Alexandra BRINTRUP - University of Cambridge, UK
Nitesh CHAWLA - University of Notre Dame, USA
Kimmo KASKI - Aalto University, Finland
Bruno LEPRI - Bruno Kessler Foundation, Italy
Eckehard SCHOLL - TU Berlin, Germany
Biography
Luís M. A. Bettencourt is a Professor of Ecology and Evolution and the College at the University of Chicago. He is also Associate Faculty of the Department of Sociology and External Professor at the Santa Fe Institute. He grew up in Lisbon (Portugal) and obtained his undergraduate degree in Engineering Physics from IST Lisbon. He obtained his PhD from Imperial College London in Theoretical Physics and held postdocs and research positions at the University of Heidelberg (Germany), Los Alamos National Laboratory, MIT, and the Santa Fe Institute. His research focuses on the theory and modeling of complex systems and the processes that underlie the structure and growth of cities, in particular. He connects interdisciplinary concepts and advanced mathematicswith new technologies and data to create new systems’ theory and methods. This work also involves collaborations with governments, NGOs, and interdisciplinary researchers worldwide to co-produce new insights and transformative practices for sustainable development. His work is well-known academically and widely covered in the media. It has helped shape our fundamental understanding of complex systems and human societies and create novel approaches to challenges of urbanization and sustainability.
Federico Battiston, an Associate Professor in Network Science, brings a wealth of experience from esteemed institutions like CEU, University College London, and the Brain & Spine Institute in Paris. Holding a PhD from Queen Mary University of London, his expertise spans statistical physics, complexity science, and social networks. As Chair of NetSci2023, he oversees the premier conference in network science, demonstrating his leadership in the field. Federico's research, published in prestigious journals like Nature Physics and Science Advances, delves into diverse topics, from sustainable urban systems to the human brain. Recognized with awards such as the Junior Award of the Complex Systems Society and the Early Career Prize in Statistical and Nonlinear Physics of the European Physical Society, Federico continues to advance our understanding of complex systems and network dynamics, mentoring a new generation of researchers.
Abstract
First-order assumptions lose information, whether you are modeling ships spreading invasive species across oceans or molecules reacting in a flask. In this talk, I will trace how our work on higher-order network representations, which began with discovering non-Markovian dependencies in the global shipping network, has evolved into new graph neural network architectures for molecular and chemical systems. Along the way, I will show how the challenge of imbalanced data follows us across scales and demands new approaches when the data lives on graphs. Across both domains, the lesson is the same: look beyond pairwise interactions, and the network tells you more.
Biography
Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering and the Lucy Family Director for Data & AI Academic Strategy, leading the Data, AI, and Computing Initiative at the University of Notre Dame. His research is focused on artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through convergence. He is a Fellow of: the Institute of Electrical and Electronics Engineers (IEEE); the Association of Computing Machinery (ACM); the American Association for the Advancement of Science (AAAS); and the Association for the Advancement of Artificial Intelligence (AAAI). He is the recipient of multiple awards, including the National Academy of Engineers New Faculty Fellowship, IEEE CIS Outstanding Early Career Award, Rodney F. Ganey Community Impact Award, IBM Big Data & Analytics Faculty Award, IBM Watson Faculty Award, and the 1st Source Bank Technology Commercialization Award. He is a serial entrepreneur having (co-)founded multiple start-ups.
Tina Eliassi-Rad is the inaugural President Joseph E. Aoun Professor at Northeastern University. She is also an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Tina works at the intersection of artificial intelligence and network science and is interested in the impact of science and technology on society. For a more extended bio, visit http://eliassi.org/bio.html .
Combining biomedical data with networks can provide unprecedented insights into the mechanisms underlying disease. Furthermore, quantifying how the complex structure of biological networks is altered during disease is critical for developing new therapeutic or prevention strategies. Although many methods have been developed to estimate biological networks, these approaches typically use multiple experimental samples to estimate a single “aggregate” network and fail to capture population-level heterogeneity. In this talk I will review several approaches my group has developed for inferring networks from biomedical data, including a mathematical framework for estimating sample-specific networks. I will show how these approaches can be used to associate complex network connectivity patterns with other sample-specific information, such as patient phenotype. Finally, I will examine the caveats and underlying assumptions made by these methods, highlighting some of the broader challenges and opportunities in the emerging field of precision network medicine.
Biography
Kimberly Glass is an expert in complex networks and genomic data analysis. She obtained her PhD in Physics in 2010 from the University of Maryland. From 2010-2014, Dr. Glass was a postdoctoral fellow at Dana-Farber Cancer Institute and the Harvard T.H. Chan School of Public Health where she received training in computational biology. During her post-doc she developed several computational and data-integration methods for inferring and analyzing gene regulatory networks. In 2014 Kimberly joined the faculty of the Channing Division of Network Medicine (CDNM) at Brigham and Women’s Hospital where she is continuing her research in systems medicine and network methods. Her current research focuses on how to integrate and interpret multiple biological data-types in the regulatory network context and on how to understand the biological mechanisms represented in these networks. She is also investigating potential applications of networks in precision medicine, using network approaches to understand susceptibility to, severity, and treatment of complex diseases.
