Welcome to DATA-UX Lab

Recruitment!!

The DATA-UX(Data-Driven User Experience) Lab
(advisor: Prof. Youngjung Suh) is recruiting postdocs, graduate students, and undergraduate students who are highly motivated and passionate.
If interested, send an email to Prof. Suh (youngjung.suh@kongju.ac.kr) with your CV..

Welcome to DATA-UX Lab

Recruitment!!

The DATA-UX(Data-Driven User Experience) Lab
(advisor: Prof. Youngjung Suh) is recruiting postdocs, graduate students, and undergraduate students who are highly motivated and passionate.
If interested, send an email to Prof. Suh (youngjung.suh@kongju.ac.kr) with your CV..

Advisor

Prof.
Youngjung Suh(서영정)
PH.D

More Information
Location

Department of Computer Science and Engineering at Kongju National University
1224-24 Cheonan-daero Seobuk-gu, Cheonan-si Chungcheongnam-do, South Korea

Contact
  • Office Engineering-08 805
  • Lab Engineering-08 708
  • Tel +82-41-521-9221
  • M youngjung.suh@kongju.ac.kr

Research Direction

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Creating new value through big data analysis and AI technologies

Identification

Identify socially, economically, and culturally significant issues requiring big data analysis and AI.

Analysis

Process and analyze data to derive actionable insights and optimal solutions.

Prediction

Predict and respond to future phenomena or trends based on pattern analysis.

Research Content

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Enhancing the usability of AI services by addressing technical challenges in data science

Labeling Technologies

Develop cost-efficient, fast, and accurate labeling methods tailored to analytical objectives.

Data Quality Quantification

Improve AI service quality by advancing data preprocessing techniques (e.g., data imputation).

Data Imbalance Solutions

Address imbalance through over/under-sampling and ML threshold calibration.

Feature Extraction

Develop methods for extracting features from heterogeneous and complex datasets, including LLM-based user characteristic modeling.

Domain-Specific Performance Metrics

Create inference performance metrics for ML models tailored to specific application domains.

Explainable AI

Enhance the interpretability of ML models using global model-agnostic and local explanation techniques.

LLM RAG

Overcome limitations of large language models and explore practical applications for retrieval-augmented generation.

Tentative Research Topics

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Cultural Value

Understanding User-Generated Content Across Social Media Platforms

  • o Predicting factors for the success of cultural content.
  • o Early trend detection: Identifying cultural trends via SNS and search data analysis.
Social Value

Addressing Social Issues Arising from Technological Advancement

  • o Detecting excessive or problematic use of technology and the internet.
  • o Predicting welfare demands for elderly populations.
Economic Value

Analyzing Industrial and Consumer Trends, and Enhancing Public Services

  • o Forecasting workforce demands by industry and analyzing consumer complaint patterns.
  • o Improving public service efficiency and quality using LLM RAG technologies.