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Resume & CV

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Summary

I am a Ph.D. mixed methods researcher with 4+ years of experience designing and executing behavioral experiments and surveys, as well as analyzing data using Python and R. Experienced in cross-functional collaboration, understanding how data can inform us about human behavior, and applying mixed research methods to find solutions to open-ended problems.

Education

University of California, Los Angeles (UCLA)

Ph.D. in Linguistics (Expected 2024)

Duke University

B.S. in Chemistry and Linguistics (2019)

Experience

UCLA Department of Linguistics | Graduate Researcher         (2019-)

PhonProv: A phonetic corpus of naturalistic infant-directed speech              

  • Led a diverse team of 5 research assistants on an end-to-end research project, including developing and analyzing a database of 100,000+ tokens of speech, followed by a quantitative experiment

  • Analyzed speech patterns to quantify the degree of phonetic variability and noisiness of 100,000+ tokens of speech directed at infants and pre-school aged children

  • Manipulated and analyzed high-volume, high-dimensionality data from the corpus with self-developed code, using appropriate exploratory methods and statistical analyses in Python and R

  • Independently designed and executed an infant speech processing experiment based on insights from the corpus analysis; designed stimuli; used Bayesian statistical models for data analysis

  • Communicated findings to large group of stakeholders in compelling and creative ways

  • Awarded $100,000+ in competitive grants and fellowships

 

4th Man Project: Event perception over development                  

  • Designed and implemented an online survey and experiment using Labvanced and Prolific to investigate adults’ perception of complex event types to further understanding of multimodal language acquisition

  • Designed child behavioral experiments to determine developmental trajectory of perception of these events

 

Computational modeling of developmental human sentence processing     

  • Developed a top-down model of preschooler’s parsing difficulties with ambiguous sentences using natural language processing (NLP) tools to probe interactions of developing cognitive systems with language acquisition

  • Implemented beam-search algorithms to generate testable empirical predictions for future experimental work

 

Kingsland Marketing Consultancy | Strategy Intern                   (2022)

  • Worked cross-functionally with agency employees outside of the strategy department, clients, and vendor partners to execute several critical projects

  • Wrote, led and supported the team with creative brand strategy documents, market research, competitive audits, mining for insights, customer archetyping, positioning, and more

Skills

Research methods: 

Online survey

Semi-structured interviews

A/B testing

Eye-tracking

Programming:

R

Python

SQL

Statistical methods:

Linear mixed effects models

Logistic mixed effects models

Bayesian mixed effects models

Multinomial regressions

Inter-rater reliability

Descriptive statistics

Machine learning: 

Regression and classification

Reinforcement learning 

Decision trees

TensorFlow

Miscellaneous:

Data visualization (RShiny)

Video editing (Premier Pro)

Notion

2D animation (Procreate)

Languages

Natural Languages:

English 

Russian

French (limited working)

Talks & Publications

Publications

Khlystova, Chong, and Sundara (2023). Phonetic variation in English infant-directed speech: A large-scale corpus analysis. Journal of Phonetics 100 (2023): 101267. 

Talks & Posters

Khlystova, E.* and Sundara, M.. The lexical representation of coda /t/. Acoustical Society of America (ASA): Poster presentation, 5 December 2023. Sydney, Australia. pdf coming soon!

Narkar, J., Khlystova, E.*, Mayer, C., Aly, A., Kim, J. Y., and Sundara, M. . Evaluating the learnability of vowel categories from Infant-Directed SpeechAcoustical Society of America (ASA): Poster presentation. Denver, CO. Click here for a pdf. 

Khlystova, E.*, Chong, A., and Sundara, M.. Quantifying phonetic variation: A large-scale corpus analysis of coronal segments in English infant-directed speech. Acoustical Society of America (ASA): Poster presentation, 29 Nov 2021. Seattle, WA. Click here for a pdf.

Khlystova, E.*, Chong, A., and Sundara, M.. Phonetic variation in coronals in English infant-directed speech: A large-scale corpus analysis. Boston University Conference on Language Development (BUCLD): Poster presentation, 6 Nov 2021. Virtual. Click here for a pdf.

*presenting author

Teaching

Courses taught

LING140: Bilingualism and second language acquisition

Responsibilities: developed lesson plans for discussion sections based on the week’s lectures, guided students through effectively planning and executing a survey- or observation-based research project, held weekly office hours and graded homework assignments and papers.

LING132: Language Processing

Responsibilities: developed lesson plans for discussion sections based on the week’s lectures and instructor’s worksheets, held weekly office hours and graded homework assignments and exams.

LING130: Language Development 

Responsibilities: developed lesson plans for discussion sections based on the week’s lectures, guided students through effectively planning and developing an experimental research proposal, held weekly office hours and graded homework assignments and exams.

LING20: Introduction to Linguistic Analysis

Responsibilities: developed lesson plans for discussion sections based on the week’s lectures and instructor’s worksheets, held weekly office hours and graded homework assignments and exams.

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