Frank Emmert-Streib, a distinguished Professor of Data Science at Tampere University, leads the Predictive Society and Data Analytics Lab, focusing on interdisciplinary research in data science. Formerly a Senior Lecturer at Queen's University Belfast, his expertise spans biostatistics, computational biology, and theoretical physics. Frank received training as a Senior Fellow at the University of Washington and was a Postdoctoral Research Associate at the Stowers Institute for Medical Research. With a Ph.D. in Theoretical Physics from the University of Bremen, he has significantly contributed to computational and statistical methods, particularly in addressing uncertainty and explainability in data analysis. Frank's extensive experience includes sabbaticals at prestigious institutions like Harvard School of Public Health and the University of Cambridge. As a co-founder and former CSO of sAnalytiCO Ltd, he has demonstrated leadership in academia and industry, shaping the future of data technology. He plays an active role in academic publishing as an editor and associate editor for prestigious scientific journals.
One of the oldest of network problems is the ranking of individuals, teams, or commodities on the basis of pairwise comparisons between them. For example, if you know which football teams beat which others in a particular year, can you say which team is the best overall? This is a harder problem than it sounds because not all pairs of teams play one another in a given season, and also because the outcomes of the games can be contradictory. This talk will introduce the techniques used to solve such ranking problems, with examples from games and sports, consumer research and marketing, and social hierarchies in both animal and human communities, then ask how those techniques can be extended to address a range of new questions about competition and ranking, including the development of new computer algorithms for ranking, questions about the varying patterns of competition in different sports, and what happens when individuals or teams compete in multiple different ways.
Biography
Professor Newman's research is on statistical physics and the theory of complex systems, with a primary focus on networked systems, including social, biological, and computer networks, which are studied using a combination of empirical methods, analysis, and computer simulation. Among other topics, he and his collaborators have worked on mathematical models of network structure, computer algorithms for analyzing network data, and applications of network theory to a wide variety of specific problems, including the spread of disease through human populations and the spread of computer viruses among computers, the patterns of collaboration of scientists and business-people, citation networks of scientific articles and law cases, network navigation algorithms and the design of distributed databases, and the robustness of networks to the failure of their nodes. Professor Newman also has a research interest in cartography and was, along with collaborators, one of the developers of a new type of map projection or "cartogram" that can be used to represent geographic data by varying the sizes of states, countries, or regions. Professor Newman is the author of several books, including a recent textbook on network theory and a popular book of cartography.
Filippo Menczer is the Luddy distinguished professor of informatics and computer science and the director of the Observatory on Social Media at Indiana University. He holds a Laurea in Physics from the Sapienza University of Rome and a Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego. His research interests span Web and data science, computational social science, science of science, and modeling of complex information networks. Dr. Menczer was named a Fellow of the ACM for his research on the vulnerability of social media networks to disinformation and manipulation.
Abstract
Synchronization is a widespread phenomenon occurring in dynamical networks of nonlinear oscillators in a variety of natural, socio-economic, and technological systems. We review synchronization scenarios emerging in networks of statically or adaptively coupled nonlinear oscillators.
Power grids, as well as neuronal networks with synaptic plasticity, or macroscopic stochastic models for economic cycle dynamics, describe real-world systems of tremendous importance for our daily life.
An intriguing example are chimera states which consist of spatially coexisting domains of coherent (synchronized) and incoherent (desynchronized) dynamics, i.e., seemingly incongruous parts.
We show that a plethora of partial synchronization patterns, like chimera states, cluster states or solitary states, may arise. We also focus on the subtle interplay of local dynamics, delay, and the network structure. Synchronization transitions are also associated with nonequilibrium phase transitions and critical collective phenomena.
Biography
Eckehard Schöll is Professor Emeritus of Theoretical Physics at TU Berlin, Germany, and Principal Investigator of the Bernstein Center for Computational Neuroscience Berlin. He holds PhD degrees in mathematics from the University of Southampton/UK and in physics from RWTH Aachen/Germany, and an Honorary Doctorate from Saratov State University/Russia. He is President of the International Physics and Control Society (IPACS), a member of the German Physical Society (DPG, Badge of Honor 2018), a member of the Italian Society for Chaos and Complexity (SICC), and a Board member and a Fellow of the Network Science Society. He is Speciality Chief Editor of the open access Journal Frontiers in Network Physiology: Networks of Dynamical Systems. He has authored more than 600 publications in peer-reviewed journals (Hirsch index h=83, google scholar) and 3 books (among these the Handbook of Chaos Control), and is editor of 5 books and 14 topical journal issues. He is an expert in the field of nonlinear dynamical systems and complex networks. His work pertains to a wide area of research in the fields of mathematics and physics, particularly semiconductor physics, laser physics, computational neuroscience, synchronization of complex systems and networks, time-delayed feedback control, and bifurcation theory. His latest research is also related to topics in biology and technology, e.g. simulation of the dynamics in physiological or neuronal networks and power grids. He is one of the forerunners into the research of chimera states.
Luciano Pietronero graduated in Physics in 1971 in Rome. After several experiences abroad in the corporate research sector (Xerox Research Center Webster in New York and Brown Boveri Research Center in Switzerland), he was a full professor of Condensed Matter Physics at the University of Groningen in the Netherlands and then at the University of Rome “Sapienza”. In 2004, he founded the Institute of Complex Systems (ISC) of the CNR. In 2007, he was the president of the 23rd edition of the STATPHYS conference, and in 2008, he won the Enrico Fermi prize, the most important of the Italian Physics Society. Author or co-author of more than 400 scientific publications; in 1987, he introduced the concept of fractal cosmology and in 2012, the Economic Fitness and Complexity model. Mentor of a generation of young scientists in the fields of complex systems, statistical mechanics and superconductivity at high temperatures, since 2019, he has been President of the Enrico Fermi Historical Museum of Physics and Study and Research Centre.