I’m a PhD candidate in Linguistics at the University of Maryland, working with Ellen Lau and Colin Phillips. My research investigates how different kinds of linguistic information—morphosyntactic structure and morphophonological form—are planned in production and retrieved during sentence processing, and how mismatches between them drive errors and create illusions in production and comprehension. I also work in formal properties of case and adjectival morphophonology, as well as question semantics and embedding facts.
I recently visited UMass to work with Shota Momma and Faruk Akkus. Previously, I received my MA from Boğaziçi University (advisor: Pavel Logačev) and was a visiting researcher at Masaryk University (advisor: Pavel Caha).
My favorite food is gata with koritz and my favorite icecream flavor is saffron and rose (5pm snack of Anthony Bourdain). In my freetime, I like doing calligraphy.
experiments are *necessary* and quite often *not misleading*, our blind trust in them is misleading. and I do not think the answer lies in being more careful with your stats, though that is also a must.
you do not have to hear yourself in experiments, and you can generate experiment instructions with ai. here is how.
i am obssessed with early advance planning, but let me make sure about some picture saliency
next step is to have a norming data on text-to-image-to-image generated standardized stimuli
on why that phrase is usually unhelpful and attempt to be a charitable
modern replacement for the outdated JS guide in the original pcibex website.
Kate’s asymmetry meets Turkish rounding: evidence that’s gradient but syntactically bounded (also a rant about dual-process framings)
step-by-step instructions for filtering, renaming, and aligning pcibex recordings with mfa
it got really annoying after a while, so i wrote this code
attraction in turkish seems to be modulated by bias, register, task details, but not morpology
findings point to a fricative status for /j/, despite its occasional sonorant disguise
turkish as if clauses specifically target gradable adverbials and adjectives, pointing to a degree-based analysis
using yaml with embedded r expressions for cleaner, reusable quarto configs