Past seminars

Realizing strongly correlated topological phases of ultracold gases is a central goal for quantum gas experiments. Due to the difficulty in preparing these phases, ongoing experiments are focusing on ensembles of few atoms, and the preparation of a fractional quantum Hall state of two bosonic atoms has been achieved. Beyond their preparation, the characterization of these few-body states poses a unique challenge due to their small system size. I will discuss which signatures can be used, and show that hallmark fingerprints of fractional quantum Hall phases, such as a quantized Hall conductivity, or chiral edge modes, can be extracted in few-particle systems through local density measurements.

How species interact determines whether communities thrive or collapse. Yet we lack a principled way to connect measurable interaction properties to diversity and stability. The Lotka-Volterra model, a cornerstone of theoretical ecology, links community-level outcomes to statistical properties of interaction distributions but typically assumes these interactions are Gaussian-distributed. Using consumer-resource models that mechanistically capture microbial metabolism (competition for shared nutrients and cross-feeding of metabolic byproducts), I will show that species interactions automatically organize into a “many weak, few strong” pattern that deviates strongly from Gaussian assumptions. Remarkably, this same skewed pattern appears across empirical communities spanning microbes, soil food webs, and planktonic networks. I will present an analytical explanation rooted in the combinatorial structure of metabolic interactions, and demonstrate that accounting for this pattern improves predictions of both community stability and diversity, in the model and against experimental data. More broadly, these results highlight how metabolic constraints shape the structure of ecological communities and point toward a tighter integration of metabolism and predictive ecology.

When exposed to a contagion phenomenon, individuals may adapt their behavior in response to the perceived risk of infection, reducing both their exposure and their probability of infecting others. While adaptive behaviors have been studied in pairwise networks, many real-world contagion processes and adaptive mechanisms are driven by group (higher-order) interactions, potentially giving rise to new phenomena.

We consider adaptive behaviors triggered by risk awareness perceived through group and pairwise interactions, and we study their impact on contagion processes using numerical simulations and a mean-field analytical approach. We show that adaptivity can profoundly reshape the nature of contagion dynamics, and that its effectiveness in mitigating contagion depends on the type of information triggering adaptive behaviors. Adaptation can suppress bistability and defuse explosive discontinuous transitions typical of higher-order contagion, effectively transforming the process into a pairwise one and making the phase transition continuous. Moreover, adaptive behaviors based on group information outperform pairwise strategies in mitigating contagion while requiring a lower social cost. By exploiting population heterogeneity, they prevent the spread from reaching highly connected nodes and large groups, which would otherwise sustain the dynamics.

These results highlight the crucial role of group interactions and adaptive mechanisms in shaping both the nature and control of contagion processes, paving the way for the design of new intervention measures.

Dense crowds form some of the most dangerous environments in modern society. Dangers arise from uncontrolled collective motions, leading to compression against walls, suffocation and fatalities. Our current understanding of crowd dynamics primarily relies on heuristic collision models, which effectively capture the behaviour observed in small groups of people. However, the emergent dynamics of dense crowds, composed of thousands of individuals, remains a formidable many-body problem lacking quantitative experimental characterization and explanations rooted in first principles. Here we analyse the dynamics of thousands of densely packed individuals at the San Fermín festival (Spain) and infer a physical theory of dense crowds in confinement. Our measurements reveal that dense crowds can self-organize into macroscopic chiral oscillators, coordinating the orbital motion of hundreds of individuals without external guidance. Guided by these measurements and symmetry principles, we construct a mechanical model of dense-crowd motion. Our model demonstrates that emergent odd frictional forces drive a non-reciprocal phase transition towards collective chiral oscillations, capturing all our experimental observations. To test the robustness of our findings, we show that similar chiral dynamics emerged at the onset of the 2010 Love Parade disaster and propose a protocol that could help anticipate these previously unpredictable dynamics.