Center for Quantum Computer Science. He is also co-founder and chief scientist at IonQ, Inc.
Matjaž Perc is Professor of Physics at the University of Maribor in Slovenia, and director of the Complex Systems Center Maribor. He is member of Academia Europaea and among top 1% most cited physicists according to Thomson Reuters Highly Cited Researchers. He is Outstanding Referee of the Physical Review and Physical Review Letters journals, and Distinguished Referee of EPL. He received the Young Scientist Award for Socio-and Econophysics in 2015. His research has been widely reported in the media and professional literature.
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In network theory, multidimensional networks, a special type of multilayer network, are networks with multiple kinds of relations. Increasingly sophisticated attempts to model real-world systems as multidimensional networks have yielded valuable insight in the fields of social network analysis, economics, urban and international transport, ecology, psychology, medicine, biology, commerce, climatology, physics, computational neuroscience, operations management, and finance.
Baruch Barzel is an Israeli physicist and applied mathematician at Bar-Ilan University, a member of the Gonda Multidisciplinary Brain Research Center and of the Bar-Ilan Data Science Institute. His main research areas are statistical physics, complex systems, nonlinear dynamics and network science.
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Social physics or sociophysics is a field of science which uses mathematical tools inspired by physics to understand the behavior of human crowds. In a modern commercial use, it can also refer to the analysis of social phenomena with big data.
Yamir Moreno is a professor of Physics at the Department of Theoretical Physics of the University of Zaragoza and Director of the Institute for Biocomputation and Physics of Complex Systems at the same institution.
Stefano Boccaletti is an Italian physicist. He is senior researcher at the CNR's Institute for Complex Systems in Florence, Italy. His research is mainly concerned with nonlinear and statistical physics, and complex systems sciences with applications to systems biology and systems medicine, data and networks sciences, social science and engineering, among others.
Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians, learning quantum phase transitions, and automatically generating new quantum experiments. Classical machine learning is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technologies development, and computational materials design. In this context, it can be used for example as a tool to interpolate pre-calculated interatomic potentials or directly solving the Schrödinger equation with a variational method.
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In network science, the network entropy is a disorder measure derived from information theory to describe the level of randomness and the amount of information encoded in a graph. It is a relevant metric to quantitatively characterize real complex networks and can also be used to quantify network complexity
Giuseppe Carleo is an Italian physicist. He is a professor of computational physics at EPFL and the head of the Laboratory of Computational Quantum Science.
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