MY ACADEMIC PATH
Since 2020, I am working as a doctoral researcher in natural language processing (NLP) at the Cluster of Excellence "The Politics of Inequality", University of Konstanz. My work is part of the project Framing Inequalities, where I focus on the automated detection of framing in journalistic texts (a ubiquitous persuasive strategy to promote opinions; see here) using NLP techniques.
I received a master's degree in Speech and Language Processing (2019, with a focus on NLP) and a master's degree in General Linguistics (2020, with a focus on formal semantics) from the University of Konstanz.
Earlier, I completed my bachelor's degree in Scandinavian Studies (Beijing Foreign Studies University, with a one-year state-funded exchange at the University of Southern Denmark). As this is a very unusual study program which only accepts around 15 new students every four years, I was often asked why I made this choice. Honestly, this was not desired by me at all: I rather wished to study German linguistics due to my fascination with several rock/gothic bands from the German-speaking region at that time. But in the higher education system at my country, especially contrasted with the German one where I currently reside, there is extremely limited freedom to choose the field of study or switch the study program. Instead, the priority was to play safe and secure a study place at any of the prestigious universities - no matter for which subject - rather than to follow personal interests.
But over time, my interest shifted strongly towards informatics. Yet, my passion for the German language still lasts until today.
RESEARCH INTERESTS
Broadly, I am passionate about applying machine learning and statistical approaches to explore large-scale datasets, with a particular focus on textual data. Specifically, my interests include:
- Explainable artificial intelligence (XAI) for large-scale text analyses in computational social science
- Statistical modelling and machine learning approaches for sparse design matrices or rare events
- Analysis of the behavior patterns of large language models (LLMs)