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Data Challenge: Mobile Phone Data

Mobile phone data are ubiquitous today, as almost everyone carries a mobile device in their pocket. This generates huge data trails that provide almost unbiased information about people’s mobility and location. These data have been used, for example, in the COVID-19 years to assess the impact of non-pharmaceutical interventions on mobility patterns and firm location decisions as well as for traffic planning or academic research.

Under the umbrella of the BERD Academy, the workshop aims to bring together data scientists and researchers working on mobile phone data as well as related flow, geo-spatial and mobility data to address methodological as well as applied research questions from sociology, economics, or related fields. This includes the pre-processing of massive data, the restructuring of data into mobility data, or the detection of local clusters, to name just a few possible applications. The workshop provides an open forum for current research and new ideas on data analysis.

The workshop will be conducted in English.

:spiral_calendar_pad: Nov 12, 2024
:round_pushpin: LMU Munich

Submission/Participation

Due to limited seat capacity and in order to keep the event interactive, applications for presentations will be given preference over requests for attendance only. We strongly encourage submissions from early-career researchers, including PhD students and practitioners. Contributions that are methodological or empirical in nature are welcome provided they use some kind of mobile phone data. While extended abstracts or early-stage drafts will be taken into consideration, complete papers will be given preference.

Timeline

Application/Submission deadline: September 30th

Confirmation/rejection: October 8th (at the latest)

Workshop: Tuesday, November 12, from midday to evening (more info to follow)

Scientific Committee

Göran Kauermann has been a full professor of Statistics at LMU Munich since 2011 and heads the Chair of Statistics for Economics, Business, and Social Sciences there. Additionally, he is the chairman of the German Data Science Society (GDS). His research interests focus on semi- and nonparametric analysis, generalized linear and mixed models, and network data analysis.

Victor Tuekam is a doctoral student in Statistics at LMU Munich and the ifo Institute. His research interests are in generalized linear and mixed models, network data analysis, and urban economics.

Sebastian Wichert is the head of the LMU-ifo Economics & Business Data Center, the joint research data center of the ifo Institute and the LMU Munich. His research interests are in public economics, regional economics and household finance using and combining traditional statistical data with real-world data.

Contact:
berd-academy@stat.uni-muenchen.de

Data Science for Social Good

Registration ended (Feb 15, 2023)

This 2-month full-time program joins forces of aspiring talents in the area of Data Science in small groups to work on projects with a positive societal impact.

The program is designed for two teams of 4-5 fellows, each working on a separate project for the social good. Both teams will be assisted by a Technical Mentor and a Project Manager. The participants receive a fellowship that covers living expenses for the time of the program from August 1st – September 30th.

The program is aimed at students, recent graduates or PhD students from diverse scientific, as well as geographical backgrounds. We therefore encourage applications from the fields of data science, computer science, statistics, but also in general the social and natural sciences.

If you are interested in joining the program or have students, friends or colleagues in mind that could be interested, check out the website and apply by February 15th: https://sites.google.com/view/dssgx-munich-2023/startseite

Please also share this call in your network, in classes you teach or approach people directly.

In case of any concerns please feel free to reach out via dssg2023@stat.uni-muenchen.de

About the Host and the organizing Institution

This Event is hosted by the Chair of Frauke Kreuter, who is a professor of Statistics and Data Science in the Social and Behavioral Sciences at LMU Munich. In her research, she focuses on statistical methods related to labor market and occupational research, as well as data science. In addition to her academic work, she is the founder or co-founder of several programs that address evolving data environments and data-driven research. The Munich Center for Machine Learning (MCML) is one of six national AI Competence Centers and brings together the leading ML researchers from LMU, TUM and associated institutions.