Short bios of the instructors

 

Professor R. Harald Baayen is a Professor of Quantitative Linguistics at the University of Tübingen. He is one of the most influential and innovative researchers in the field of lexical statistics and quantitative linguistics.  He is a pioneer of quantitative research in corpus linguistics and psycholinguistics using advanced statistical modeling (mixed-effects models, generalized additive modeling), and has made fundamental contributions to our understanding of human speech and the role of the memory in language processing. His current research focuses on computational modeling of language processing using naive discrimination learning. In 2017, he received the prestigious ERC Advanced Grant for his WIDE project (“Wide Incremental learning with Discrimination nEtworks”).  

Harald Baayen’s website: http://www.sfs.uni-tuebingen.de/~hbaayen/index.html

 

Ilmari Ivaska is an applied linguist with a specialization in second language acquisition and corpus methodologies. He completed his education with a PhD at the University of Turku,  Finland in 2015. Ivaska has worked as a visiting lecturer at the University of Washington, USA, is currently positioned as a postdoctoral researcher at the University of Bologna and has been appointed as an assistant professor of Finnish at the University of Turku as of fall of 2019. In his research, Ivaska has focused on data-driven corpus methodologies and their applications in studying typical tendencies in texts written by L2 language users and, more recently, translated texts written by L1 language users, when compared to non-translated texts written by L1 language users. The investigation of new ways to study linguistic divergence between different modes of language use lie at the core of Ivaska’s research interests. He finds it fascinating that the factors to-be-taken-into-account can range from genres to the communication task at hand, and from the nativeness of the language users to constraining factors of multilingual communication – and the myriad relationships between these factors.