Ruiping Huang

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Ruiping Huang
Résumé ↓
Data Science · Analytics · Statistics

I turn complex data into decisions people can act on.

I’m Ruiping Huang — a statistician and PhD candidate (M.S. Statistics) with hands-on data-analytics experience across education and nonprofit research. I build predictive and explanatory models, and turn messy, real-world data into clear, honest insight.

Open to Data Scientist & Data Analyst roles

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Ruiping Huang
◆ PhD Candidate · UIC ◆ M.S. Statistics · 4.0 ◆ Python · R · SQL ◆ Erdős Institute ’26
About

Statistics, rigor, and plain-language answers.

I work where statistics, machine learning, and real-world questions meet. My graduate training at the University of Illinois Chicago — an M.S. in Statistics alongside doctoral research in measurement & assessment — gave me a deep foundation in modeling, experimental design, and interpreting data responsibly.

I’ve put it to work as a data analytics intern and research analyst: modeling Chicago Public Schools curriculum data, segmenting students, and evaluating programs for nonprofits. I care about getting the analysis right — and about explaining it in language a decision-maker can actually use. I’m fluent in English, Mandarin, and Cantonese.

What I’m looking for Data Scientist / Data Analyst roles where I can build models, design analyses, and help teams make better decisions with data.
Selected work

Projects

A few projects that show how I frame a problem, model the data, and land on something useful.

Predictive Modeling · Erdős Institute

Predicting Teachers’ Use of AI in the Classroom

PythonPredictive ModelingSurvey DataFeature Selection
Problem

Schools are investing in AI, but adoption is uneven. Which teacher and school characteristics actually predict whether educators use AI tools?

Approach

Cleaned and modeled the TALIS 2024 international teacher survey in Python — feature selection, predictive modeling, and visualization to surface the strongest drivers of adoption.

Outcome

Public GitHub repository and a presentation at the Erdős Institute (Summer 2026).

View on GitHub
// TALIS 2024 · adoption drivers
Internship · The Learning Partnership

Measuring Curriculum Impact in Chicago Schools

HLMR / SASCPS DataMultilevel
Problem

Does a curriculum actually move student outcomes — once you account for students being nested inside very different schools?

Approach

Analyzed Chicago Public Schools datasets with hierarchical linear (multilevel) modeling to separate true curriculum effects from school-to-school differences.

Outcome

Presented at a research symposium and published the report in a national quarterly magazine.

// curriculum effect · multilevel fit
Research · University of Illinois Chicago

Segmenting Students by Engagement

Latent Class AnalysisMplusMixed Methods
Problem

Students aren’t simply “engaged” or not. How many distinct engagement profiles actually exist, and what defines each one?

Approach

Ran latent class analysis on 386 students to surface hidden engagement segments, then coded open-ended responses to give each class real-world meaning.

Outcome

A data-driven segmentation that pairs statistical classes with the qualitative story behind them.

// 3 latent engagement classes
Award-winning · Project Leader

What Drives Teacher Well-Being? n = 848

SEMMixed MethodsSurvey n=848
Problem

What really drives teacher well-being — and how do workload, support, and efficacy fit together?

Approach

Led a mixed-methods study (848-teacher survey + 6 interviews) and built a structural equation model to map how the drivers relate.

Outcome

Won a provincial research competition for the analysis and its practical recommendations.

Support Workload Well-being // SEM · drivers of well-being

Full experience, publications & awards live on my résumé. Download it →

Career so far

Experience

Hands-on data & research roles across universities and nonprofits.

2024

Data Analytics Intern

The Learning Partnership

Analyzed Chicago Public Schools datasets with hierarchical linear modeling to examine curriculum effects; presented at a research symposium and published a report in a national quarterly magazine.

2021 — Present

Research & Data Assistant · Project Leader

UIC — PASTA & SkillTree Projects

Lead an AI teacher-development research team and co-wrote a $119K NSF grant. Built React admin tools and survey workflows, and analyzed 42 AI education tools for the SkillTree platform.

2021 — 2022

Data Analytics Assistant

Student Engagement Project, UIC

Performed latent class analysis on 386 students and coded qualitative responses to profile engagement patterns.

2021

Data Analytics Intern

Save the Children — Guangzhou

Designed surveys, ran mixed-methods program evaluation, managed databases, and co-authored an accountability report.

2018 — 2019

Project Leader

Teachers’ Well-Being Study, Guangzhou

Ran a mixed-methods study (n = 848 survey, 6 interviews) and built an SEM model — winning a provincial research competition.

Honors: 3-Minute-Thesis Finalist, UIC Graduate College (2026) · Graduate Student Council Travel Award (2025)

Toolkit

Skills & tools

The stack I reach for, from raw data to a decision someone can act on.

Languages & Querying

  • Python
  • R
  • SQL
  • Node.js

Statistics & Modeling

  • Hierarchical linear modeling
  • Structural equation modeling
  • Latent class analysis
  • Predictive modeling
  • IRT & psychometrics

Statistical Software

  • SAS · STATA · SPSS
  • Mplus · AMOS · HLM
  • Winsteps · IRTPro
  • ConQuest

Viz, Survey & Tools

  • Tableau
  • Qualtrics
  • GitHub · VS Code
  • Figma
Leadership & community

AI leadership, done responsibly

Beyond the analysis, I help people use AI thoughtfully — as a graduate of the AI Leaders Program, sponsored by WordPress.

AI Leadership & Artifacts

Real artifacts of agentic & generative AI use — including my open-sourced Peer-Review Comment Refinement Workflow and experiments in AI-assisted manuscript review.

See my AI artifacts →

AI Ethics

How I use AI honestly and transparently — to augment human judgment, never replace it — with a worked example from my TALIS project.

Read the statement →

WordPress & AI

How I build with WordPress and current AI tools — including how this portfolio itself was designed, iterated, and assembled.

How I built this →
Research depth

Research-grade rigor behind every analysis.

My doctoral research at UIC focuses on measuring and improving AI literacy in education — including systematic reviews and assessment design. For the full academic profile (publications, teaching, and service), visit my research site.

Academic site → Google Scholar →

Let’s work together.

I’m open to data scientist and analyst opportunities. The fastest way to reach me is email.

rhuang26@uic.edu LinkedIn GitHub