Indaco Biazzo

Lausanne, Switzerland

I work as postdoctoral researcher at the EPFL in Lausanne, Switzerland. I have a Ph.D. in statistical physics of the Politecnico di Torino. I graduated in physics at the “La Sapienza” university of Rome. My current areas of research are generative neural network, statistical physics, epidemic inference problems and urban science.

I’m the creator and webmaster of the CityChrone project.

Presentations

Dec 2, 2021	CityChrone - an interactive platform for transport network analysis and planning in urban systems -- at the complex networks conference [2021 Madrid]
Oct 16, 2021	sustainability and urban mobility at the red bull basement workshop [2021 (Turin)
Jul 1, 2019	Bayesian framework for inference in epidemic processes on livestock movement networks at CCS/Italy 2019
May 2, 2019	Presentation of CityChrone Project at the Interactive Storytelling course of Torino University
Feb 28, 2019	Presentation of "Universal scores for accessibility and inequalities in urban areas" at the fourth SmartData Workshop

selected publications

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Biazzo, Indaco

Arxiv e-prints 2023

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A Bayesian generative neural network framework for epidemic inference problems

Biazzo, Indaco, Braunstein, Alfredo, Dall’Asta, Luca, and Mazza, Fabio

Scientific Reports 2022

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The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.
General scores for accessibility and inequality measures in urban areas

Biazzo, Indaco, Monechi, Bernardo, and Loreto, Vittorio

Royal Society open science 2019

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Epidemic mitigation by statistical inference from contact tracing data

Baker, Antoine, Biazzo, Indaco, Braunstein, Alfredo, Catania, Giovanni, Dall’Asta, Luca, Ingrosso, Alessandro, Krzakala, Florent, Mazza, Fabio, Mézard, Marc, Muntoni, Anna Paola, and others,

Proceedings of the National Academy of Sciences 2021

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CityChrone: an Interactive Platform for Transport Network Analysis and Planning in Urban Systems

Biazzo, Indaco

In Complex Networks & Their Applications X 2022

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Urban systems studies in the last decades have greatly benefited from the digital revolution and the accumulation of a massive amount of data. Extracting useful information from these data calls for new and innovative theoretical and computational approaches. This work presents an open-source, modular, and scalable platform for urban planning and transports network analysis, the CityChrone [citychrone.org]. The platform shows, on interactive maps, measures of performances of public transport in cities. The measures are based on the computation of the travel time distance between a large set of points. Thanks to the high efficiency of the routing algorithm developed, the platform allows users to create new public transports networks and showing the effect on mobility in a small amount of time. A preliminary analysis of the user-generated scenarios is presented. All the source code of the CityChrone platform is open-source, and we employ only open data to ensure the reproducibility of results.