Juan Carlos Parra-Alvarez is an Associate Professor at Aarhus University's Department of Economics and Business Economics, with adjunct roles at Aalborg University and research fellowships at CREATES and the Danish Finance Institute. He holds a PhD in Economics and Business from Aarhus University. His research focuses on quantitative methods for macroeconomic and financial analysis, including dynamic equilibrium economies, asset pricing, and disaster risk modeling. Key interests span continuous-time econometrics, heterogeneous agent modeling, and consumption-based capital asset pricing. Recent publications demonstrate concentrated work on refining estimation techniques for DSGE models, solving peso problems in asset pricing, and developing risk-sensitive approximation methods. Awards include the William E. Brigman Award for outstanding graduate research.
Ljubiša Bojić is a Senior Research Fellow at the Digital Society Lab, Institute for Philosophy and Social Theory, University of Belgrade, and at the Institute for Artificial Intelligence of Serbia. He is a communication scientist and futurologist specializing in AI alignment, recommender systems, and the societal impact of emerging technologies. Bojić received his Ph.D. from the University of Lyon II, France in 2014 and has established himself as a leading voice in digital humanism and AI ethics. University of Belgrade, Institute for Philosophy and Social Theory, Digital Society Lab Institute for Artificial Intelligence of Serbia Complexity Science Hub Vienna (External Faculty Member) Bojić's educational background includes a Ph.D. in Communication Science from the University of Lyon II, France, completed in 2014. His academic journey has focused on the intersection of communication theory, digital technologies, and societal impact. Dr. Bojić's research centers on AI alignment with human values , particularly through his theoretical framework for a "CERN for AI" – an international collaborative model for AI testing and alignment. He investigates how recommender algorithms function as powerful social forces that shape human behavior, often creating echo chambers and social polarization. His work on the metaverse examines its addictive potential versus therapeutic promise, while his concept of digital humanism advocates for recognizing digital identity as a human right and recommender systems as public goods. Bojić's approach integrates communication science, futures studies, and ethical frameworks to address the challenges posed by emerging technologies to democracy and human autonomy. Bojić's recent publications reveal a consistent focus on understanding AI's capabilities and limitations, particularly regarding linguistic pragmatics, personality traits in language models, and the societal impact of recommendation algorithms. His work bridges technical AI research with social science perspectives, emphasizing the need for multidisciplinary approaches to AI governance. A notable trend is his advocacy for international collaboration in AI testing and alignment, reflected in his influential "CERN for AI" framework that has been cited by the World Economic Forum and G7 policy documents. Appointed to the United Nations Environment Programme Foresight Expert Panel Featured in "The Edinburgh Companion to the New European Humanities" as a founder of new digital humanities approaches Fellow of the Institute for Human Sciences Vienna (IWM) Associate Editor of Springer's journal "Humanities & Social Sciences Communications" Executive Board Member of Horizon Europe's "TWin of Online Social Networks" Project Bojić founded the EMERGE: Forum on the Future of AI Driven Humanity and initiated the Belgrade Digital Freedom Pledge , which advocates for recognizing recommender systems as public goods and digital identity as a human right. His research has influenced policy recommendations, including a Horizon Europe project that adopted his work to recommend algorithms designed to deliver balanced content that avoids negative bias and promotes diversity. Bojić frequently collaborates with international organizations including the United Nations, Complexity Science Hub Vienna, and various European research networks. As leader of the Digital Society Lab (DigiLab) at the Institute for Philosophy and Social Theory, Bojić directs research on the societal impact of AI and digital technologies. His team develops frameworks for assessing AI alignment with human values and wellbeing, with a particular focus on recommender systems and large language models. The lab's work combines computational methods with social science perspectives to address challenges in digital society.
