Daniel Wiechmann

Dr Daniel Wiechmann is a researcher at the University of Amsterdam's Faculty of Humanities, Department of Literature and Language Science, affiliated with the Institute for Logic, Language and Computation (ILLC). His primary focus lies in Natural Language Processing with applications in mental health informatics and computational psycholinguistics.

His research interests center on explainable AI for mental health detection, specializing in hybrid models that integrate transformer architectures with psycholinguistic features. He investigates German-language social media analysis (evidenced by SMHD-GER and FANG-COVID datasets), personality detection through verbal behavior, and text simplification techniques. Recent work emphasizes cross-lingual mental disorder classification and feature fusion strategies for improved model interpretability in clinical contexts.

Analysis of his 15 most recent publications (2021-2024) reveals a strong trajectory toward multilingual mental health detection systems, with significant contributions to German-language NLP resources. His work consistently bridges computational linguistics and clinical psychology through

• Hybrid transformer-psycholinguistic model architectures
• Large-scale dataset creation for mental health screening
• Explainability frameworks for clinical decision support

Dr Wiechmann maintains active research output with 20+ publications since 2021, primarily through the MANTIS research group, focusing on social media-based mental health detection systems and German-language NLP applications.