Keynotes

Abstract: Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia is not a source of original information, but was conceived as a gateway to secondary sources: according to Wikipedia’s guidelines, to ensure knowledge integrity facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the very heart of Wikipedia, little is known about how users interact with them, how contributors use them, and the overall citation coverage. In this talk, we will see how machine learning can support knowledge integrity and help understand and monitor information quality in Wikimedia spaces.

Short Bio: Miriam Redi is a Senior Research Scientist at the Wikimedia Foundation and Visiting Research Fellow at King’s College London. Formerly, she worked as a Research Scientist at Yahoo Labs in Barcelona and Nokia Bell Labs in Cambridge. She received her PhD from EURECOM, Sophia Antipolis. She conducts research in social multimedia computing, working on fair, interpretable, multimodal machine learning solutions to improve knowledge equity.

Abstract: The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful.

In this talk, I will present the CoronaSurveys project (https://coronasurveys.org/). CoronaSurveys uses a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). The CoronaSurveys project has been collecting reports for the COVID-19 pandemic since March 2020. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool to track the COVID-19 pandemic. This makes it especially interesting and useful for LMICs.

Short Bio: Dr. Antonio Fernández Anta is a Research Professor at IMDEA Networks. Previously he was a Full Professor at the Universidad Rey Juan Carlos (URJC) and was on the Faculty of the Universidad Politécnica de Madrid (UPM), where he received an award for his research productivity. He was a postdoc at MIT from 1995 to 1997, and spent sabbatical years at Bell Labs Murray Hill and MIT Media Lab. He has been awarded the Premio Nacional de Informática "Aritmel" in 2019 and is a Mercator Fellow of the SFB MAKI in Germany since 2018. He has more than 25 years of research experience, and more than 200 scientific publications. He was the Chair of the Steering Committee of DISC and has served in the TPC of numerous conferences and workshops. He received his M.Sc. and Ph.D. from the University of SW Louisiana in 1992 and 1994, respectively. He completed his undergraduate studies at the UPM, having received awards at the university and national level for his academic performance. He is a Senior Member of ACM and IEEE.