How can we model and solve challenges for future mobility applications?
Future mobility applications (FMAs) focus on the society and environment. As a first example, when transporting patients, we might want to minimize their home waiting times and their hospital arrival times. As a second example, before planning a cruise, we might want to avoid migration routes for saving marine life. As a third example, when requesting taxi rides, we might want to minimize the risk of harassment. As a fourth example, when visiting friends abroad, we might want to select the most convenient door-to-door option. In this course, BUA students will work on and analyze such case studies from a scientific perspective, as a research project with various programming, data, and analysis tasks. During the course, they will thus get to assess existing solutions and design new solutions for FMAs.
Course prerequisites are skills in mathematics, programming (e.g. Python, C, Java, C++), statistics (e.g. mean, standard deviation), or ethics. Course activities include individual supervision, team tasks, invited talks, and conference participation.
Institutional affiliation: Fachbereich Mathematik und Informatik
(XR006 a (BUA-Veranstaltung))
|Dozent/in||Dr. Martin Aleksandrov|
|Institution||Dahlem Center for Machine Learning and Robotics|
|Raum||Takustr. 9 SR055|
jeweils 10-12 Uhr
Links auf Kursbeschreibung
For information on how to participate in an X-Student Research Group please visit the website of the Berlin University Alliance.
X-Student Research Groups can be credited in the extra curricular domains: Freier Wahlbereich/ABV/ÜwP, individual crediting in other study programs is possible.
Module description: https://www.berlin-university-alliance.de/commitments/teaching-learning/sturop/research-groups/information/Modul_X-Student-Research-Groups.pdf