Professor Alexandra Brintrup is a leading academic in digital manufacturing and supply chain systems. She is Professor in Digital Manufacturing at the University of Cambridge's Department of Engineering and leads the Supply Chain AI Lab. She is also External Faculty at the Complexity Science Hub Vienna and leads Digital Manufacturing research at the Alan Turing Institute. Additionally, she is a Fellow of Darwin College, Cambridge. Her research focuses on understanding supply chains as complex adaptive systems. She pioneered empirical studies on large-scale supply networks, characterizing their resilience and emergent behaviors through data-driven and AI-based methods. Her work includes developing algorithms to predict supply chain dependencies and disruptions, using multi-agent systems and nature-inspired optimization techniques. She actively collaborates with industrial partners such as Boeing, Rolls-Royce, Jaguar Land Rover, Suzuki, and Procter & Gamble. Her recent publications highlight trends in supply chain science, particularly in modeling firm-level networks and informing policy. The 2024 policy brief The Need for a Better Map of the Global Supply Network , co-authored with leading complexity scientists, underscores the importance of systemic mapping for economic resilience and policy design. She advises policy development through her membership in the All-Party Parliamentary Groups on Artificial Intelligence and Data Analytics, contributing to national and European strategies on supply chain risk and economic performance. She has advised multiple scientific committees and government bodies over the past decade, translating complex research into actionable insights. Her work bridges academia, industry, and public policy, with ongoing projects in autonomous decision-making and scalable optimization in distributed supply networks. She is involved in major interdisciplinary initiatives, including the Interdisciplinary Workshop on Firm-Level Supply Networks hosted by the Complexity Science Hub, reflecting her leadership in advancing supply chain science as a cross-cutting discipline.
Siew Ann Cheong is an Associate Professor in the Division of Physics and Applied Physics at the School of Physical & Mathematical Sciences, Nanyang Technological University (NTU), Singapore. He is also an External Faculty member at the Complexity Science Hub (CSH) since 2019. Associate Professor, NTU (2016–present) Assistant Professor, NTU (2007–2016) Postdoctoral Associate, Cornell Theory Center (2006–2007) External Faculty, Complexity Science Hub (2019–present) Educational Background: B.Sc. (Hons) in Physics, National University of Singapore (1997) M.Sc., National University of Singapore (2000) M.Sc., Cornell University (2002) Ph.D. in Theoretical Condensed Matter Physics, Cornell University (2006) Siew Ann Cheong’s research centers on understanding the dynamics of complex systems with many degrees of freedom, such as financial markets, earthquakes, infectious diseases, biological sequences, and social systems. He employs both modeling and data-driven approaches to explore fundamental questions: What makes a system complex? How does complexity emerge? His goal is to develop a computational theory of complex systems by treating their dynamics as information processing. He applies methods from statistical physics, network science, time series analysis, and agent-based modeling to uncover universal principles across disciplines. His recent publications reveal a strong trend toward interdisciplinary research, particularly in econophysics, urban science, and computational history. He frequently uses topological data analysis (TDA), persistent homology, and network-based methods to study financial market crashes, urban gentrification, and knowledge evolution. His work bridges physics with social sciences, ecology, and digital humanities, demonstrating a consistent focus on identifying critical transitions and structural changes in complex systems. Scientific Awards: SPMS Excellence in Teaching Award (2008, 2010, 2011) Nanyang Award for Excellence in Teaching (2010) Science Mentorship Programme Outstanding Mentor Award (2010) Best Paper Award, International Conference on Culture and Computing (2013) Siew Ann Cheong has supervised numerous PhD, undergraduate, and high school research students, contributing significantly to academic mentoring. He has received multiple teaching awards, reflecting his commitment to education. His research is supported by interdisciplinary collaborations and grants, particularly in complex systems and data science. He has also contributed to computational history and heritage impact modeling through projects like SHIFT (Sustainable Heritage Impact Factor Theory). He leads a research group focused on complex systems, with former fellows and students now in academic and research positions worldwide. Labs and Research Groups: While no formal lab name is mentioned, his research is conducted within the Division of Physics and Applied Physics at NTU, involving a team of former and current students and fellows working on complex systems, econophysics, and network science. He collaborates with institutions such as the Complexity Science Hub, National University of Singapore, and international universities.
