Speakers

More speakers will be announced soon, stay tuned on our social media channels to find out (Bluesky, X, LinkedIn, and Instagram).

Joint Keynote Speakers

Duncan J. Watts

University of Pennsylvania

Duncan Watts is a computational social scientist interested in social and organizational networks, collective dynamics of human systems, web-based experiments, and analysis of large-scale digital data, including the production, consumption, and absorption of news. He is the Stevens University Professor and Penn Integrates Knowledge University Professor at the University of Pennsylvania, where he directs the Computational Social Science Lab. Professor Watts holds appointments in the Annenberg School for Communication, the Department of Computer and Information Science in the School of Engineering and Applied Science, and the Department of Operations, Information and Decisions in the Wharton School. Previously, he was a principal researcher and founding member of the Microsoft Research NYC lab, an A.D. White Professor-at-Large at Cornell, a professor of Sociology at Columbia, and a principal research scientist at Yahoo! Research, where he led the Human Social Dynamics group. Professor Watts is the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton 2003), Small Worlds: The Dynamics of Networks Between Order and Randomness (Princeton University Press 1999), and Everything Is Obvious: Once You Know The Answer (Crown Business 2011). He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University. He was named an inaugural fellow of the Network Science Society in 2018, a Carnegie Fellow in 2020, a fellow of the American Association for the Advancement of Science in 2021, and the National Academy of Science in 2023.

Steven Strogatz

Cornell University

Steven Strogatz is an applied mathematician interested in nonlinear dynamics and chaos, small-world networks and synchronization, mathematical biology, and the public understanding of mathematics. He is the Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics at Cornell University. Strogatz graduated summa cum laude in mathematics from Princeton University, was a Marshall Scholar at Trinity College, Cambridge, and did his doctoral work in applied mathematics at Harvard University, followed by a National Science Foundation postdoctoral fellowship at Harvard and Boston University. He previously taught in the Department of Mathematics at MIT before joining the Cornell faculty in 1994. Professor Strogatz is a leading public communicator of mathematics through his New York Times series “The Elements of Math,” “Me, Myself and Math,” and “Math, Revealed.” He is also the author of Nonlinear Dynamics and Chaos (1994), Sync (2003), The Calculus of Friendship (2009), and The Joy of x (2012). His most recent book, Infinite Powers (2019), was a New York Times Best Seller and was shortlisted for the 2019 Royal Society Science Book Prize. In 2024, Strogatz was elected to the National Academy of Sciences. He is also a Fellow of the Society for Industrial and Applied Mathematics (2009), the American Academy of Arts and Sciences (2012), the American Physical Society (2014), the American Mathematical Society (2016), and the Network Science Society (2018).

Albert-László Barabási

Northeastern University

Albert-László Barabási is a University Distinguished Professor at Northeastern University, where he directs the Center for Complex Network Research at the Network Science Institute. He also holds an appointment in the Department of Medicine at Harvard Medical School and Brigham and Women’s Hospital, and he is a visiting professor in the Department of Network and Data Science at Central European University in Budapest. Trained as a theoretical physicist, he earned his master’s degree at Eötvös Loránd University in Budapest and his PhD at Boston University. Professor Barabási’s work helped establish network science as a quantitative framework for complex systems, including the discovery and characterization of scale-free networks and the Barabási-Albert model explaining how growth and preferential attachment shape many real‑world networks. His research spans the statistical physics of networks and applications ranging from network robustness and controllability to network medicine, where he helped catalyze a network-based view of disease and comorbidity. He has also played a key leadership role in the field as the founding president of the Network Science Society. Professor Barabási is the author of influential books including Linked, Bursts, Network Science, and The Formula, and among his honors include election to the U.S. National Academy of Sciences (2024) and the American Physical Society’s Julius Edgar Lilienfeld Prize (2023).

