Prof. James P. Gleeson (University of Limerick, Ireland) – stochastic dynamics and mathematical modelling of diffusion processes on complex networks
Prof. Esteban Moro (Universidad Carlos III de Madrid, Spain) – dynamics in complex networks: analysing real-world data
Prof. Katarzyna Sznajd-Weron (Wrocław University of Technology, Poland) – diffusion of innovations
Dr. Bruno Gonçalves (New York University Center for Data Science, United States) – modelling and predicting contagion processes
Dr. Márton Karsai (Ecole Normale Supérieure de Lyon, France) – modelling contacts in social networks – the impact of network dynamics on the diffusion processes
Dr. Glenn Lawyer (Max-Planck-Institut für Informatik, Germany) – real-world applications of models of contagion processes
Dr. Matteo Magnani (Uppsala University, Sweden) – diffusion processes in multi-layer networks
Dr. Ingo Scholtes (ETH Zurich, Switzerland) – mining high-frequency data on temporal networks
Prof. James P. Gleeson – James Gleeson holds a BSc in Mathematical Science and an MSc in Mathematical Physics from University College Dublin. In 1999 he completed his PhD in Applied Mathematics at Caltech. He has lectured at Arizona State University and at University College Cork, and since 2007 has held a Chair in Industrial and Applied Mathematics at the University of Limerick. James’ research interests are in mathematical modelling of stochastic dynamics, with a particular focus on complex systems and networks. As co-director of MACSI, the Mathematics Applications Consortium for Science and Industry, he leads research into applications of mathematics to real-world problems with significant economic and societal impact.
Prof. Esteban Moro – Prof. Esteban Moro is associate professor at Universidad Carlos III de Madrid, Spain and member of the Joint Institute UC3M-Santander on Big Data. Moro serves as consultant for many public and private institutions and has held previously positions in University of Oxford, Institute of Knowledge Engineering (Spain), Instituto Mixto de Ciencias Matemáticas (Spain). Moro earned his BSc in Physics from the University of Salamanca and a Ph.D in physics from Universidad Carlos III de Madrid. Prof. Moro has published over 50 articles and have led and participated in over 20 projects funded by government agencies and/or private companies. His areas of interests are applied mathematics, financial mathematics, viral marketing and social network. Moro received the “Shared University Award” from IBM in 2007 for modeling the spread of information in social networks and application to viral marketing. And a Research Excellence Award in 2013 and 2015 by the Carlos III University of Madrid. Recent moro’s work has been covered by many media outlets, including articles and interviews in newspapers like El Pais, Muy Interesante or The Atlantic.
Prof. Katarzyna Sznajd-Weron is Professor of Physics at Wrocław University of Technology (PWr), Poland. Prior to joining the Department of Theoretical Physics at PWr in October 2012, she was Head of the Complex Systems and Nonlinear Dynamics Division and UNESCO Chair of Interdisciplinary Complex Systems at the University of Wrocław, Poland. She is an Advisory Editor for Physica A: Statistical Mechanics and its Applications and an Associate Editor for Frontiers in Physics. Her research focuses on applications of statistical physics (mainly simple lattice models and the theory of phase transitions) in a variety of complex systems, including social and biological. She is the author of over 50 peer-reviewed articles (most notably in top-tier journals Energy Policy, EPL, Physical Review Letters, Physical Review E, PLoS ONE) and popular publications. A model of opinion dynamics, co-developed by her in 2000 and known in the literature as the Sznajd model, has been cited over 600 times according to WoS. In 2007 she was awarded the prestigious Young Scientist Award in Socio- and Econophysics for her original contribution to a better understanding of open problems in socio-economic systems by means of physical methods.
Dr. Bruno Gonçalves is a Data Science fellow at NYU’s Center for Data Science while on leave from tenured faculty position at Aix-Marseille Université. He has a strong expertise in using large scale datasets for the analysis of human behavior. After completing his joint PhD in Physics, MSc in C.S. at Emory University in Atlanta, GA in 2008 he joined the Center for Complex Networks and Systems Research at Indiana University as a Research Associate. From September 2011 until August 2012 he was an Associate Research Scientist at the Laboratory for the Modeling of Biological and Technical Systems at Northeastern University. Since 2008 he has been pursuing the use of Data Science and Machine Learning to study human behavior. By processing and analyzing large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading. He is the author of 50+ publications with over 3200+ Google Scholar citations and an h-index of 25. He is also the editor of the book Social Phenomena: From Data Analysis to Models (Springer, 2015).
Dr. Márton Karsai is an assistant professor with Inria Chair of excellence at the Computer Science Department of ENS Lyon. During his early career, after receiving a co-supervised PhD degree from the University of Szeged (Hungary) and at the CNRS-Grenoble, Université Joseph Fourier (France) in 2009, he was a postdoctoral researcher at Aalto University (Finland) in the group of Prof. Kimmo Kaski and Prof. Jari Saramaki, and at Northeastern University (USA) in the group of Prof. Alessandro Vespignani. His main research topics focus on the theoretical foundation and modelling of temporal networks and their co-evolution with epidemic and social contagion processes, analysis of large-scale longitudinal data sets of human dynamics, and the development of novel data-driven modelling techniques.
Dr. Glenn Lawyer completed his masters in computer science, specializing in autonomous systems, at Sweden’s Royal Institute of Technology. His Ph.D. research applied statistical learning to investigate brain structure/cognitive ability relationships in healthy adults, adults with a diagnosis of schizophrenia, and intravenous drug users. Since nothing in biology makes sense except in light of evolution, he then went to the Max Planck Institute for Informatics to study viral evolution dynamics. For many virus, evolution and transmission are interrelated. This motivated work on network-mediated spreading processes. His most recent work is on forecasting infectious disease epidemics.
Dr. Matteo Magnani is a senior lecturer in database systems and data mining at Uppsala University, and has previously held positions at CNR, Italy, at the University of Bologna and at Aarhus University. He has written around 1.5 Kg of papers, presented in top venues in the data management area (VLDB journal, EDBT, CIKM, etc.) and in several other areas spanning multiple disciplines. He also has an h-index. He has received several awards, including a Pedagogical Award (best teacher, Uppsala University), an informal Funniest Presentation award (SBP conference), a recognition of excellence in reviewing (SocInfo conference) and a Best Young Chess Player award at a local tournament with two participants. He has authored the forthcoming book “Multilayer Social Networks” (Cambridge University Press) and several articles on this topic, including one of the first research papers on multilayer social networks (best paper award at the ASONAM conference) and a survey paper on information diffusion in multilayer networks.
Dr. Ingo Scholtes is a senior researcher and lecturer at the Chair of Systems Design at ETH Zürich. Following studies in computer science and mathematics, he completed his doctorate studies in the Systems Software and Distributed Systems group at the University of Trier in 2011. He was involved in the Large Hadron Collider experiment at CERN, designing and implementing a Peer-to-Peer-based framework for large-scale data distribution which is since being used to monitor particle collision data from the ATLAS detector. Inspired by this experience, he turned his attention to the modeling and analysis of complex networked systems. His latest research addresses applications of network science in the analysis of data from socio-technical systems, but also from biology and sociology. In a theoretical line of research he further studies new methods in the analysis of time-stamped network data. At ETH Zürich he developed a course on network science which bridges the curricula of engineering and natural sciences. He previously held a scholarship from the Studienstiftung des Deutschen Volkes and was awarded a Junior-Fellowship from the Gesellschaft für Informatik in 2014.