18 citations · 47 across the 14 of their papers we have counts for
15 papers
Treadstone: A Social-Media-Inspired Platform for Multi-Agent Collaborative Data Analysis
Hyunwook Lee, Sungbeom Cho, William Benjamin +6
Coordinating human analysts with autonomous AI agents faces the same challenges as human-to-human collaboration: sharing intermediate results, avoiding conflicts, and maintaining g…
How Do LLMs See Charts? A Comparative Study on High-Level Visualization Comprehension in Humans and LLMs
Hyotaek Jeon, Hyunwook Lee, Minjeong Shin +6
Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to extract complex and interconnected data patter…
VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions
Thu Phuong Nguyen, Duc M. Nguyen, Hyotaek Jeon +4
Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but it remains a significant challenge due…
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
Juwon Kim, Hyunwook Lee, Hyotaek Jeon +2
Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patter…
ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
Hyotaek Jeon, Hyunwook Lee, Juwon Kim +1
Traffic forecasting represents a crucial problem within intelligent transportation systems. In recent research, Large Language Models (LLMs) have emerged as a promising method, but…
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has…
Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data
Sungbok Shin, Sunghyo Chung, Hyeon Jeon +6
An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help pe…
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from fact…
DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs
Joohee Kim, Hyunwook Lee, Duc M. Nguyen +4
Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs -- graphs whose vertices and edges change over time. However, manually creatin…
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
Hyunwook Lee, Sungahn Ko
Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works main…
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee +3
Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification). However, even with the same scatterplot, the way…
TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
Hyunwook Lee, Chunggi Lee, Hongkyu Lim +1
Time-series forecasting has gained increasing attention in the field of artificial intelligence due to its potential to address real-world problems across various domains, includin…
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
Seungmin Jin, Hyunwook Lee, Cheonbok Park +4
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the t…
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
Hyunwook Lee, Seungmin Jin, Hyeshin Chu +2
Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve…
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
Hyunwook Lee, Cheonbok Park, Seungmin Jin +3
To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the propos…