Réka Albert

Penn State

Réka Albert is the Evan Pugh University Professor and Distinguished Professor of Physics and Biology at The Pennsylvania State University. She earned her PhD in physics at the University of Notre Dame, after completing her BS and MS at Babeș‑Bolyai University in Cluj‑Napoca, Romania. A pioneer of network science, Albert co-developed the Barabási-Albert model for the emergence of scale-free networks and has helped shape how researchers represent and analyze complex systems across disciplines. Her current research seeks to uncover the organizing principles that determine the dynamic repertoire of complex networks, with a particular emphasis on biological networks. She was among the pioneers of logical (Boolean) modeling of biological systems, and her group’s models yield both specific biological predictions and general insights into how network structure constrains dynamics. Working in close dialogue with wet‑bench biologists and clinicians, she has advanced network-based control ideas to propose interventions that mitigate dysregulation in biological systems, with validations spanning contexts such as signal transduction in plant and human cells and immune responses to pathogens. Professor Albert is a Fellow of the American Physical Society, the American Association for the Advancement of Science, and the Network Science Society, and an external member of the Hungarian Academy of Sciences. She was elected to the National Academy of Sciences in 2025 and has been recognized with honors including the APS Maria Goeppert‑Mayer Award and Penn State’s Eberly College of Science Distinguished Mentoring Award.

Keynote Speakers

Yong-Yeol (YY) Ahn

University of Virginia

Yong-Yeol (YY) Ahn is a network and data scientist whose work combines network science, machine learning, and the study of complex social, biological, and information systems. He is a Quantitative Foundation Distinguished Professor at the University of Virginia’s School of Data Science. Before joining UVA, he was a Professor at Indiana University’s CNetS, Luddy School of Informatics, Computing, and Engineering, and a Visiting Professor at MIT. Earlier, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after completing his PhD in Statistical Physics from KAIST. His research focuses on the architectures of complex systems—how networks shape behavior, cognition, and scientific progress—and on developing methods in network analysis, machine learning, and natural language processing to investigate these mechanisms at scale. He is the co-author of “Working with Network Data.” His work has been recognized with several honors, including the Microsoft Research Faculty Fellowship.

Moon Duchin

The University of Chicago

Moon Duchin is Professor of Computer Science and Data Science at the University of Chicago and the faculty director of Data & Democracy, a research initiative jointly run by the Data Science Institute and the Center for Effective Government that investigates how digital media, data, and algorithms shape political behavior, misinformation, and democratic institutions. Duchin leads the Data and Democracy Lab, a multidisciplinary team spanning mathematics, computing, geography, law, and public policy. The Lab develops data-science interventions for democracy—from redistricting and community mapping to ranked choice voting—builds open-source tools for election analysis, partners with civil-rights organizations to strengthen voting-rights protections, and provides expert consulting to stakeholders across the political spectrum. Professor Duchin has served as an expert witness in court cases across the United States and has built training programs that prepare mathematicians and data scientists to engage in election-related litigation and policy processes. Her earlier academic appointments include faculty positions at Tufts University, Cornell University, and the University of Michigan, as well as postdoctoral work at the University of California, Davis. She is an external faculty member of the Santa Fe Institute. Her scholarship has been recognized widely, including an NSF CAREER award, a Guggenheim Fellowship, a Radcliffe Institute Fellowship, a Sloan Professorship, and election as a Fellow of the American Mathematical Society.

Alessia Melegaro

Bocconi University

Alessia Melegaro is a Full Professor of Demography and Social Statistics in Bocconi University's Department of Social and Political Sciences and a Research Associate at the Dondena Centre for Research on Social Dynamics and Public Policy. She directs Bocconi’s Covid Crisis Lab, a multidisciplinary laboratory for research on the epidemiological, economic, and social dimensions of health crises. Trained as an economist, she earned a PhD in Biological Sciences (Ecology and Epidemiology) from the University of Warwick and previously worked in the Modelling and Economics Unit at the UK Health Protection Agency. Her research develops mathematical and statistical models to study infectious-disease transmission and to evaluate the effectiveness and cost-effectiveness of public-health interventions—particularly vaccination strategies—as well as vaccine hesitancy and compliance with health measures. A distinctive feature of her work is the integration of demographic structure, social contact patterns, and behavioral dynamics into epidemiological modeling, including field studies using contact diaries and RFID sensors to measure age-structured mixing in low- and middle-income settings. Her work has been supported by two European Research Council grants—a Starting Grant (DECIDE, 2012–2017) on demographic change and infectious-disease dynamics and a Consolidator Grant (IMMUNE, 2021–2026) on modeling human behavior in infection spread. During the COVID-19 pandemic, she served on Italy's national scientific technical committee and on the Lombardy regional committee.

