Dr. Philipp Wicke

Dr. Philipp Wicke is a tenured Researcher and Lecturer (Akademischer Rat) at the Center for Information and Language Processing (CIS) at LMU Munich, where he works under the supervision of Prof. Hinrich Schütze. In 2025, he will commence his role as a Junior Researcher in Residence at the Center for Advanced Studies (CAS). Dr. Wicke earned his PhD in Artificial Intelligence in 2021, following a Bachelor of Science in Cognitive Science from the University of Osnabrück in 2017. His research integrates computational linguistics, artificial intelligence, and cognitive science, with a focus on embodied cognition, spatial reasoning, and the adoption of large language models (LLMs) in human-centered applications. His work has been presented at leading conferences such as ACL, EMNLP, and HRI, with recent publications exploring spatial schema intuitions in large language and vision models. Dr. Wicke actively contributes to the scientific community through extensive service roles, including reviewing for the German Research Foundation (DFG) and international conferences, and organizing workshops on topics like computational linguistics and cognitive modeling.

Beyond academia, he has engaged in interdisciplinary dialogues between industry and research, notably as an invited speaker at events co-hosted by appliedAI and SAP, focusing on the integration of AI technologies in industrial contexts. Dr. Wicke is also an Associate Member of the Munich Center for Linguistics (MCL) and an Associated Member of the Munich Center for Machine Learning (MCML).

Project at CAS

As Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), continue to advance, the need to move beyond purely textual interactions becomes increasingly critical. Human communication is inherently multimodal, relying not only on words but also on gestures, facial expressions, and body language to convey meaning, intention, and emotion. Recognizing this complexity, the research residency at the Centre for Advanced Studies (CAS) will focus on exploring and integrating non-verbal communication cues into AI systems to create more intuitive and natural Human-AI interactions.