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*.fls | ||
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*~ | ||
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*etter* | ||
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*.lock |
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# frozen_string_literal: true | ||
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source "https://rubygems.org" | ||
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# gem "rails" | ||
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gem "erb", "~> 4.0" | ||
gem "json", "~> 2.7" | ||
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gem "pandoc-ruby", "~> 2.1" |
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# Ryan Hildebrandt | ||
### Data Scientist & NLP Specialist | ||
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--- | ||
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[Github](https://github.com/ryancahildebrandt) | ||
[LinkedIn](https://linkedin.com/in/rcah) | ||
[ResearchGate](https://researchgate.net/profile/Ryan-Hildebrandt) | ||
#[Github](https://github.com/ryancahildebrandt) [LinkedIn](https://linkedin.com/in/rcah) [ResearchGate](https://researchgate.net/profile/Ryan-Hildebrandt) | ||
[email protected] | Corning, New York | ||
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--- | ||
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## Work Experience | ||
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## Skills | ||
### **Programming Languages, Tools, & Platforms** | ||
Keywords: *R, Python, Ruby, SQL, NoSql Databases, Vector Databases, Julia, MATLAB, SAS, Microsoft VBA, Google AppsScript, Git, Shell, Linux/Unix, Windows, Google Cloud Platform* | ||
*R, Python, Ruby, SQL, NoSql Databases, Vector Databases, Julia, MATLAB, SAS, Microsoft VBA, Google AppsScript, Git, Shell, Linux/Unix, Windows, Google Cloud Platform* | ||
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### **Data Analysis & Statistical Techniques** | ||
Keywords: *Data manipulation, Data tidying, NLP (sentiment analysis, topic modeling, embedding), Feature engineering, Regresison (linear, logistic, polynomial), Mixed linear modeling, Structural equation modeling, (M)AN(C)OVA, Unsupervised & supervised classification, Principal component analysis & dimensionality reduction* | ||
*Data manipulation, Data tidying, NLP (sentiment analysis, topic modeling, embedding), Feature engineering, Regresison (linear, logistic, polynomial), Mixed linear modeling, Structural equation modeling, (M)AN(C)OVA, Unsupervised & supervised classification, Principal component analysis & dimensionality reduction* | ||
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### **Data & Statistics Software** | ||
Keywords: *SPSS, Tableau, Excel, Microsoft Access, Google Data Studio, KNIME, JASP, jamovi, RStudio, Spyder, MiniTab, Amazon MTurk, Qualtrics, MongoDB, Power BI, Looker, BigQuery* | ||
*SPSS, Tableau, Excel, Microsoft Access, Google Data Studio, KNIME, JASP, jamovi, RStudio, Spyder, MiniTab, Amazon MTurk, Qualtrics, MongoDB, Power BI, Looker, BigQuery* | ||
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### **Research Skills** | ||
Keywords: *Experimental design, Survey design, Data collection, Literature review, Data analysis, Data visualization, Research presentation, Human subjects research, IRB compliance, Scientific writing, Academic writing* | ||
*Experimental design, Survey design, Data collection, Literature review, Data analysis, Data visualization, Research presentation, Human subjects research, IRB compliance, Scientific writing, Academic writing* | ||
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### **Language** | ||
Keywords: *Language teaching, Translation, Linguistic theory, Cultural & educational activities, Natural Language Processing, Multilingual Research* | ||
*Language teaching, Translation, Linguistic theory, Cultural & educational activities, Natural Language Processing, Multilingual Research* | ||
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## Education | ||
### **Rochester Institute of Technology** | ||
### Rochester Institute of Technology | ||
*Graduate - Experimental Psychology*, 2018-01 - 2019-12 | ||
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### **Colgate University** | ||
### Colgate University | ||
*Undergraduate - Japanese & Psychology*, 2013-08 - 2017-05 | ||
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### **Kyoto Japanese Language School** | ||
### Kyoto Japanese Language School | ||
*Semester-long Language Study - Japanese*, 2014-08 - 2014-12 | ||
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## Volunteer Work | ||
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- Communicate in Japanese with staff and other assistants to conduct conversatory operations | ||
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## Projects | ||
### **[particles](https://github.com/ryancahildebrandt/particles)** | ||
### **[particles(https://github.com/ryancahildebrandt/particles)]** | ||
Contextual particle frequency in written Japanese, taking a swing at the age old question of は vs が | ||
Keywords: *Report, Python, Japanese, NLP* | ||
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### **[thesis](https://github.com/ryancahildebrandt/thesis)** | ||
### **[thesis(https://github.com/ryancahildebrandt/thesis)]** | ||
Analyses from my graduate thesis work, Investigating Emotion-label and Emotion-laden Words in a Semantic Satiation Paradigm | ||
Keywords: *Publication, R, Psycholinguistics, Study* | ||