Sarika Jalan

Indian Institute of Technology Indore

Sarika Jalan is a Professor in the Department of Physics at the Indian Institute of Technology Indore, where she works at the interface of nonlinear dynamics, complex systems, and network science. Her research investigates how network structure shapes collective behavior in coupled nonlinear systems, with contributions that include the identification and characterization of novel collective states such as cluster synchronization and explosive synchronization. She earned her PhD from the Physical Research Laboratory (Ahmedabad), followed by postdoctoral appointments at the Max Planck Institute for Mathematics in the Sciences (Leipzig) and the Max Planck Institute for the Physics of Complex Systems (Dresden). Prior to joining IIT Indore, she was a Senior Research Scientist at the National University of Singapore. Professor Jalan has authored more than 85 peer‑reviewed publications and has played a prominent leadership role in the community through editorial and professional service. She has served as Editor‑in‑Chief of the Journal of Computational Science and is a Section Editor for Chaos, Solitons & Fractals; she also serves as an Editor for The European Physical Journal B and is on the Editorial Board of Scientific Reports. She is an elected executive member of the Complex Systems Society and a Board Member of the Network Science Society. Her honors include the NetSc Outstanding Service Award (2025), the SERB POWER Fellowship (2022-2025), and nomination to the founding cohort of INSA Women Associates.

Invited Speakers

Ulrik Brandes

ETH Zürich

Ulrik Brandes is Full Professor for Social Networks in the Department of Humanities, Social and Political Sciences at ETH Zürich, where he also serves as Head of Department. Trained in computer science, he earned his PhD from the University of Konstanz with dissertation work in graph drawing and visualization. Professor Brandes previously held academic positions in Germany, including a long tenure as Professor of Algorithmics at the University of Konstanz. Professor Brandes’s research focuses on the methodological and algorithmic foundations of social network science, with influential contributions to centrality, clustering, and network visualization. He is editor of the journal Social Networks and has contributed extensively to the field’s scholarly infrastructure. His work has been recognized with the ESA Test-of-Time Award for the 2003 paper “Experiments on Graph Clustering Algorithms,” written with Marco Gaertler and Dorothea Wagner. In recognition of his lifetime contributions to social network research, he received the 2024 Georg Simmel Award from the International Network for Social Network Analysis (INSNA). In recent years, his work has also extended to sports analytics, developing network- and geometry-informed representations of team shape and spatial organization in football (soccer) using tracking data.

Petra E. Vértes

University of Cambridge

Petra E. Vértes is a Professor in the Department of Psychiatry at the University of Cambridge, where she leads the Systems Neuroscience Lab—an interdisciplinary team working at the intersection of psychiatry, neuroscience, and applied mathematics. Her research integrates computational modelling, network science, and large-scale data analysis to uncover principles of brain organization, function, and dysfunction, with a particular focus on neurodevelopmental disorders across scales from genes and cells to brain networks and behaviour. Alongside human studies, her group uses model systems including C. elegans and cerebral organoids as testbeds for methodological innovation and for understanding network dysfunction and repair. Prof. Vértes received a master’s degree in theoretical physics and a PhD in artificial neural networks, and she has held interdisciplinary roles spanning quantitative science and mental health research. Vértes is also a visiting researcher at the MRC Laboratory of Molecular Biology and a co-founder of the Cambridge Networks Network, which connects researchers applying network science across disciplines. Her work has been recognized internationally, including selection in Foreign Policy’s 2016 “Top 100 Global Thinkers” for research linking brain imaging with gene expression as part of the NSPN consortium.

