The talks will be about how to find Willy (Valdo) using neural networks and how data science can contribute to reach UN SDGs goals

Nov 21, 2022 6:30 PM — Jun 2, 2022 8:30 PM
Quellenstrasse 51 - 1100 Vienna
Quellenstrasse 51, Vienna, Vienna 1100

Direct link to join the event: https://www.meetup.com/vienna-data-science-group-meetup/events/288864744/

Finding Waldo with permutational equivariance

Thomas Niedermayer, Data Science Student

Thomas will introduce the computer vision problem of the most recent Lumos datathon, showcase the winning submission, and explain the concept of permutational equivariance to improve the learning of neural networks for input data in the form of sets.

Leo and Thomas are TU Data Science students with a CS and math background, respectively. They are part of a student organization specializing in Data Science called Lumos

Data science for the Sustainable Development Goals: Opportunities & Challenges

Elisa Omodei, Assistant Professor, Department of Network and Data Science, CEU - LinkedIn

In a rapidly changing world, facing an increasing number of socioeconomic, health and environmental crises, data science can help us quantify vulnerabilities and monitor progress toward achieving the UN Sustainable Development Goals. In this talk, I will provide a non-exhaustive overview of the main areas of applications where data-driven computational methods have shown their potential for social impact, and I will then deep-dive more specifically into my work on predicting food insecurity from conflict, weather, and economic data.

Elisa Omodei am an Assistant Professor at the Department of Network and Data Science of the Central European University, in Vienna, Austria. Previously, she worked at the United Nations, first at UNICEF’s Office of Innovation in New York and then at the UN World Food Programme in Rome.

community building

We are an association promoting knowledge about data science as a nonprofit. We connect data scientists in Europe and all around the world. Our members are passionate data scientists from various areas of research and industry.