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### **[aozora_corpus](https://github.com/ryancahildebrandt/aozora_corpus)** | ||
### **[aozora_corpus(https://github.com/ryancahildebrandt/aozora_corpus)]** | ||
A compilation of Japanese texts pulled from 青空文庫 | ||
Keywords: *Corpus, R, Japanese, Literature* | ||
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### **[hanakotoba](https://github.com/ryancahildebrandt/hanakotoba)** | ||
### **[hanakotoba(https://github.com/ryancahildebrandt/hanakotoba)]** | ||
Looking at the use of 花言葉 in literature | ||
Keywords: *Report, Python, Japanese, Literature* | ||
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### **[embs](https://github.com/ryancahildebrandt/embs)** | ||
### **[embs(https://github.com/ryancahildebrandt/embs)]** | ||
Tools streamlining sentence embedding or clustering techniques | ||
Keywords: *Application, Report, Python, NLP* | ||
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### **[siftr](https://github.com/ryancahildebrandt/siftr)** | ||
### **[siftr(https://github.com/ryancahildebrandt/siftr)]** | ||
Shiny app using SIF sentence embeddings to separate out unwanted text data | ||
Keywords: *Application, R, NLP, Sentence embedding* | ||
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### **[priors](https://github.com/ryancahildebrandt/priors)** | ||
### **[priors(https://github.com/ryancahildebrandt/priors)]** | ||
An experiment combining pretrained and bag of words embeddings to incorporate prior semantic knowledge | ||
Keywords: *Report, Python, NLP, Sentence embedding* | ||
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### **[bowts](https://github.com/ryancahildebrandt/bowts)** | ||
### **[bowts(https://github.com/ryancahildebrandt/bowts)]** | ||
An experiment combining pretrained and bag of words embedding approaches for embedding vector space manipulation | ||
Keywords: *Report, Python, NLP, Sentence embedding* | ||
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### **[iterate](https://github.com/ryancahildebrandt/iterate)** | ||
### **[iterate(https://github.com/ryancahildebrandt/iterate)]** | ||
Iterative clustering for sklearn clusterers | ||
Keywords: *Tools, Python, Clustering, NLP* | ||
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## Languages | ||
### **English: Native** | ||
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### **Japanese: CEFR B2** | ||
# | ||
### **Japanese: CEFR B2** | ||
*Conversational: Advanced High* | ||
*Compositional - Spoken: Advanced Mid* | ||
*Compositional - Written: Intermediate High* | ||
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https://google.accredible.com/d3ef369f-9c2c-486a-bda6-943a74f70dff | ||
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## Research Experience | ||
### Emotion Word Processing in a Satiation Paradigm | ||
### **Emotion Word Processing in a Satiation Paradigm** | ||
Rochester Institute of Technology | ||
Graduate Thesis | ||
- Study investigating how emotion word type and valence impact word processing via semantic satiation | ||
- R : Stimulus selection, data analysis & visualization | ||
- Qualtrics/SuperLab : Data collection & experiment coding | ||
- Presented at Psychonomic Society 2019 Meeting, Montreal | ||
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### Detection & Perception of Emotional Faces | ||
### **Detection & Perception of Emotional Faces** | ||
Rochester Institute of Technology | ||
Graduate Research Assistantship | ||
- Research examining the categorization, detection, and rating of emotional faces as compared to other images | ||
- R : Stimulus selection | ||
- Python/PsychoPy : Experiment coding | ||
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### Content Retention & Note Taking Style | ||
### **Content Retention & Note Taking Style** | ||
Rochester Institute of Technology | ||
Graduate Research Assistantship | ||
- Study looking at how note taking mode (laptop or hand-written) impacts retention of different kinds of information in an educational setting | ||
- Presented at APS 2019, Washington DC | ||
- Published: Crumb, R., Hildebrandt, R. & Sutton, T. M. (2019). The value of handwritten notes: A failure to find state-dependent effects when using a laptop to take notes and complete a quiz. | ||
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### Speech Intelligibility Testing for Novel Noise Reduction Algorithm | ||
### **Speech Intelligibility Testing for Novel Noise Reduction Algorithm** | ||
Rochester Institute of Technology | ||
Graduate Researcher Position | ||
- Project assessing noise reduction algorithms for use in microphones used by first responders | ||
- Praat/R : Stimulus preparation, data coding | ||
- MTurk : Experiment coding, data collection | ||
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### Integration of Visual Cues When Reproducing Novel L2 Phonemes | ||
### **Integration of Visual Cues When Reproducing Novel L2 Phonemes** | ||
Colgate University | ||
Undergraduate Thesis | ||
- Assessing how visual cues impact how accurately English speakers reproduce Japanese long vowels and geminate consonants | ||
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### Synthesis of English & Japanese Research on the McGurk Effect | ||
### **Synthesis of English & Japanese Research on the McGurk Effect** | ||
Colgate University | ||
Independent Research Grant | ||
- Translating and synthesizing English and Japanese scientific literature for use in my thesis study through a student research grant awarded by Colgate University | ||
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