Alessandro Vespignani is an Italian American physicist whose research sits at the intersection of computational modeling, data science, and complex systems, with a focus on contagion phenomena, collective behavior, and epidemic preparedness. He trained in theoretical physics in Rome, held postdoctoral posts at Yale and Leiden, and later worked at the International Center for Theoretical Physics and Indiana University before joining Northeastern University, where he is the Sternberg Family Distinguished University Professor of Physics, Computer Sciences, and Health Sciences. He directs Northeastern’s Network Science Institute and MOBS Laboratory, serves as President of the ISI Foundation in Turin, Italy, and helps lead EpiStorm, a CDC initiative that models and forecasts epidemics. In this wide-ranging InterDialogue, Vespignani traces network science’s shift from foundational theory toward urgent, data-driven application, and discusses the community, funding, and technological forces behind that growth. He makes the case for science as a collective, interdisciplinary effort—increasingly recognized by policymakers and the public—and walks through his own path from theoretical physics in Rome, through Yale, Leiden, and the ICTP, to the landmark papers and books that helped define the fields of complex networks and computational epidemiology. The conversation turns to Vespignani’s public health work over the last two decades—including CDC forecasting through FluSight and EpiStorm, and his team’s recent role in the Ebola response in the Democratic Republic of the Congo—and to what differentiates outbreaks like COVID-19, influenza, and Zika. We close on the erosion of public trust in science, the broad work being done at institutions he leads at Northeastern and at ISI Foundation, and how artificial intelligence is reshaping the scientific enterprise. Timestamps 0:00 – Introduction 2:20 – The growing urgency of network science 5:58 – From toy models to real-world data 8:56 – Data, community, and funding as growth drivers 13:19 – Science as a collective,...

Over the past two decades, complexity science has matured from its origins as a small, largely esoteric subfield of theoretical statistical physics and mathematics into an expansive area of inquiry that bridges disciplines and addresses some of the world’s most acute interconnected challenges.  Nowhere has this interdisciplinary, applied process become more apparent than with the ongoing convergence of complexity and health.  This convergence is happening at all scales—individual and collective, private and public—across approaches to prevention, treatment, management, and containment in integrative and public health spaces, as well as through applied research and innovation. “Our methodological approach goes on and on down to the level of microbiology and finally finding cures for many diseases,” says Italian American physicist and complex networks scientist Alessandro Vespignani. “All that goes through networks and through a vision that has complex systems at the core. Network science is a way to approach complex systems,” Vespignani explained from within Northeastern University’s Network Science Institute, where he serves as director, leads the Laboratory for the Modeling of Biological and Socio-technical Systems (MOBS) lab, and heads several impactful public health initiatives. “We look at elements and their interactions, and we do not get rid of nonlinear phenomena, feedback loops, et cetera, that exist in real world systems; we need to consider those,” Vespignani continues. “And in that sense, there is even more urgency in the work that we as a community do to respond to those challenges and to be of service to the society.” Explore the full InterDialogue with Alessandro Vespignani. The COVID-19 pandemic served as a critical moment of applied research and broad engagement with public health policy for those, like Vespignani, who work at the interface of complexity and health. But theoretical research and its translation into experiments and action began several years prior and...