报告题目：TTZ – Understand Data. Trust AI. Shape the Future

报告人：Prof. Sebastian Dorn, Technical University of Applied Sciences Augsburg, Germany

报告时间：2026年5月7日上午9:30-11:00

报告地点：信息学馆307

邀请人：电气工程系罗艳红教授

报告内容概要：

The second presentation provides an overview of data science research at the Technology Transfer Center (TTZ) of Augsburg University of Applied Sciences. Following a brief introduction to the main research areas, two selected case studies offer concrete insights into current challenges, methods, and application domains. The aim is to highlight both the scientific focus and the practical transfer of data science solutions into real-world contexts.

报告人简介：

Prof. Sebastian Dorn received his Ph.D. degree in Physics from LMU Munich, Germany, in 2016, with his doctoral research conducted at the Max Planck Institute for Astrophysics in Garching, Germany. He also received his M.Sc. degree in Astro-, Particle-, and Nuclear Physics and his B.Sc. degree in Physics from the Technical University of Munich, Germany. From 2016 to 2018, he worked as a Development Engineer at Intel Corporation in Neubiberg, Germany, where he focused on machine learning for model-hardware correlation of wafer data. From 2018 to 2019, he served as Machine Learning Tech Lead at Audi Electronics Venture, conducting computer vision and deep neural network research for image- and LiDAR-based perception in highly automated driving. From 2020 to 2024, he worked at Audi AG in Ingolstadt as Machine Learning/AI Tech Lead, coordinating company-wide AI-based R&D projects. In March 2024, he joined the Technical University of Applied Sciences Augsburg, Germany, where he is currently Professor for Data Science. Since August 2024, he has also served as Scientific Director Data Science at the Technology Transfer Center for Data Science and Autonomous Systems in Landsberg am Lech, Germany. His research interests include deep learning-based computer vision, Bayesian inference theory and applications, machine learning theory and applications, optimization problems, time series analysis, uncertainty quantification, and reliability. His selected publications cover topics such as trajectory prediction with diffusion models, graph-based vehicle trajectory interpretation, 3D object detection, autonomous driving datasets, and Bayesian signal inference.