Diego Garlaschelli

IMT School for Advanced Studies Lucca & Leiden University

Diego Garlaschelli is a Professor of Theoretical Physics at the IMT School for Advanced Studies in Lucca, where he leads the NETWORKS research unit, and at Leiden University’s Lorentz Institute for Theoretical Physics, where he leads the Econophysics and Network Theory group. He is an External Faculty member at the Complexity Science Hub in Vienna and an associate member of the Enrico Fermi Research Center (CREF) in Rome, and he is a cofounder of the Dutch Network Science Society (NetSci.nl). His research combines statistical physics, information theory, and random-graph modeling to build principled maximum-entropy null models for network reconstruction and statistical pattern detection, with applications spanning financial and economic networks, social dynamics, and biological and ecological systems. He is the co-author (with Tiziano Squartini) of the monograph Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics (SpringerBriefs in Complexity, 2017). Trained in theoretical physics, he earned his master’s degree from the University of Rome III (2001) and his PhD from the University of Siena (2005), and he has held research positions including postdoctoral appointments at institutions such as the Australian National University and the University of Oxford. Across his academic roles, he teaches courses in network theory, econophysics, and complex systems and collaborates broadly across physics, mathematics, economics, and data science.

Asu Ozdaglar

Massachusetts Institute of Technology (MIT)

Asu Ozdaglar is the Mathworks Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). She is the department head of EECS and deputy dean of academics of the Schwarzman College of Computing at MIT. Her research expertise includes optimization, machine learning, economics, and networks. Her recent research focuses on designing incentives and algorithms for data-driven online systems with many diverse human-machine participants. She has investigated issues of data ownership and markets, spread of misinformation on social media, economic and financial contagion, and social learning. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the 2014 Spira teaching award, and Keithley, Distinguished School of Engineering and Mathworks professorships. She is an IEEE fellow, IFAC fellow, and was selected as an invited speaker at the International Congress of Mathematicians. She received her Ph.D. degree in electrical engineering and computer science from MIT in 2003.

Joint School Speakers

Kyriaki Kalimeri

UNICEF & ISI Foundation

Kyriaki Kalimeri is a computational social scientist working at the intersection of machine learning, natural language processing, and Humanitarian AI. She is a Senior Research Scientist at UNICEF in New York and at the ISI Foundation in Turin, where she develops data-driven methods to understand human behavior from digital traces and support decision-making for social impact. She received her PhD from the University of Trento and was a visiting PhD researcher at the MIT Media Lab’s Human Dynamics group under Sandy Pentland; she also holds a Diploma in Electrical and Computer Engineering from the Technical University of Crete. Her research spans polarization and misinformation dynamics—particularly in public-health and vaccine contexts—as well as applied humanitarian work, including improved geolocation of humanitarian documents and impact-based forecasting approaches for climate hazards such as tropical cyclones. She is active in training and community-building, including mentoring through ISI Foundation’s Lagrange Scholarship program. Her work has been recognized with multiple awards, including a 2023 Best Paper Award at the ACM Conference on Information Technology for Social Good.

Elisa Omodei

Central European University

Elisa Omodei is an Associate Professor in the Department of Network and Data Science at Central European University (CEU) in Vienna. She trained in physics and applied mathematics, earning a BSc in Physics (University of Padua), an MSc in Applied Physics (University of Bologna), and a PhD in Applied Mathematics for the Social Sciences at École Normale Supérieure (Paris), followed by postdoctoral work at Rovira i Virgili University in Tarragona. Before joining CEU, she spent several years at the United Nations—first as a Research Scientist in UNICEF’s Office of Innovation in New York and then as Lead Data Scientist in the Research, Assessment and Monitoring Division at the UN World Food Programme in Rome—bringing network and data-science methods to humanitarian monitoring and decision support. Her research uses complexity science and computational social science to study societal challenges such as political polarization and misinformation, and to develop practical, data-driven approaches for monitoring vulnerability and progress toward the Sustainable Development Goals, including work on forecasting food insecurity at scale. Professor Omodei’s contributions have been recognized with the Complex Systems Society’s Junior Scientific Award (2024) and Service Award (2023), and she has played leadership roles in the community, including serving as Vice-President Secretary of the Complex Systems Society (2018-2021).

