Event Schedule: Data Science Day Jena 2025
🕐 1:00 PM - 1:05 PM
Welcome and Introduction
Speaker: Joachim Giesen, Institute of Computer Science, Friedrich Schiller University Jena
🕐 1:05 PM - 2:00 PM
Keynote: TBA [english]
Speaker: Stefan Funke, Institute for Formal Methods of Computer Science, University of Stuttgart
Abstract:
TBA
🕐 2:00 PM - 2:45 PM
Poster Session & Company Exhibition with Coffee Break ☕🖼️💼
🕐 2:45 PM - 3:10 PM
Presentation: Life Centricity: Mehr Zeit für das Wesentliche durch KI-Agenten [german]
Speaker: Mario Eckhardt
Company: Accenture GmbH
Abstract:
Werden KI-Agenten die neue Armee nerviger Bots oder endlich das Versprechen intelligenter Assistenz erfüllen, die Kunden begeistert, Mitarbeiter entlastet und tatsächlichen Mehrwert erzielt?
„Life Centricity“ legt den Fokus darauf mit KI-Agenten das Leben zu erleichtern und „virtuelle Avatare“ geben KI dafür eine Stimme und Gesicht.
🕐 3:10 PM - 3:35 PM
Presentation: Stellarium Sky Cultures - An example of transdisciplinary collaboration [english]
Speaker: Susanne M. Hoffmann, Michael Stifel Center, Friedrich Schiller University Jena
Abstract:
Stellarium is a free, open-source planetarium software that is available in various independent flavours (desktop, mobile, web...). The applications for the humanities go far beyond the typical applications for amateur and professional astronomers for observation planning: Research results (data) from ethnology, anthropology, philologies/linguistics and the history of science can be contributed here directly by researchers. The presentation will briefly introduce the software and the challenges associated with transdisciplinarity.
🕐 3:35 PM - 4:00 PM
Presentation: Das Thüringer Zentrum für Lernende Systeme und Robotik - Vernetzung und Wissenstransfer für KI-Akteuren in Thüringen [german]
Speaker: Oliver Mothes, Thuringian Center of Learning Systems and Robotics (TZLR)
Abstract:
TBA
🕐 4:00 PM - 4:45 PM
Company Exhibition with Coffee Break ☕💼
🕐 4:45 PM - 5:30 PM
Capstone Presentation: Discrete Optimization Problems in Data Science - Practical Solutions for Hard Problems [english]
Speaker: Christian Komusiewicz, Institute of Computer Science, Friedrich Schiller University Jena
Abstract:
Many tasks in Data Science and Machine Learning can be formulated as discrete optimization problems. Often, these optimization problems turn out to be algorithmically hard. Using example problems from network analysis and decision tree learning, we discuss how to obtain efficient solvers for such problems despite their hardness.
🕐 5:30 PM - 7:30 PM
Get-together 🎊
Enjoy the event!