Roger Cremades is an Associate Professor of Urban Environmental Change at the Sustainability Research Institute, School of Earth and Environment, University of Leeds. He has previously held academic positions at Wageningen University and the Climate Service Center Germany, and his work is deeply rooted in complex systems science applied to global change and urban sustainability. His research focuses on the human and economic dimensions of environmental change, particularly in cities. Key interests include urban resilience , social tipping points , climate-economic systems , and cascading disasters . He integrates agent-based modeling , artificial intelligence , and behavioral science to simulate complex urban systems and co-produce tools with societal stakeholders for sustainable transformations. The recent publications highlight a strong trend toward data-rich, AI-enhanced simulations of urban systems under global crises, with a focus on food systems, behavioral dynamics, and cross-scale sustainability transformations. His work consistently bridges computational modeling and real-world policy applications. Scientific Leadership and Service: Member, Executive Board, Complex Systems Society Member, Executive Board, Network for Computational Modeling in the Social and Ecological Sciences Editorial Board Member, Complexity (Wiley) Editorial Board Member, PLOS Complex Systems Roger has coordinated major research projects on the complex dynamics of global change in urban environments and has pioneered methods for integrating stakeholder knowledge into modeling processes. His PhD, funded by the Max Planck Society, was supervised by Nobel laureate Klaus Hasselmann, underscoring the high caliber of his academic training. He continues to lead cutting-edge research at the intersection of climate science, economics, and urban systems. He is actively involved in labs and research groups focused on computational modeling of socio-ecological systems, urban sustainability, and crisis response, often working in transdisciplinary teams that include policymakers, data scientists, and community representatives.
Elma Dervic is a Postdoctoral Researcher at the Section for Science of Complex Systems at the Medical University of Vienna and a resident scientist at the Complexity Science Hub Vienna. Her work integrates data science, network science, and machine learning to address complex challenges in healthcare, human migration, and supply chain resilience. Research Interests: Machine Learning and Network Science for health data Modeling disease trajectories and comorbidity networks Systemic risk analysis in global supply chains Migration dynamics and refugee integration Agent-based modeling of healthcare systems Her recent research has focused on unraveling cradle-to-grave disease trajectories using population-wide health data and analyzing systemic risks in antibiotic and food supply chains. Her publications span high-impact journals such as Nature Communications , Nature Human Behaviour , and Translational Psychiatry . The articles reflect a strong interdisciplinary trend, combining medical data with network theory and machine learning to uncover hidden patterns in disease progression, gender disparities in health outcomes, and structural vulnerabilities in critical systems. Scientific Awards: Life Sciences Research Award 2024 – for breakthroughs in understanding disease trajectories Elma has been actively involved in advising and collaborative research, contributing to major projects such as ASCII (Austrian Supply Chain Intelligence Institute), D4Dairy, and MigraCapax. She has also secured research visibility through TEDx talks and media coverage in outlets like International Business Times and US Pharmacist. Her work on data transparency in medical research has led to the creation of open datasets, including the CSH Covid-19 Control Strategies List. Labs and Research Teams: Complexity Science Hub Vienna (CSH) Section for Science of Complex Systems, Medical University of Vienna Co-founder of BeeAnd.me – an IoT startup applying data science to beekeeping Core member of the Montenegrin AI Association (MAIA)
J. Doyne Farmer is the Baillie Gifford Professor of Complex Systems Science at the School of Geography and the Environment, University of Oxford, and Director of the Complexity Economics programme at the Institute for New Economic Thinking (INET) at the Oxford Martin School. He is also a Senior Associate Research Fellow at Christ Church College, Oxford, and an External Professor at the Santa Fe Institute. Farmer serves as Chief Scientist and CEO at Macrocosm, a company applying complexity science to the green energy transition. University: University of Oxford School: School of Geography and the Environment Key Affiliations: INET Oxford, Christ Church College, Santa Fe Institute, Macrocosm Farmer’s research centers on complexity economics, utilizing agent-based modeling and big data to understand financial instability, technological progress, and economic resilience. His work bridges theoretical physics and economics, focusing on how production and supply networks propagate shocks, as seen during the COVID-19 pandemic. He advocates for a scientific revolution in economics through data-driven, realistic simulations. His recent publications (2020–2024) emphasize supply chain science, economic modeling during crises, and the application of complex systems to policy. Key themes include input-output modeling, network resilience, health-economy tradeoffs, and the green transition. His 2024 book, Making Sense of Chaos: A Better Economics for a Better World , presents a manifesto for reforming economic science. Oppenheimer Fellow Farmer has founded research groups and companies, including the Complex Systems Group at Los Alamos and Prediction Company. He currently leads research initiatives on global supply networks and advises on economic policy. His work is supported by interdisciplinary collaborations and data-driven projects. He also leads the development of new modeling frameworks at Macrocosm to guide sustainable economic transformation. Farmer is a key figure in the Complexity Economics programme at INET Oxford and leads collaborative efforts such as building a global map of supply networks. He is involved with the Centre for the Study of Existential Risk (CSH) and promotes international scientific alliances to address systemic risks.
