DATA SCIENCE + QUALITY ENGINEERING

Hi, I’m

Ping-Lun
Chen.

An engineer who likes getting to the bottom of things. I turn manufacturing data into better inspection decisions—and build software that makes messy information useful.

Explore the technical work A little about me

Previously at Corning
M.S. Data Science, The University of Texas at Austin

Portrait provided by Ping-Lun Chen — Ping-Lun Chen陳平倫

ON MY DESK LATELYSwing Scanner A personal project for the dance community.

01 / SELECTED WORK

Browse with the arrows, by dragging, or by swiping.

PERSONAL PROJECT · DATA ENGINEERING

DANCE COMMUNITIES / EVENTS / CONNECTIONSswing
scanner.CONCEPT COVER · NOT A PRODUCT SCREENSHOT

Reliable event data, from unreliable sources.

PERSONAL PROJECT · SPEECH ANALYSIS

LANGUAGE / LISTENING / LEARNINGHello.
Olá.CONCEPT COVER · ILLUSTRATIVE WAVEFORM

Feedback, down to individual sounds.

CORNING · CUSTOMER QUALITY / ESCAPE PREVENTION

Generic glass-production vision line depiction, not a photograph of Corning, a customer-return incident, or a measured result. — Reference illustration · generic production depiction, not a workplace photograph or return record

Returned batches. Cancelled orders. A detection gap to close.

FOOTWEAR MANUFACTURING · AUTOMATION & CONTROLS

AI-generated illustration of a generic footwear-production conveyor with a PLC cabinet and HMI, not the original shoe-manufacturing line. — AI-generated illustration · generic footwear automation, not the original production line

From machine behavior to a more reliable line.

CORNING · INSPECTION STRATEGY

CONCEPT COVER · NO PRODUCTION DATA

Compare the rule before adding another check.

CORNING · PRODUCTION IMAGE PROCESSING

PRODUCTION / IMAGE PROCESSINGA clearer
criterion.Repeatable defect classificationCONCEPT COVER · NOT MEASURED OUTPUT

From subjective judgment to production classification.

CORNING · PROCESS INVESTIGATION

Illustrative screening design-of-experiments workflow from candidate factors to a process decision; factor values and effects are examples, not project data. — Reference explanatory diagram · factors, effects and decisions are illustrative, not project data

Screen the causes, not just the finished product.

CORNING · MEASUREMENT SYSTEMS

INSPECTION / MEASUREMENT SYSTEMSMeasure the
measurement.Calibration / variation / consistencyCONCEPT COVER · NOT MEASURED OUTPUT

Separate measurement variation from the process.

UT AUSTIN · DEEP LEARNING

Illustrative convolution and pooling feature stacks; not the exact trained architecture, dimensions or model output. — CNN feature-stack illustration · not model output

Pixel segmentation and depth regression together.

UT AUSTIN · PREDICTIVE MODELING

Illustrative PM2.5 prediction workflow from satellite and temporal features through PCA and modeling; plots, model values and metrics are examples, not project results. — Reference explanatory diagram · plots, model values and metrics are illustrative, not project results

2,304 image features, with time kept in view.

UT AUSTIN · RELATIONAL DATA / REGRESSION

UT AUSTIN / RELATIONAL DATA13 tables.
One cohort.Performance / time / confoundingCONCEPT COVER · NOT MEASURED OUTPUT

Thirteen tables and a fairer comparison of progress.

Open a case to read the full project.

03 / A LITTLE CONTEXT

How I got here.

My background starts on the factory floor, not with a perfectly clean dataset.

I spent more than six years in display glass manufacturing, working on defects, inspection systems, and customer quality. The work meant understanding both what happened in the process and why our checks had missed it.

I later completed an M.S. in Data Science at UT Austin. Now I bring that engineering background to statistical modeling, machine learning, and the software projects I build.

2017–2024Corning Display TechnologyProcess engineering → Quality engineering

2023–2026The University of Texas at AustinM.S. in Data Science · completed

BEYOND THE JOB TITLE

There’s a language
thread here, too.

Mandarin, English, Portuguese.
And a little German.

I spent a year studying Portuguese in São Paulo. Alongside the technical work, languages are another part of my background—and a fitting connection to a project like Pronunciation Analyzer.

04 / HOW I WORK

The practical side.

Experience from work
and personal projects.

Data & machine learning

Python, R, SQL · PyTorch, scikit-learn
Statistical modeling, simulation, DOE

Quality engineering

Root-cause analysis, 8D / CAPA, SPC
Inspection strategy, NPI, qualification

DevOps & deployment

Git / GitHub, CI/CD, Docker, Linux / VPS
k3s, Terraform, Helm

Personal-project use, with AI-assisted implementation.

Agentic software development

Claude Code, OpenAI Codex, GitHub Copilot
Agent-assisted implementation, testing, and debugging

Personal-project workflows, with review and validation.

05 / CURRICULUM VITAE

Data Science CV.

Industrial analytics, modeling & software projects.

Preview CV ↗Download CV ↓

Ping-Lun
Chen.

01 / SELECTED WORK

Swing Scanner

What I worked on

Pronunciation Analyzer

What I worked on

When shipped glass came back.

What I worked on

When is more inspection not better inspection?

What I worked on

A repeatable answer to “what defect is this?”

What I worked on

Finding the process variables behind a defect.

What I worked on

Can the inspection gauge be trusted?

What I worked on

One image. Two prediction tasks.

What I worked on

Predicting PM2.5 from satellite features.

What I worked on

Who is improving—not just performing well?

What I worked on

PLC-driven line automation.

What I worked on

How I got here.

There’s a language
thread here, too.

The practical side.

Data & machine learning

Quality engineering

DevOps & deployment

Agentic software development

Data Science CV.

Ping-LunChen.

01 / SELECTED WORK

How I got here.

There’s a languagethread here, too.

The practical side.

Data & machine learning

Quality engineering

DevOps & deployment

Agentic software development

Data Science CV.

Ping-Lun
Chen.

There’s a language
thread here, too.