I'm Duc, a manufacturing engineer with a background in optical measurement and computational research. I’m interested in making complex physical systems easier to understand—and improve.
HOANG QUANG DUC / ENGINEER & CURIOUS PROBLEM SOLVER
FIG. 01 — A CONNECTED SYSTEM↗
PHYSICAL PROCESS → DIGITAL UNDERSTANDING01 / 03
Currently Manufacturing engineering · MeikoBackground Science Tokyo · GSEPPerspective Vietnam → Japan
01 / Selected work
From observation to understanding.
Three areas that shape how I approach engineering problems.
01 — MANUFACTURING↗
Understanding the production floor.
Developing practical process knowledge in PCB manufacturing, with exposure to MSAP plating, vacuum hole filling, and the relationship between materials, equipment, and quality.
PCB manufacturingProcess evaluation
Inside the work
My work at Meiko includes process observation, physical-property evaluation, standard-document revisions, and equipment and material evaluation. Production-line training has included exposure, plating, DES, AOI, and AVI.
Direction: connect this process understanding with better traceability, data systems, and automation.
02 — OPTICAL MEASUREMENT↗
Connecting hardware with computation.
Internship experience at OCTEC spanning projector–camera calibration, structured light, and optical measurement—where physical experiments meet software and analysis.
CalibrationStructured light
Inside the work
Worked on optical measurement topics including spectral and photometric measurement and a portable BRDF measurement device. This experience shaped my interest in building useful systems across hardware and software.
03 — COMPUTATIONAL RESEARCH↗
Finding patterns in translation.
Bachelor’s research investigating translator style in Japanese retranslations of Sherlock Holmes, using text processing and machine learning.
Python & NLPMachine learning
Inside the work
Built a workflow spanning OCR, Japanese tokenization, linguistic feature extraction, and classification. Explored part-of-speech patterns and character features, including evaluation designed to account for story-level differences.
Engineering lesson: the quality of an answer depends on both the data pipeline and how the experiment is evaluated.
02 / Experience
A foundation across disciplines.
Learning how things work, from individual measurements to the systems around them.
APR 2026 — NOW
Manufacturing engineering
Meiko Electronics / Miyagi Meiko
Joined Meiko in April 2026. Assigned to Ishinomaki production engineering in September, developing hands-on experience in PCB processes through on-the-job training.
JAN 2023 — MAR 2026
Engineering intern
OCTEC Inc.
Projector–camera calibration, structured-light methods, and optical measurement across hardware, experiments, and computation.
UNDERGRADUATE
Transdisciplinary Science & Engineering
Institute of Science Tokyo · GSEP
Engineering education spanning disciplines, with bachelor’s research in computational analysis of translation style.
03 / How I think
“I like hard problems that can be stated simply—and whose solutions are useful.”
I’m drawn to the space between physical engineering and software: understanding a process, representing it with data, and building something that helps people make better decisions.
My longer-term interests include factory automation, manufacturing data architecture, and AI-assisted engineering. I’m building toward these through practical process experience and continued learning.