Biography

Prof. Alexandra Brintrup is Professor in Digital Manufacturing at the University of Cambridge’s Engineering Department, where she leads the Supply Chain AI Lab. She also leads Digital Manufacturing at the Alan Turing Institute, is external faculty at the Complexity Science Hub Vienna, and is a fellow of Darwin College.

Prof. Brintrup was the first researcher to empirically study large-scale supply chains as complex adaptive networks, examine their emergent properties, and take a data-driven perspective to characterise their resilience, which led to understanding of universal patterns that govern supply chains. She was also the first to develop algorithms to predict supply chain dependencies and disruptions. Over the past decade she advised policy makers, and national and European scientific committees, and worked with both start ups , SMEs and international organisations. She is a member of the All Party Parliamentary Groups in Artificial Intelligence and Data Analytics, and advises policy development in supply chain risk, economic performance and resilience. Her current research includes: Predictive methods for automated detection of supply chain dependencies, especially with collective learning paradigms; complex system approaches to model emergence in supply networks, autonomous and scalable optimisation and distributed decision making technologies, particularly with nature-inspired algorithms and Multi-agent Systems.

Abstract

Supply networks emerge as companies procure goods and services from one another to produce their own products – forming the backbone of modern economy. As supply networks become increasingly volatile and weaponised, the study of their structural properties has become critical. Studies conducted in the past two decades have shown that although they exhibit similar organisational patterns to other network types, they also carry distinct characteristics impacting their efficiency and robustness. This talk will give a review of the current state of the art, and touch upon intersections between AI and network science in the study of complex supply networks, posing AI as an enabler to predict network formation, as a theory builder to help us understand supply networks, and as a solution finder to nudge their reconfiguration. We will then discuss the potential pitfalls and challenges, such as loss of data and decision traceability, lack of accountability, and cognitive atrophy. The talk concludes with supply chain management needing to become an irrevocably interdisciplinary field, with a call to complex network scientists to study them.

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.

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

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.

Biography

Kimmo Kaski, (DPhil in Theoretical Physics, University of Oxford) is Professor of Computational Science at Aalto University School of Science (formerly Helsinki University of Technology) and has been Academy Professor and Director of the Centre of Excellence in Computational Complex Systems Research. He is a Supernumerary Fellow of Wolfson College, University of Oxford, External Faculty of Complexity Science Hub Vienna, and Fellows of American Physical Society (APS), Institute of Physics (IOP) and Chartered Physicist UK, and members of Finnish Academy of Sciences and Letters (vice-president (2020-2021) and president (2022-2023)), Academia Europeae, and Academia Mexicana de Ciencas. His interdisciplinary research interests are in Computational Science, Statistical Physics, Complexity Science, Complex Systems and Networks, Social Physics, and Complexity Data Science, including Simulation and AI, as well as their applications to various Socio-technical and Cyber-physical Systems.

Abstract

Social Physics of today focuses on investigating social and societal phenomena in today’s large-scale socio-technical networks using data analytics and computational modelling, to gain insight into the structures and processes of these networks. This has - among a few other studies -been demonstrated by our analysis of a large mobile phone dataset, where we find the social network having a modular structure of communities with strong internal and weak external ties. As the data include phone users’ demographics, i.e., gender and age, we have investigated the nature of social interactions from an egocentric perspective to gain insight into gender- and age-related social behavior patterns and dynamics of human relationships across the lifespan. Moreover, fusing openly accessible data from country statistics and geophysical records with the mobile phone data, we have been able to decipher the daily and seasonal activity patterns of individuals and society. In these studies, computational modelling plays a key role in finding plausible mechanisms for the formation of social ties and dynamics such as spreading in socio-technical networks. As a follow-up to the latter, we have developed a computational model to investigate influence spreading processes in large complex networks, such as cyber-attacks against service or software vulnerabilities in computer networks.

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

Bruno Lepri is a senior researcher at Fondazione Bruno Kessler (Trento, Italy) where he leads the Mobile and Social Computing Lab (MobS). He is co-director of the Center for Computational Social Science and Human Dynamics and of the Ellis Unit Trento, two joint initiatives between Fondazione Bruno Kessler and the University of Trento. Since July 2022, he is the Chief Scientific Office of Ipazia, a company active on generative AI agents and solutions. From 2019 to 2022, Bruno was also the Chief AI Scientist of ManpowerGroup where he has collaborated with the global innovation team on AI projects for recruitment and HR management. Bruno is also a senior research affiliate at Data-Pop Alliance, the first think-tank on big data and development co-created by the Harvard Humanitarian Initiative and the MIT Media Lab as well as a fellow of the ELLIS Human-centric Machine Learning program. In 2010 he won a Marie Curie Cofund postdoc fellowship and he has held a 3 year postdoc position at the MIT Media Lab. He holds a Ph.D. in Computer Science from the University of Trento. His research interests include computational social science, collective behaviors of generative AI agents, human-centric machine learning, network science, graph neural network and multimodal analysis of human behaviors. His research has received attention from several international press outlets and obtained the 10-year impact award at MUM 2021, the James Chen Annual Award for best 2016 UMUAI paper, and the best paper awards at ACM Ubicomp 2014 and RecSys 2025. His past work on personal data management was one of the case studies discussed at the World Economic Forum.

Abstract

Generative AI is moving from systems that act in isolation toward networks of agents that interact, collaborate, negotiate, and coordinate with one another and with humans. The central argument of this keynote is that the capabilities and risks of these systems will increasingly be determined not only by the intelligence of any single agent, but by the patterns of interaction through which agents produce collective behaviour. As agents become embedded in open-ended environments, the key challenge is to engineer and govern the social intelligence that allows them to coordinate effectively, adapt to others, and remain accountable. This shift changes what it means to build capable AI. Improving individual agents remains important, but it is only the first step: once agents interact, their decisions shape one another, local strategies can generate system-level outcomes, and new forms of cooperation, conflict, and coordination can emerge. Understanding these dynamics requires moving beyond benchmarks of isolated performance toward models and evaluations of collective behaviour, social expectations, norms, coordination, and emergent collective intelligence. I will present a conceptual framework for understanding, designing, and governing multi-agent GenAI systems, with particular attention to the risks that arise from interaction itself. The keynote will discuss how insights from artificial intelligence, computational social science, cognitive science, and the study of human collective behaviour can inform the design of robust, accountable, and scalable agentic systems. Ultimately, the challenge is to move from intelligent agents to intelligent collectives: AI systems capable not only of reasoning and acting, but of understanding their social environment, coordinating with others, and behaving responsibly within complex human and machine societies.