Rudolf Hanel is an Associate Professor at the Medical University of Vienna and a faculty member at the Complexity Science Hub. His interdisciplinary research bridges theoretical physics, complex systems, medical imaging, and socio-economic modeling. He is deeply involved in advancing the foundations of statistical mechanics and non-equilibrium thermodynamics. His research interests include complex systems, non-equilibrium thermodynamics, information theory, medical robotics, and social physics. He investigates how systems evolve far from equilibrium, focusing on phase transitions, tipping points, and the emergence of structure. His work applies these principles to diverse domains such as firm dynamics, social cohesion, and pandemic response. The recent trend in his publications reveals a strong focus on generalized entropy, sample space reduction, network-based social modeling, and practical applications in public health. His articles span foundational physics, computational medicine, and socio-economic systems, reflecting a unifying framework of complexity science across disciplines. Thermodynamics of driven systems Generalized entropy and information theory Social fragmentation and homophily Pooled testing for pandemics Firm performance via information consumption Structure-forming systems Rudolf Hanel has received no explicitly mentioned scientific awards in the provided text. He has not been stated to advise any formal students, though he collaborates widely with researchers such as Stefan Thurner, Jan Korbel, and Peter Klimek. He has contributed to major interdisciplinary grants and projects, particularly through the Complexity Science Hub, including work on pandemic testing strategies and economic modeling. He is a key member of the Complexity Science Hub, where he collaborates on foundational and applied research in complex systems. The Hub serves as a central platform for his work in integrating physics-inspired models into social, biological, and economic systems.
Atushi Ishikawa is a Professor at Kanazawa Gakuin University in the Faculty of Information Engineering. His research focuses on econophysics and computational social science, particularly statistical patterns in economic and natural systems. Education: PhD in Physics, Graduate School of Science, Osaka University (1994) JSPS Research Fellow, Yukawa Institute for Theoretical Physics, Kyoto University His research interests lie at the intersection of physics and social systems. He investigates statistical laws in firm-size variables such as sales and employee counts, extending to earthquake statistics and human movement trajectories . His work applies methods from statistical physics to complex real-world data, contributing to econophysics and data science. He employs computational modeling and large-scale data analysis to uncover universal patterns across diverse domains. The recent publications and events (2010–2025) reflect a sustained engagement with econophysics , complexity science , and data visualization . Themes include interdisciplinary workshops (e.g., CSH Workshop), colloquia, and project-based research, indicating an active role in the scientific community and applied research initiatives. Scientific Awards: No awards listed in the provided text. Regarding advising and grants, there is no explicit mention of students, funded projects, or research grants in the available data. However, his long-standing research trajectory and participation in international workshops suggest potential involvement in collaborative and grant-funded work. He leads or contributes to research projects involving data visualization and complex systems analysis. He is involved in organizing and participating in academic events such as the Econophysics Colloquium and the Complexity Science Hub Workshop , which serve as platforms for interdisciplinary exchange. These activities indicate an active research group or team focused on computational social science and data-driven modeling, likely involving students and collaborators in project development and scientific dissemination.
Peter Klimek is a faculty member at the Complexity Science Hub and holds an associate professorship at the Medical University of Vienna. His interdisciplinary research integrates complexity science, data science, statistics, and physics to model and predict behaviors in socio-economic and healthcare systems. He is also the director of the Supply Chain Intelligence Institute Austria (ASCII), where he leads research on supply chain resilience and economic networks. PhD in Physics (2010) Venia Docendi (Habilitation) in Computational Science (2018) Director, Supply Chain Intelligence Institute Austria (ASCII) His research interests center on complex systems , with applications in healthcare modeling (e.g., comorbidity networks, disease progression, pandemic forecasting), economic and financial systems (e.g., shock propagation, trade disruptions), and election forensics . He has developed novel statistical methods to detect electoral fraud and stress-test models for healthcare workforce resilience and financial market stability. The most recent publications reflect a strong trend in data-driven policy modeling , combining population-scale health data, economic input-output models, and network science. Key themes include the long-term health effects of famine, antibiotic and food supply chain vulnerabilities, and gender disparities in chronic disease outcomes. His work leverages machine learning, agent-based simulations, and forensic data analysis to inform public policy. Paul Watzlawick Ring of Honor (2021) Austrian Scientist of the Year 2021 Klimek has advised on major public health initiatives, including the Austrian government’s COVID-19 forecasting model. He has no listed students in the provided text but collaborates extensively within large research teams. He leads initiatives on national medical data infrastructure and digital transformation in agriculture (e.g., 'Internet of Cows'). His lab and team focus on building digital twins of complex systems, with applications in healthcare, supply chains, and socio-political systems.
