Min-Ah Cheon Ph.D.
NLP · Generative AI · LLM Prompt Engineering Researcher (Ph.D. in Engineering).
Architecting language intelligence by bridging linguistic theory with applied machine learning.
Core Competencies
What I do
From LLM prompt engineering to corpus construction and automated scoring — research and engineering across the full NLP stack.
LLM Prompt Engineering
Researching workflow optimization with large language models. Designing and evaluating prompts for generative AI applications.
Named Entity Recognition
Deep-learning multilingual NER. Published the BIT named-entity tagging scheme for low-resource environments in Journal of KIISE.
Corpus Construction
Automatic and semi-automatic generation of large training corpora. Earned Ph.D. with a method using relationship scores of multiple generative units.
Automated Scoring
Built a Korean short-answer automated scoring system with KICE over five years, resulting in three registered patents.
Bilingual Lexicon Mining
Automatic bilingual dictionary construction from non-parallel comparable corpora using SOM and perceptron learning.
R&D Trend Analysis
Global R&D investment analysis commissioned by the National Research Foundation of Korea. Data-driven discovery of promising researchers.
About me
I'm an NLP and AI researcher who earned a Ph.D. in natural language processing from Korea Maritime & Ocean University, with eighteen national R&D projects under organizations such as KICE, ETRI, and the National Research Foundation of Korea.
Today I lead workflow-efficiency research with LLM prompt engineering at MetaIntelligence, alongside global R&D trend analysis.
Education
Ph.D. in Computer Engineering, Korea Maritime & Ocean University (2021). M.S. (2016), B.S. (2014). Advisor: Prof. Jae-Hoon Kim.
Current Affiliation
Inside Director, Computer Science & AI Center, MetaIntelligence Inc. (Jan 2024 — present).
Research Output
6 KCI journals · 1 SCOPUS journal · 7 conference papers (international/domestic) · 3 registered patents · 3× KIISE Best Paper Awards.
Philosophy
Research Philosophy
Principles that connect theory and application.
From Linguistics to Engineering
Translating linguistic intuition into robust models and algorithms is, in my view, the highest research skill. Meaningful NLP systems are born where theoretical rigor meets engineering soundness.
Prompt Engineering & AI Agents
My current focus: automated report generation and analysis powered by prompt engineering and AI agent workflows. Turning frontier LLM capabilities into reliable research tools.
Reproducibility First
I prioritize specifying corpora, evaluation metrics, and training procedures. Only reproducible research turns into follow-up work and industrial application.
Bridging Research and Industry
I pursue research that becomes something tangible — registered patents, deployed analysis tools, validated systems — not papers that gather dust. Field feedback is the starting point of the next study.
Latest Insights
View all posts »Notes on natural language processing, LLM prompt engineering, and ongoing research. Recent posts share what I'm working on and learning.