Dae Hyun Kim

schoolStanford Universitybar_chart#644 Global Rank

Total Papers

HCI Rank

#644

Research Areas

Years Active

Recent Publications

Understanding Spatiotemporal-Aware Multimodal Conversational Search in the Outdoor Urban Space

Emerging multimodal conversational search (MCS) tools (e.g., Gemini Live) allow users to search for spatiotemporal information through natural language dialogues as they move through urban space. Despite the growing popularity of these tools, there is limited understanding of how people engage with this technology. To…

CHI '265th+ AuthorHuman-LLM Collaboration +2

Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring

LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge, such as dataset schemas, domain conventions, and task-specific requirements, that isn't explicitly provided. This results in frequently er…

CHI '265th+ AuthorGenerative AI (Text, Image, Music, Video) +3

DiaryPlay: AI-Assisted Creation of Interactive Story Vignettes for Everyday Storytelling

An interactive vignette is a visual storytelling medium that lets the audience role-play a character and interact with non-player characters (NPCs) and the digital environment. Yet, the authoring complexity of interactive vignettes has obstructed their adoption in everyday storytelling, which builds on immediacy. We i…

CHI '251st AuthorHuman-LLM Collaboration +1

PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models

In client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration a…

DIS '241st AuthorHuman-LLM Collaboration +1

AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients

Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborat…

DIS '243rd AuthorAR Navigation & Context Awareness +3

A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models

One common asset of metaverse is that users can freely explore places and actions without linear procedures. Thus, it is hard yet important to understand the divergent challenges each user faces when onboarding metaverse. Our formative study (N = 16) shows that first-time users ask questions about metaverse that conce…

CHI '244th AuthorHuman-LLM Collaboration +2

Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic dive…

IUI '244th AuthorInteractive Data Visualization +2

DataDive: Supporting Readers' Contextualization of Statistical Statements with Data Exploration

Statistical statements that refer to data to support narratives or claims are commonly used to inform readers about the magnitude of social issues. While contextualizing statistical statements with relevant data supports readers in building their own interpretation of statements, the complexity of finding contextual…

CHI '211st AuthorInteractive Data Visualization +1

Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts

Charts often contain visually prominent features that draw attention to aspects of the data and include text captions that emphasize aspects of the data. Through a crowdsourced study, we explore how readers gather takeaways when considering charts and captions together. We first ask participants to mark visually promi…

UIST '203rd AuthorInteractive Data Visualization +1

Sneak Pique: Exploring Autocompletion as a Data Discovery Scaffold for Supporting Visual Analysis

Natural language interaction has evolved as a useful modality to help users explore and interact with their data during visual analysis. Little work has been done to explore how autocompletion can help with data discovery while helping users formulate analytical questions. We developed a system called Sneak Pique as a…

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