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EILEEN BLUM

PhD

(510)407-2646                     eileen.blum92@gmail.com

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ABOUT ME

I am an experienced technical writer specializing in conversational AI, specifically Google CCAI. I like to describe my work as translating software engineer for the general public. Through documentation, I ensure that anyone can understand what software features offer and how to implement them.

I excel at paying attention to details while maintaining a vision of the larger goals for a project. I value clear communication, organization, concise writing, and thrive with responsibility. I have written public-facing conceptual documents, setup and troubleshooting guides, and best practices; client-specific recommendations for using DialogflowCX; and  internal task instructions used to train new employees. I love when something I have written can directly help others to accomplish their technical goals.


Outside of work, I love spending time with animals. I rode and trained off-the-track thoroughbreds in the hunter/jumper discipline for over ten years and now I volunteer at a local adaptive riding program. I trained my first dog as a kid and even trained my late cat to perform some basic tasks on cue.

Home: About Me

EDUCATION

PHD LINGUISTICS, RUTGERS UNIVERSITY

September 2015 - January 2023

BA LINGUISTICS, UNIVERSITY OF CALIFORNIA SANTA CRUZ

September 2012 - June 2014

INTERSEGMENTAL GENERAL EDUCATION TRANSFER CURRICULUM, DIABLO VALLEY COLLEGE

August 2010 - May 2012

Home: Education

WORK EXPERIENCE

February 2024 - Present

TECHNICAL WRITER, Data Piper

Google Contact Center AI – Agent Assist and Insights 

  • Write 4 documents including introduction, concepts, setup guide, and best practices to create the knowledge base for a new Google product. 

  • Update over 17 public documents, including user guides, API documentation, and troubleshooting manual 

  • Create API documentation and release notes for 4 simultaneous feature releases within a two-week timeline 

  • Collaborated closely with SMEs to maintain quality and accuracy of content 

  • Identified audience and purpose to tailor tone of documents for improved user comprehension 

  • Convert Google documents to markdown for online publication 

  • Create, manage, and submit changes using Cider-V and Critique 

  • Adhere to Google quality standards and style guides to ensure everyone can access and understand content 

September 2022 - February 2024

DIALOGUE DESIGNER, Data Piper

Google Contact Center AI – Agent Assist 

  • Edited 4 Agent Assist documents for clarity and comprehension in English 

  • Wrote client-facing DialogflowCX recommendations to improve call containment up to 185% 

  • Wrote instructions and sample data for 90 conversations to train an Agent Assist LLM 

  • Annotated and summarized 557 customer service conversations over 6 months for 5 clients 

  • Reviewed existing documentation and participated in peer reviews to improve clarity and accuracy of content 

July 2021 - July 2022

DIALOGUE DESIGNER, Tek Systems

Google Contact Center AI –Virtual Agent 

  • Designed, co-wrote, and edited best practices for Dialogflow CX 

  • Wrote documentation guide and edited ReadMe for SCRAPI Python library on Github 

  • Wrote 17 instructional documents to clarify processes and train 5 new team members 

  • Annotated conversation data to identify virtual agent failures and successes 

  • Able to prioritize and work under pressure and with tight deadlines in an agile workflow 

Home: Experience

PROJECTS

PhD DISSERTATION

The effects of non-linear data structures on the computation of vowel harmony (Jan 2023)

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 data structures on complexity and I develop a new theory of autosegmental locality.

METAL LYRICS GENERATOR

Erdös Institute Natural Language Processing Bootcamp (February-March, 2021)

My partner and I built a Wasserstein Generative Adversarial Network (WGAN) in Python to generate automated song lyrics in lines of 8 words at a time. We compared our WGAN with a Soft-GAN trained on the same dataset. We trained both GANs on a Kaggle dataset of metal song lyrics, which we processed using NLTK and pandas. The GANs were built using Keras, Tensorflow, and Numpy. Lastly, we calculated BLEU scores for both models and determined that neither generated very natural sounding lyrics: WGAN received all 0s, Soft-GAN averaged 0.06 for n-grams of length 1-4.

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.

Home: Projects
Home: Projects
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