Yen-chieh Liao is an affiliate fellow and computational political researcher with eight years’ experience bridging comparative politics and AI-driven methods. He completed a PhD project on Taiwan’s electoral reform using ideal point estimation and now applies NLP and deep learning to legislative speeches, social media, and even facial/vocal cues to quantify polarization and internal conflict. His roles at University of Birmingham, Birmingham CAIG, Bamberg and UCD reflect a mix of hands-on data scraping across parliaments and methodological teaching in quantitative text analysis. Yen-chieh’s work is notable for combining traditional legislative studies with novel multimodal approaches—using speech, text and visual signals—to uncover subtle drivers of representation and pork-barrel behavior. He has secured competitive funding (Taiwan Government Scholarship, MOST TOP Grant) and contributes to cross-national datasets that support comparative oversight research. Based in the UK, he brings both policy domain expertise on the Legislative Yuan and practical technical skills in computational social science.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Political Science and Government, Doctor of Philosophy - PhD Political Science and Government at University of Essex
Master of Arts - MA Public Administration and Public Policy, Master of Arts - MA Public Administration and Public Policy at National Taipei University
The asmcjr package supports the book “2nd Analyzing Spatial Models of Choice and Judgment”, and streamlined functions for analysing and visualising estimates with ggplot2
Contributions:148 commits, 59 PRs, 524 pushes in 2 years 8 months
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