Month: October 2017

Open research position at ISTC in collaboration with the LABSS

A post-doctoral position is now opening at the ISTC-CNR, in collaboration with the Laboratory of Agent Based Social Simulation (LABSS). The position is promoted in memory of Rosaria Conte, Research Director and founder of the LABSS, to foster research following the directions that Rosaria contributed to develop, especially in the field of social and cognitive dynamics. The fellowship is intended as a way to continue developing Rosaria Conte’s work and ideas, reflecting her many inter-disciplinary interests alongside her commitment and enthusiasm for mentoring younger researchers.


Interdisciplinary Training in Complex Networks and Systems

Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how dynamic brain connections give rise to thoughts and behaviors, to detecting and preventing the spread of misinformation or unhealthy behaviors across a population. Graduate training, however, typically occurs in one of two dimensions: experimental and observational methods in a specific area such as biology and sociology, or in general methodologies such as machine learning and data science.

With more and more students seeking to gain sufficient expertise in mathematical and computational methods on top of domain-specific laboratory and social analysis methodologies, a greater demand for more efficient training is emerging. This National Science Foundation Research Traineeship (NRT) award to Indiana University will address this growing need with an integrated dual PhD program that trains students to be “bidisciplinary” in Complex Networks and Systems (CNS) and another discipline of their choosing from the natural and social sciences. It will seamlessly integrate traditional education with interdisciplinary hands-on research in a culture of academic and human diversity.


Applications Due December 1


Complex Networks: Theory, Methods, and Applications | Lake Como School of Advanced Studies

Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline—including sociology, transportation, economics and finance, biology, and myriad others—and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.


Spring School
(4th edition)
Lake Como School of Advanced Studies
Villa del Grumello, Como, Italy, 14-18 May 2018

*** DEADLINE FOR APPLICATION: February 18, 2018 ***


What intelligent machines can learn from a school of fish

Science fiction visions of the future show us AI built to replicate our way of thinking — but what if we modeled it instead on the other kinds of intelligence found in nature? Robotics engineer Radhika Nagpal studies the collective intelligence displayed by insects and fish schools, seeking to understand their rules of engagement. In a visionary talk, she presents her work creating artificial collective power and previews a future where swarms of robots work together to build flood barriers, pollinate crops, monitor coral reefs and form constellations of satellites.


Estimating savings in parking demand whem using shared vehicles for home-work commuting

The increasing availability and adoption of shared vehicles as an alternative to personally-owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95\% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home-work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of increased traveled kilometers. We consider the city of Singapore as a case study, and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of increasing total traveled kilometers of less than 2%.


Estimating savings in parking demand whem using shared vehicles for home-work commuting
Dániel Kondor, Hongmou Zhang, Remi Tachet, Paolo Santi, Carlo Ratti