SPEAKERS (2024)
(Infineon)
Ann-Christin Bette is a developer of embedded software and firmware for sensor systems with a focus on AI applications. Since July 23, she is staff engineer and lead of the firmware team for advanced sensor control systems. Previously, she worked as a research scientist for advanced vision methodologies for integrated circuit analysis in the security department of Infineon. She is still in the process of finishing her PhD in collaboration with TU Munich. In 2020, she completed her studies in Computer Science with a focus on Artificial Intelligence at the TU Munich. Prior to that, she obtained the first state examination in mathematics and physics at LMU Munich in 2017.
(University of Tübingen)
Katharina Eggensperger is an Early Career Research Group Leader for “AutoML for Science” in the Cluster of Excellence “Machine Learning for Science” at the University of Tübingen, Germany. Before joining the University of Tübingen, she completed her PhD at the University of Freiburg, Germany. Her research focuses on AutoML methods, including hyperparameter optimization, AutoML systems and efficient benchmarking, to leverage the full potential of ML for new challenges. She was part of the team winning three AutoML competitions (2016, 2018, 2020), co-developed several well-established open-source AutoML tools, co-organized the AutoML workshop series at ICML in 2019-2021, served as a social chair for the AutoML conference in 2022/23 and is now a program chair for AutoML’24.
(bulwiengesa AG)
Dr. Federica Fusco is data scientist at bulwiengesa AG, one of the largest independent real estate consulting companies in Germany. Federica studied Physics at the University of Rome “Tor Vergata” (Italy) and then in 2016 obtained a joint PhD in Astrophysics between the University of Rome “La Sapienza” (Italy) and the University of La Laguna (Spain). Since 2017 Federica is working as a data scientist, first in an IT Company, where she had the opportunity to work on very different projects in many different business areas. In 2022 she then moved to bulwiengesa AG, where she is working mainly, but not only, with geographical data. Federica loves projects where she can follow the whole data science lifecycle: For her a project starts with the data collection and use case definition and ends with the model deployment in an application.
(Deepset AI)
Miriam Kuemmel holds a Bachelor’s degree in German linguistics and literature, alongside theater and media studies from FAU Erlangen. She furthered her academic pursuits with a Master’s degree in Speech and Language Processing, spanning the University of Konstanz and the University of Massachusetts.
Miriam entered the professional arena as a Data Scientist and NLP engineer in 2019, eventually assuming leadership of the Solution Engineering Team at deepset. Her team specializes in diverse areas such as Natural Language Processing, linguistics, statistics, Machine Learning, Computer Science, Data Science, Data Engineering, and, last but not least, effective, productive, and empathetic project management and customer success management.
(TUM MIRMI)
Kim Kristin Peper is a Research Associate at the Munich Institute of Robotics and Machine Intelligence at TUM. Her expertise reflects a commitment to advancing technology in the fields of robotics and biomechanics for the benefit of healthcare and rehabilitation. In her ongoing project, she is dedicated to enabling the monitoring of musculoskeletal disorders in early rehabilitation. Her diverse professional journey includes exploring robotics applications in „Ambient Assisted Living“ and delving into „Movement Biomechanics“.
(DLR)
Lina Putze received the B.Sc. and M.Sc. degrees in mathematics from the University of Münster in 2016 and 2019, specializing on the topics of stochastic processes, probability theory and its applications. She currently works at the German Aerospace Center (DLR) e.V. Institute of Systems Engineering for Future Mobility. Her research focuses on methods to ensure safety of highly automated transport systems, including the identification and analysis of hazards and risk triggering scenario properties, causal modelling and risk assessment.
(Google DeepMind)
Laura Weidinger is a Senior Research Scientist at Google DeepMind, where she leads research on evaluating the ethics and societal implications of generative AI systems. Laura’s work bridges philosophy and technical AI research. Laura previously worked as a cognitive science researcher, and as policy advisor at UK and EU levels. She holds degrees from the University of Cambridge and Humboldt Universität Berlin.