Researcher profile / trustworthy AI and research systems

Hanyong Lee이한용

Building trustworthy NLP and autonomous research systems

I build language systems that remain useful under noisy, adversarial, and real-world conditions, and I care as much about disciplined execution as I do about model quality.

RolePh.D. Student in Artificial Intelligence

Chung-Ang University

LabCAU AutoML Lab

Seoul, Korea

Emailglhy0718@gmail.com Bloglhy0718.github.io Hugging Facelhy X@hanyong_lee_

Research focus

Trustworthy NLP
Autonomous Research Systems
LLM-based Applications
Experiment Governance
Robust Language Understanding

Mission

My work connects trustworthy NLP, LLM applications, and research infrastructure, with an emphasis on governed workflows, evidence-aware evaluation, and AI systems that can be inspected, resumed, and trusted.

Overview

Research

A concise view of the questions, environments, outputs, and deployment mindset shaping my work.

Current focus

Trustworthy NLP, autonomous research execution, and evidence-aware LLM systems

Research style

Turning research ideas into governed, inspectable systems instead of one-off demos

What I build

Research infrastructure, evaluation workflows, and real-world NLP applications

Based in

Seoul, Korea

Selected publications

Representative publications

Representative publications across phishing defense, adversarial robustness, dialogue systems, and practical language model development.

Findings of NAACL 2025

2025

BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks

H. Lee, C. Lee, Y. Lee, J. Lee

New Mexico, USA | April 29 - May 4, 2025

View publication

ICCE 2025

2025

GoodGPT: Counseling-chat

K. Kim, H. Lee, J. Lee

Las Vegas, USA | January 11 - 14, 2025

Conference record

ICCE 2024

2024

An Efficient Fine-Tuning of Generative Language Model for Aspect-Based Sentiment Analysis

C. Lee, H. Lee, K. Kim, S. Kim, J. Lee

Las Vegas, USA | January 5 - 8, 2024

Conference record

ICEIC 2023

2023

Exploitation of Character-Wise Language Model for Recovering Adversarial Text

H. Lee, J. Lee

Conference publication

Conference record

PlatCon 2021

2021

An Efficient Neural Network based on Early Compression of Sparse CT Slice Images

A. Moon, S. Lee, S. Cho, T. Lee, H. Lee, J. Lee

pp. 1-5 | doi: 10.1109/PlatCon53246.2021.9680749

Conference record

Featured Build

Project spotlight

A representative system that reflects how I think about AI research, tooling, and reliability.

Featured project

GitHub

AutoLabOS

An operating system for autonomous research

AutoLabOS is a research systems project for running literature-grounded, checkpointed, and inspectable workflows from paper collection to manuscript drafting. Instead of treating research as a single generation step, it structures the process as a governed loop with explicit review, evidence tracking, and resumable state.

TypeScriptNode.jsReactOpenAICodex CLISemantic Scholar

AutoLabOS

Fixed multi-stage workflow for research execution instead of open-ended agent drift

AutoLabOS

Checkpointed runs with inspectable artifacts and resumable progress

AutoLabOS

Evidence-bound claim review that limits conclusions to what the run actually supports

AutoLabOS

Terminal and web interfaces for operating autonomous research pipelines

Journey

Academic timeline

From computer science training to current doctoral research in AI.

2024.03 - Present
Ph.D. Course, Department of Artificial Intelligence
Chung-Ang University
2022.03 - 2024.02
M.Sc. Course, Department of Artificial Intelligence
Chung-Ang University
2021.09 - 2021.12
Intern
S2W Inc.
2015.03 - 2022.02
B.Sc. Course, School of Computer Science and Engineering
Chung-Ang University
2013.03 - 2015.02
Hansung Science High School
Science-focused secondary education

Impact

Awards and selected builds

Recognition and projects that connect academic research with deployable AI systems.

Awards

3rd Prize, 2022 AI Graduate School Challenge, LG
3rd Prize, 2021 Text Ethics Verification Data Hackathon Competition, National Information Society Agency (NIA)

2025 - Present

AutoLabOS

Designed and built an autonomous research system that organizes literature review, hypothesis generation, experiment planning, review gating, and manuscript drafting inside a governed execution loop.

Open-source project

2023.09 - 2024.12

Automatic Generation of Children's Song Lyrics and Improvement of Lyric Quality Based on Large Language Model

Research and development project focused on creative language generation and quality improvement with large language models.

2023.03 - 2024.12

Integrated Framework for Automatic Neural Network Generation and Deployment Optimized for Runtime Environments

Developed an end-to-end framework for generating and deploying neural networks across runtime-constrained environments.

In cooperation with ETRI

Capabilities

Tech stack

Tools I use to move from research ideas to governed experiments, prototypes, and production-facing systems.

Languages

PythonJavaCC++C#JavaScriptTypeScriptSwiftHTML5CSS3

AI / ML

PyTorchTensorFlowHugging FaceLangChainLangGraphOpenAIOpenAI AgentsSemantic Scholarscikit-learnPandasTensorFlow LiteONNXWeights and Biases

Web / App / Tools

Node.jsNext.jsReactVitestFastAPIFlaskDjangoUnityAndroid StudioOpenGLLinuxmacOSWindowsRaspberry PiVS CodeVim

Coverage

In the news

Selected media and university coverage related to my research and collaborations.

Chung-Ang University

NAACL 2025 paper acceptance news from Chung-Ang University

Open

NewsTheAI

Chung-Ang University featured in Safe AI coverage from Singapore

Open

GetNews

LG selected as organizer for the 2022 AI Graduate School Symposium

Open

ETNews

ETRI Invention Camp grand prize coverage

Open

Contact

Let's build something meaningful.

I'm especially interested in trustworthy NLP, autonomous research systems, and collaborations that turn strong ideas into reliable and inspectable AI products.

Emailglhy0718@gmail.com Bloglhy0718.github.io Hugging Facelhy X@hanyong_lee_

Hanyong Lee이한용

Research

Trustworthy NLP, autonomous research execution, and evidence-aware LLM systems

Turning research ideas into governed, inspectable systems instead of one-off demos

Research infrastructure, evaluation workflows, and real-world NLP applications

Seoul, Korea

Representative publications

BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks

GoodGPT: Counseling-chat

An Efficient Fine-Tuning of Generative Language Model for Aspect-Based Sentiment Analysis

Exploitation of Character-Wise Language Model for Recovering Adversarial Text

An Efficient Neural Network based on Early Compression of Sparse CT Slice Images

Project spotlight

AutoLabOS

Fixed multi-stage workflow for research execution instead of open-ended agent drift

Checkpointed runs with inspectable artifacts and resumable progress

Evidence-bound claim review that limits conclusions to what the run actually supports

Terminal and web interfaces for operating autonomous research pipelines

Academic timeline

Ph.D. Course, Department of Artificial Intelligence

M.Sc. Course, Department of Artificial Intelligence

Intern

B.Sc. Course, School of Computer Science and Engineering

Hansung Science High School

Awards and selected builds

AutoLabOS

Automatic Generation of Children's Song Lyrics and Improvement of Lyric Quality Based on Large Language Model

Integrated Framework for Automatic Neural Network Generation and Deployment Optimized for Runtime Environments

Tech stack

In the news

NAACL 2025 paper acceptance news from Chung-Ang University

Chung-Ang University featured in Safe AI coverage from Singapore

LG selected as organizer for the 2022 AI Graduate School Symposium

ETRI Invention Camp grand prize coverage

Let's build something meaningful.