Prof. Michael Färber is a distinguished scholar at the Technische Universität Dresden, where he holds the Chair for Scalable Software Architectures for Data Analytics since April 2024. His research focuses on natural language processing, machine learning, and knowledge representation, aiming to develop efficient and scalable software architectures for data analytics. Prof. Färber has authored over 75 publications in high-ranking conferences and journals, contributing significantly to advancements in artificial intelligence and data science.

Ercong Nie is a Ph.D. candidate at the Center for Information and Language Processing (CIS) at LMU Munich and a research intern at Huawei Technologies. His research focuses on computational linguistics and natural language processing, with a particular emphasis on multilingual language models and cross-lingual transfer learning. Ercong has contributed to several notable publications, including “Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages” presented at ACL 2023, and “Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration” featured in the Findings of EMNLP 2023. His work aims to enhance the performance of language models across diverse languages, especially those with limited resources.

Ruotong Liao is a Ph.D. student at LMU Munich, supervised by Prof. Volker Tresp. Her research focuses on Generative AI, foundational multi-modal models, and large language models, with a special emphasis on reasoning over temporal data such as videos and dynamic graphs. Ruotong has authored several notable publications, including “VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning” presented at EMNLP 2024, and “GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models” featured in the Findings of NAACL 2024. Her work aims to advance the understanding and application of AI in processing and reasoning over complex temporal information.

Nicholas Popovič is a fourth-year Ph.D. student in the Web Science group at the Karlsruhe Institute of Technology. His research focuses on natural language processing and machine learning, particularly on representation learning for information extraction. Nicholas has authored several notable publications, including “Few-Shot Document-Level Relation Extraction” presented at NAACL 2022 and “Embedded Named Entity Recognition using Probing Classifiers” accepted at EMNLP 2024. His work aims to enhance the efficiency and accuracy of information extraction systems through advanced machine learning techniques.

Chunlan Ma is a Ph.D. student at the Center for Information and Language Processing (CIS) at LMU Munich. Her research focuses on natural language processing, multilingual large language models, and cross-lingual transfer learning. Chunlan has authored several notable publications, including “Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages”, presented at ACL 2023, and “Translico: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models,” featured at ACL 2024. Her work aims to enhance the performance of language models across diverse languages, especially those with limited resources.

Jin Zhang

ScaDS.AI/ Technische Universität Dresden