I am a sixth year abd PhD student in the linguistics department at Rutgers University. I am interested mainly in computational phonology. My dissertation uses a formal language theory approach to investigate the computational complexity of vowel harmony patterns over multi-tiered autosegmental representations.
Outside of academia, I also enjoy training animals and medieval armored combat. I have ridden and trained off-the-track thoroughbreds in the hunter/jumper discipline for over ten years. I participated in heavy combat in the Society for Creative Anachronism (SCA) and I enjoy larp and boffer fighting, with an affinity for pole weapons, particularly glaive and axe.
September 2015 - June 2021
LINGUISTICS FELLOW & TA, RUTGERS UNIVERSITY
Research: Phonology, Formal Language Theory, Computational Complexity, Acoustic Experimentation
Developed theory to explain how representations affect computational complexity of vowel harmony patterns across languages
Recorded native speakers, analyzed data using Praat t-tests, and linear mixed effects models in R
Managed all aspects of multiple projects from inception to completion
Strong proficiency in IPA and other linguistic representations
Self motivated and prioritized multiple tasks to meet deadlines
June 2013-August 2014
DAY CAMP RIDING & HORSE DAY CAMP DIRECTOR,
ROUGHING IT DAY CAMP
Received formal management training
Coordinated between two on-site programs, Supervised staff for both
Delegated tasks, Organized weekly meetings, Prepared and managed daily staff schedules
(tentative title): How locality of vowel harmony is affected by representations (Oct 2021)
I apply formal language theory to natural language data in order to analyze the computational complexity of vowel harmony patterns across both well studied and understudied languages. I use this computational approach to investigate the effects of different representational primitives on computation and develop a new theory of autosegmental locality.
METAL OR NOT?
Erdös Institute Data Science Bootcamp, May 2020
My partner and I created a classifier in Python to distinguish song lyrics by genre. We used two Kaggle data sets of song lyrics, which were cleaned using the GenSim and NLTK packages. We then used the shallow neural network in the Word2Vec package to create high-dimensional word vectors, PCA and k-clustering to group them based on semantic similarity, and trained a DecisionTreeClassifier to distinguish lyric sets. The classifier achieved 81% accuracy.
QUALIFYING PAPER 2
On the locality of vowel harmony over autosegmental representations (2018)
I applied Formal Language Theory to natural language data in order to analyze the computational complexity of vowel harmony patterns over autosegmental representations. I analyzed vowel harmony patterns in multiple languages and predicted possible cross-linguistic variation.
QUALIFYING PAPER 1
Allophony-driven stress in Munster Irish (2018)
I designed and implemented a production experiment to determine the word stress pattern of Munster Irish (Gaelic). I organized all the data files by hand in Excel, then annotated, transcribed, and analyzed all of the acoustic data by hand in Praat. Statistic analyses was performed using t-tests in Excel then verified using linear mixed effects models in R.