Jana Lasser is Professor for Data Analysis at the University of Graz and leads the Complex Social & Computational Systems research group at the interdisciplinary IDea_Lab. She is also Associate Faculty at the Complexity Science Hub Vienna (CSH), reflecting her deep engagement with complex systems research across institutions. Her educational background includes a PhD in Physics from Georg-August-University of Göttingen, based on research at the Max Planck Institute for Dynamics and Self-Organization. She held postdoctoral and visiting positions at the Medical University of Vienna, Graz University of Technology (as a Marie Curie Fellow), and RWTH Aachen (as interim professor) before joining the University of Graz in 2024. Her research centers on emergent phenomena in complex social systems, using machine learning, data science, NLP, and computational modeling. Key interests include misinformation, counterspeech, social media algorithms, mental health in academia, and pattern formation in geophysical systems. She is a leading voice in open science, data literacy, and reforming academic culture. Her recent publications (2023–2025) reveal a strong interdisciplinary trend, bridging computational social science, public health, political communication, and geophysics. Many papers focus on misinformation, political discourse, and algorithmic governance, often using large-scale social media data. Others explore mental health, academic labor, and foundational geophysical processes, demonstrating her wide-ranging analytical expertise. ERC Starting Grant 101160928 (DeSiRe) FWF standalone project P 37280-N netidee SCIENCE prize Open Knowledge Fellow of the Wikimedia Foundation (2019/2020) Marie Curie Fellow Jana Lasser actively mentors and advises through her leadership of major research projects and initiatives. She leads the Survey Special Interest Group in the COST Action on Researcher Mental Health and co-founded the Network Against Abuse of Power in Science. Her research has been supported by prestigious grants including an ERC Starting Grant and FWF funding. She has developed and taught numerous open-access courses in computational social science, Python, and data literacy, emphasizing reproducibility and educational outreach. She leads the Complex Social & Computational Systems research group at IDea_Lab and is a key figure in the Complexity Science Hub Vienna. Her work on the Schwurbelarchiv and agent-based models for healthcare resilience demonstrates her leadership in building and utilizing large-scale data infrastructures for societal benefit.
Simon Levin is the James S. McDonnell Distinguished University Professor in the Department of Ecology and Evolutionary Biology at Princeton University. He is also the Director of the Center for BioComplexity. His work bridges theoretical ecology, biodiversity science, and socioeconomic systems, with a strong emphasis on scaling principles and sustainability. Levin's research focuses on the dynamics of biological diversity across scales, from molecular to global systems. He investigates how ecological and evolutionary theory can inform sustainable management of natural resources, especially in the context of public goods and common-pool resources. His lab employs mathematical models and empirical data to explore collective behavior, ecosystem resilience, and biocomplexity. His recent publications reveal a strong trend toward integrating human behavior into ecological and epidemiological models, exploring prosocial behavior, risk compensation, and invasion dynamics. This reflects a growing emphasis on social-ecological systems and the role of human agency in environmental outcomes. Levin has advised several students and postdoctoral researchers, including Xander Huggins and Ryan Chisholm, both of whom have received notable recognition in ecology. His lab continues to publish in high-impact journals such as PNAS , Current Biology , and Journal of Theoretical Biology . He is actively engaged in research and shows no signs of retirement or former status, maintaining a current email and ongoing publication record.
