University of Oxford, UK
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).
Carnegie Mellon University, USA
Kathleen Carley is a professor in the Engineering and Public Policy Department, Computer Science Department, and Social and Decision Sciences Department at Carnegie Mellon University. She is the director of the Center for Computational Analysis of Social and Organizational Systems (CASOS), a university wide interdisciplinary center that brings together network analysis, computer science and organization scienceOpens in new window, and has an associated NSF-funded training program for Ph.D. students. Carley’s research combines cognitive science, social networks, and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, the impact of telecommunication technologies and policy on communication, information diffusion, and disease contagion and response within and among groups particularly in disaster or crisis situations.
Carley and her lab have developed infrastructure tools for analyzing large-scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining system for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource, and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE, a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces.
Carley is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS), which has more than 25 members, both students and research staff. She is the founding co-editor with Al Wallace of the journal Computational Organization Theory and has co-edited several books in the computational organizations and dynamic network area.
Manlio DE DOMENICO
University of Padua, Italy
Manlio De Domenico is ab associate Professor of Applied Physics and Head of the Complex Multilayer Networks (CoMuNe) Lab at the Department of Physics and Astronomy 'Galileo Galilei' of the University of Padua.
His research activity is at the edge of theoretical, experimental and computational aspects of statistical physics of complex systems, where theory is used to make hypothesis about empirical phenomena in biological, ecological, socio-technical and socio-ecological sciences, which are then validated on real (sometimes massive) data sets. To date, he has applied such tools to:
- The interactome of human and several other organisms
- The human, macaque and C. Elegans connectomes
- A variety of socio-ecological and socio-technical ecosystems
- The Internet and the Dark Web
- A variety of transportation infrastructures, including the global airport network, rail networks, road networks and multimodal urban transportation means
A (non-exhaustive) list of his current activities includes:
- The mathematical formulation of multiplex networks, the study of their structure and of dynamical processes on such systems, the study of their resilience to random or targeted pertubations
- The formulation of an appropriate statistical physics/information theory of complex networks
- The formulation of a geometry of network-driven processes
- The application of advanced mathematical techniques to reduce the complexity of networked systems
- The functional representation of a system from the measurement of signals produced by its units, with application to human brain, human interactome, climate change and social systems
He also finds enough time to investigate hidden structural and dynamical patterns in complex real and virtual time-varying networks, with particular attention to social, biological and economic systems. Indeed, he develops models and simulations for human mobility, the spreading of epidemics and of information in real-world social networks.
University of Michigan, USA
Danai Koutra is an Associate Director of the Michigan Institute for Data Science (MIDAS) and an Associate Professor in Computer Science and Engineering at the University of Michigan, where she leads the Graph Exploration and Mining at Scale (GEMS) Lab. She is also an Amazon Scholar. Her research focuses on principled, practical, and scalable methods for large-scale real networks, and her interests include graph summarization, graph representation learning, graph neural networks, knowledge graph mining, similarity and alignment, temporal graph mining, and anomaly detection. She has won an NSF CAREER award, an ARO Young Investigator award, the 2020 SIGKDD Rising Star Award, research faculty awards from Google, Amazon, Facebook and Adobe, a Precision Health Investigator award, the 2016 ACM SIGKDD Dissertation award, and an honorable mention for the SCS Doctoral Dissertation Award (CMU). She holds a patent on bipartite graph alignment, and has 8 award-winning papers in top data mining conferences. Over time, she has held a variety of service roles: She is an Associate Editor of ACM Transactions on Knowledge Discovery from Data (TKDD) and a program co-chair for ECML/PKDD 2023. She was a track co-chair for The Web Conference 2022, a co-chair of the Deep Learning Day at KDD 2022, the Secretary of the new SIAG on Data Science in 2021, and has routinely served in the organizing committees of all the major data mining conferences. She has worked at IBM, Microsoft Research, and Technicolor Research. She earned her Ph.D. and M.S. in Computer Science from CMU, and her diploma in Electrical and Computer Engineering at the National Technical University of Athens.
University Politècnica de Catalunya, Spain
Romualdo Pastor-Satorras is full Professor at the Universitat Politècnica de Catalunya; He received a Ph.D. in Condensed Matter Physics from the Universitat de Barcelona in 1995. He spent four years as a postdoctoral researcher at the Massachusetts Institute of Technology (1996-1998) and The Abdus Salam International Centre for Theoretical Physics, ICTP (1998-2000). He has been a visiting scientist at Yale University (USA), the University of Notre Dame (USA), the Kavli Institute for Theoretical Physics (USA), the Helsinki University of Technology TKK (Finland), Indiana University (USA), and the Institute for Scientific Interchange (ISI) Foundation (Italy). He has been awarded twice with the national “ICREA Academia Prize” by the Government of Catalonia. He has published in more than 180 peer-reviewed journals in statistical physics. The main topics he works on are 1) Topological and temporal properties of natural systems. 2) Dynamical processes and non-equilibrium phase transitions in disordered substrates. 3) Dynamics of social systems. 4) Human activity and dynamics. 5) Non-Markovian temporal networks. 6) Collective motion.
Tao Zhou is the founding director of the Big Data Research Center at the University of Electronic Science and Technology of China. His main research interests include network science (e.g., link prediction, influential node identification, epidemic spreading, etc.) and computational socioeconomics. He has published many research articles in prestigious journals (e.g., Physics Reports, PNAS, Nature Communication, PRL, etc.), which received >34000 citations from Google Scholar, with H-index=86. His works have been reported by many academic medias as Nature News, PNAS News, MIT Technology Review, Sci. Am., PhysOrg.com, My Science, TG Daily, Dutch Science Magazine, Chinese Science News, etc.