Skills & Master's Studies
What I studied, what I learned, and what I can actually do with it.
M.Sc. Computational Linguistics
My master's program combined theoretical linguistics with hands-on machine learning. Coursework in Computational Linguistics covered formal grammar, parsing, and language modeling; Deep Learning for Speech and Language Processing introduced neural architectures for sequence data, from RNNs and transformers to speech-to-text pipelines; and Machine Translation covered both statistical and neural approaches to translating between languages -- fitting, given my own background as a translator. Across the program, I built a solid foundation in applied data science: designing experiments, evaluating models properly, and turning messy, real-world text into structured data I can actually work with.
Thesis
My thesis looks at a specific linguistic choice -- the passive voice -- and what it quietly does to a news story. Passive constructions ("civilians were killed") can obscure who is doing what to whom, compared to active ones ("soldiers killed civilians"), shifting how readers assign responsibility and agency. I built a computational pipeline to detect passive voice constructions across a corpus of news coverage of the Israel–Palestine conflict, then analyzed how usage patterns differed depending on the actors involved and the outlet reporting on them. The goal wasn't to accuse any outlet of bias, but to show, with data, how a single grammatical choice can shape a reader's perception of a conflict -- and to demonstrate that this kind of framing is something NLP methods can actually help make visible and measurable.
Skills
Data Engineering
Programming
Others
Languages
| Language | Level |
|---|---|
| Thai | Native |
| Japanese | C1 |
| English | C1 |
| German | B2 |