School Speakers

Eric D. Kolaczyk

McGill University

Eric D. Kolaczyk is a Professor in McGill University’s Department of Mathematics and Statistics and the inaugural director of McGill’s Computational and Data Systems Institute (CDSI). He is also an Associate Academic Member at Mila, the Quebec AI Institute. Trained as a statistician, he earned his B.S. in mathematics at the University of Chicago and his M.S. and Ph.D. in statistics at Stanford University. Kolaczyk’s research develops statistical and machine-learning foundations for network data and related data objects that arise in computing-enabled and engineered systems, with collaborations spanning computational biology, computational neuroscience, and (more recently) AI-assisted chemistry and materials science. He has published more than one hundred papers and authored several widely used books in network analysis, including Statistical Analysis of Network Data: Methods and Models and Statistical Analysis of Network Data with R (with Gábor Csárdi). Before joining McGill, he held faculty and leadership roles at Boston University, including Director of the Rafik B. Hariri Institute for Computing, Director of the Program in Statistics, and founding director of the M.S. in Statistical Practice program. He has served as an associate editor for leading journals across statistics, engineering, and applied mathematics, including JASA, JRSS-B, IEEE Transactions on Image Processing, IEEE Transactions on Network Science and Engineering, and SIAM Journal on Mathematics of Data Science. Kolaczyk has also contributed to national and professional service, including co-chairing the U.S. National Academies Roundtable on Data Science Education, and he is an elected Fellow of the AAAS, ASA, and IMS, a Senior Member of IEEE, and an elected member of the International Statistical Institute.

Melanie Weber

Harvard University

Melanie Weber is an Assistant Professor of Applied Mathematics and of Computer Science at Harvard University, where she leads the Geometric Machine Learning Group. Her research develops machine learning and optimization methods that exploit geometric structure in data—especially non-Euclidean settings such as graphs and manifolds—with an emphasis on efficiency and provable guarantees. She received her PhD in Applied Mathematics from Princeton University in 2021 and previously earned a BSc/MSc in Mathematics and Physics from the University of Leipzig. Before joining Harvard, she held fellowships at the University of Oxford (Hooke Research Fellow at the Mathematical Institute and Nicolas Kurti Junior Research Fellow at Brasenose College) and was a Research Fellow at the Simons Institute for the Theory of Computing. Her work has been recognized with honors including the Alfred P. Sloan Research Fellowship in Mathematics (2024) and the IMA Leslie Fox Prize in Numerical Analysis (2023). She is also the author of an AI Magazine survey on geometric machine learning, and her research is supported by a number of organizations including NSF, DARPA, and the Harvard Data Science Initiative.

Kim Albrecht

Folkwang University of the Arts & Harvard University

Kim Albrecht is a data and media artist, information designer, and scholar whose work investigates how data, interfaces, and computational systems shape what becomes visible—and invisible—in contemporary culture. He is Professor of Information Design at Folkwang University of the Arts and a Principal at metaLAB (at) Harvard, where he is also a Faculty Associate at Harvard’s Berkman Klein Center for Internet & Society. Albrecht co-founded metaLAB (at) Berlin and previously held a professorship at the Film University Babelsberg Konrad Wolf. Trained in graphic design and interface design, he earned a PhD in Media Theory from the University of Potsdam, after working as a data visualization researcher at the Center for Complex Network Research at Northeastern University. His projects—including Artificial Worldviews, Data Decay, and the Black Lives Matter Street Mural Map—combine investigative research with experimental aesthetics across exhibitions, performances, and publications. His work has been exhibited internationally and is held in permanent collections at ZKM, the Cooper Hewitt, and the Ars Electronica Center; Artificial Worldviews was featured on the cover of Nature’s “Nature 10” in 2023.