Profession and academia

This trimester I'm giving for the first time a course on Introduction to Network Science for the undergraduate studies on Data Science at Universitat Pompeu Fabra.

• Why studying networks
• Basics concepts of graph theory
• Random networks
• Scale-free networks
• Preferential attachment
• Link formation mechanisms
• Homophily and assortativity
• Hubs and authorities
• PageRank
• Link-based centrality
• Network flows
• Dense sub-graphs
• Hierarchical clustering
• Spreading phenomena

All the materials of the course are available on GitHub: https://chatox.github.io/networks-science-course/

IEEE Intelligent System is hosting a special issue on AI for Disaster Management and Resilience, which we are co-editing with Yu-Ru Lin from University of Pittsburgh, USA and Jie Yin from University of Sydney, Australia.

• Submission: November 15, 2018
• Notification of acceptance: April 30, 2019

In recent years, there have been an increasing number of large-scale crises, such as natural disasters or armed attacks, that have had major effect on individual lives and infrastructure, and have caused the devastation to communities. During these mass emergencies, victims, responders, and volunteers increasingly use social media and mobile devices to provide real-time situation updates, i.e., reports on damage, or request and offer help. This has generated vast volumes of crisis data in different forms and from different sources. There are a number of challenges associated with near-real-time processing of vast volumes of information in a way that makes sense for people directly affected, for volunteer organizations, and for official emergency response agencies. There is a growing need for developing new AI techniques that process large-scale crisis data to gain a “big picture” of an emergency, detect and predict how a disaster could develop, analyze the impact of disasters and the effect of negative externalities in a cyber-physical society, and assist in disaster response and resource allocation. These AI techniques can allow better preparation for emergency situations, help save lives, limit economic impact, provide effective disaster relief, and make communities stronger and more resilient.

This special issue is to call for research initiatives toward the next generation disaster management that leverage AI to strengthen disaster resilience at all levels of society in the new age of mass emergencies.

Read more information on the call for papers »

• Aris Anagnostopoulos, Carlos Castillo, Adriano Fazzone, Stefano Leonardi, and Evimaria Terzi: Algorithms for Hiring and Outsourcing in the Online Labor Market. To appear in KDD, London, UK, August 2018. ACM Press. [video teaser|data and code]

The Web Science and Social Computing Group at Universitat Pompeu Fabra in Barcelona, which I lead, is inviting applications for PhD students interested in crisis informatics and algorithmic discrimination, and in general on social computing applications that address issues of social significance.

The crisis informatics student will seek to deepen our understanding of how community-generated content in social platforms can be used to improve emergency/disaster response, and to improve the resilience of societies. A student willing to pursuit this topic must have an orientation to practical problems, excellent programming skills, and be motivated to create new applications to use time-sensitive, sometimes life-saving data.

The algorithmic discrimination student will seek to deepen our understanding of how algorithms can embody and sometimes exacerbate biases against less advantaged groups in society. A student willing to pursuit this topic must have an excellent background on statistics, data mining, or machine learning, good data management skills, and be motivated to work in applications for education, justice, medicine, and other areas in which algorithms are used in the public sector.

Both positions are funded through DTIC fellowships.

For more information, visit Web Science and Social Computing » PhD Opportunities.

Extreme weather events put the most vulnerable communities at high risk.

How can data analytics strengthen early warning systems and and support relief efforts for communities in need?

The size and frequency of natural disasters is growing at an alarming pace. In 2016 earthquakes, wildfires and other natural events caused US$210bn in global economic losses, according to a UK-based insurance broker, Aon. The year 2017 may tally an even higher figure, as a series of floods, earthquakes and hurricanes struck various areas of the world.

Developing economies, especially those located closer to the equator, are expected to bear the greatest toll from extreme weather events. These countries are the most vulnerable and least equipped to withstand these types of events, as they have fewer resources to prevent damage and protect citizens who are at risk.

Data and analytics can support relief and response initiatives for communities in need. From the use of satellite images and crowd-sourced mapping tools to predict and help prepare for disasters, to on-the-ground reports from drone footage, emergency responders, governments and non-government organisations (NGOs) are adopting data analytics as a critical tool to strengthen early warning systems and aid relief efforts in the aftermath of a disastrous event.

Full article »