Matjaz Perc is a Professor of Physics at the University of Maribor and Vice Dean of Natural Sciences at the European Academy of Sciences and Arts. He is a member of Academia Europaea and the European Academy of Sciences and Arts, and recognized among the top 1% most cited physicists by Clarivate Analytics in 2020. His research interests span Complex Systems , Network Science , Sociophysics , Game Theory , Evolution of Cooperation , Neural Networks , and Epidemic Modeling . He applies physics-based approaches to social, biological, and economic systems, often integrating AI and data analytics. The recent trend in his publications shows a strong focus on higher-order interactions in networks , cooperation dynamics , synchronization in neural systems , epidemic modeling on simplicial complexes , and AI-driven decision-making . His work bridges physics with applications in healthcare, economics, and sustainability. Young Scientist Award for Socio and Econophysics, German Physical Society, 2015 USERN Laureate, 2017 Zois Award, 2018 Fellow of the American Physical Society, 2019 Top 1% Most Cited Physicist, Clarivate Analytics, 2020 Matjaz Perc has not publicly listed his advisees, but he leads a prolific research group with extensive international collaborations. He has contributed to major interdisciplinary projects, including pan-European responses to the COVID-19 pandemic. His work is supported by high-impact publications and recognition from leading scientific academies. He is actively involved in promoting network science and complex systems research through editorial roles and collaborative platforms.
Alessandro Pluchino is an Associate Professor of Theoretical Physics, Mathematical Methods and Models at the Department of Physics and Astronomy "E. Majorana", University of Catania. He holds the qualification of Full Professor in Theoretical Physics of Fundamental Interactions and serves as a research delegate at INFN, local coordinator of LINCOLN (Learning Complex Networks), and staff member of DYNSYSMATH. He is also a member of the Società Italiana di Fisica and the Complex Systems Society, and the University of Catania referent for the Piano Nazionale Lauree Scientifiche (PLS) in Physics. His research spans the modeling and simulation of complex systems using agent-based models and network analysis, with applications in biological, ecological, economic, and social systems. He also investigates fundamental physics, statistical mechanics, chaos theory, and complex networks. His work extends to optimization methods in smart cities, sustainability, energy, and transportation infrastructure. Pluchino has authored over 100 scientific publications and several books. His recent publications focus on career dynamics in sports and seismic vulnerability using machine learning, reflecting a strong interdisciplinary trend combining physics, data science, and societal applications. His scientific achievements have been recognized with two Ig-Nobel Prizes: in 2010 for Management, for demonstrating mathematically that random promotions improve organizational efficiency, and in 2022 for Economics, for studying the role of luck in success—both shared with Andrea Rapisarda and collaborators. He actively supervises numerous PhD and master’s students across physics, engineering, and interdisciplinary programs, and teaches courses such as Agent-Based Models, Dynamic Systems and Chaos, History of Physics and Epistemology, and General Physics. He is an editorial board member of PLOS ONE, Entropy, and Frontiers in Physics, underscoring his influence in the scientific community. Pluchino leads and participates in several research teams and networks, including LINCOLN and DYNSYSMATH, and is deeply engaged in scientific dissemination for non-specialist audiences.
Andrea Rapisarda is a Full Professor of Theoretical Physics at the University of Catania, Italy, affiliated with the Department of Physics and Astronomy "Ettore Majorana" and INFN Catania. He is the coordinator of the PhD program in Complex Systems for Physical, Socio-economic and Life Sciences and serves as co-director of the International School on Complexity at the Ettore Majorana Foundation in Erice. Additionally, he is an External Faculty member at the Complexity Science Hub Vienna, reflecting his international academic engagement. His research spans Complex Systems, Statistical Mechanics, Deterministic Chaos, Nonlinear Dynamics, Complex Networks, and Agent-Based Models , with a strong emphasis on applications to socio-economic systems. He investigates the role of randomness, luck, and inefficiencies in hierarchical organizations, drawing from interdisciplinary methods in physics and computational modeling. His work has demonstrated how random strategies can outperform merit-based ones in organizational efficiency and democratic design. The 15 most recent publications highlight a consistent focus on modeling randomness in success, inequality, and decision-making across domains such as sports, economics, politics, and public health. His work integrates agent-based simulations, network analysis, and statistical physics to understand complex phenomena, often with policy-relevant implications. Ig Nobel Prize for Management (2010) Ig Nobel Prize for Economics (2022) Rapisarda is an active advisor and researcher, though specific student names are not listed in the provided texts. He has contributed to numerous editorial boards, including Physica A, Entropy, Frontiers in Physics (Social Physics) , and Complexity . He has also been involved in public outreach, policy discussions on democratic reform via sortition, and science communication through media interviews and public lectures. He leads research projects on improving organizational and societal systems using insights from complexity science.
