Speakers

Keynotes

Keynote talk: Controlling SARS-CoV-2 transmission in schools

School closures and distance learning have been extensively applied to control SARS-CoV-2 transmission in several countries. Through mathematical modeling and empirical data, I will present results on school transmission and review protocols to keep schools open in different phases of the pandemic.

Vittoria Colizza
INSERM​ & Sorbonne Université, Faculté de Mèdecine
Paris, France
Tokyo Institute of Technology, WRHI
Tokyo, Japan

Esteban Moro
Universidad Carlos III (UC3M)
Madrid, Spain
MIT Media Lab & MIT Connection Science (IDSS)
USA

Keynote talk: Understanding urban resilience through behavioral mobility data

The economic and social progress of our urban areas, our institutions, and our jobs depend on the diversity and resilience of the social fabric in cities. Despite their importance, several major forces erode the diversity and strength of those social connections: from income or racial segregation to differences in education and job access. In this talk, I will present our recent work to understand the fragility of the network of social connections and interactions in cities through the analysis of behavioral mobility data and its relationship with networked inequalities in experienced segregation, access to healthy food, or adaptation to the recent pandemic.

Keynote talk: The social dimensions of human-centered AI

The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity, and understanding. On the other side, this also reveals a social dimension of AI, as increasingly complex socio-technical systems emerge, made by interacting people and intelligent agents. The lecture will address the individual and social dimensions of such collaboration with a focus on: 1) “Explainable AI”, providing a reasoned introduction to the work and the research challenges to the work of Explainable AI for Decision Making (XAI); 2) the (undesired) emerging network effects of social AI systems and the design of transparent mechanisms for decentralized collaboration and personal data ecosystems that help achieve desired aggregate outcomes, i.e., the realization of the agreed set of values and objectives at collective levels, such as accessible urban mobility, diversity, pluralism, fair distribution of economic resources, environmental sustainability, a fair and inclusive job market.

Fosca Giannotti
Scuola Normale Superiore
Pisa, Italy

Noshir Contractor
Northwestern University
USA

Keynote talk: People Analytics: Using Digital Exhaust from the Web to Leverage Network Insights in the Algorithmically Infused Workplace

In order to bring the performance of people analytics in the algorithmically infused workplace up — and in line with the hype — organizations need to do more than analyze data on demographic attributes. We need to focus not only on who people are but also who they know. The potential for social network analysis to identify “high potentials,” who has good ideas, who is influential, what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been the collection of network data via surveys that are time consuming, elicit low response rates and have a high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long duration space exploration to demonstrate how we can leverage people analytics – and in particular relational analytics - to mine “digital exhaust”— data created by individuals every day in their digital transactions, such as e‐mails, chats, “likes,” “follows,” @mentions, and file collaboration— to address challenges they face with issues such as team conflict, team assembly, diversity and inclusion, succession planning, and post-merger integration.

Invited Speakers

Invited talk: Interacting social and disease dynamics in multiplex networks

A major issue in the theoretical modeling of epidemic spreading is to investigate the effects of human social behavior in the propagation of a disease. In this work we explore the spreading of the awareness of a disease as a method to control a pandemic. We assume that aware individuals can take measures (isolation, quarantine) to reduce the risk of contagion. I will present results that show how the prevalence of the disease is affected by the rate of information dissemination, and by the relative speed between disease and information spreading. I will also show that the dynamics of individuals’ opinions could have striking consequences on the statistical properties of disease spreading. These results are obtained within a multiplex network framework in which the interaction between disease and social processes takes place in a bi-layer network.

Federico Vázquez
Instituto de Cálculo, FCEyN
Universidad de Buenos Aires and CONICET
Buenos Aires, Argentina

Gabriel Montes Rojas
IIEP - BAIRES
Universidad de Buenos Aires and CONICET
Buenos Aires, Argentina

Invited talk: Network econometric models for the Argentine interbank markets

The effect of network centrality on interest rate spreads in the Argentine interbank markets will be discussed, both in the unsecured (Call) and in the guaranteed (Repo) markets. Markets differ in terms of collateral and microstructure. Measures of local and global centrality are used as explanatory variables in a regression on panel data with pairwise fixed effects. The local centrality measures are significant only in the Repo market, the global ones in both markets, although with different effects. The impact of centrality measures on liquidity reveals their importance for monitoring systemic risk.

Invited talk: Disease transmissibility and population size: Evidence after COVID19

A large amount of data available on the same disease in different locations, due to the COVID-19 pandemic, contributes to the study of parameters such as the basic reproduction number, R0. This work proposes to analyze the R0 for different functional urban areas (FUA), seeking a better understanding of the behavior of the dynamics of infectious diseases. For this, data were obtained from time series of accumulated cases, population estimates, and tables with codes for FUA or equivalent units in different countries. Different ways of calculating R0 were implemented, based on the SIR and SEIR models, and on statistical methods commonly used to obtain the effective reproduction number, Rt, during epidemics. The results confirm the existence of a logarithmic scaling law between the basic reproduction number and the population size for urban units, regardless of the method used for analysis. This result contrasts with the basic assumptions of epidemic models where the number of contacts is independent of the population size. We propose a simple but meaningfull mean-field network theory that explains the data at the beginning of COVID-19 pandemic in several countries. Extensions and limitations of the scaling law and theory are discussed.

Sebastián Gonçalves
Instituto de Física - UFRGS
Porto Alegre, RS, Brazil