HCII Ph.D. Thesis Proposal: Hyeonsu Kang (Remote)
When
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Description
Complex sensemaking, whether for scientists, designers, or lawyers, involves dis-
covering diverse domains and iteratively curating their core structures to gain new
insights. Typical bottom-up processes involving collecting, synthesizing, and inte-
grating examples are cognitively demanding and require frequent context-switching
between different tools and stages. Bootstrapping explorations with existing synthe-
sis (e.g., extracting research threads described in scientific papers, re-mixing mood-
boards on a theme, re-using core argument structures from legal cases) or gener-
ative artificial intelligence (GenAI) can reduce some costs but may introduce new
interaction and cognitive challenges. Furthermore, users are often left alone when
leveraging their curated data for creative insights.
My research aims to address this gap by developing systems and interaction tech-
niques that facilitate users’ bootstrapping from existing synthesis and GenAI, while
enhancing downstream discovery using user-curated data. I focus on two significant
domains: scientific research (completed work) and industrial design (proposed work).
In the first part of the proposal, I explore how users can curate a specific form of
‘research threads’ (i.e., sentences describing and citing specific groups of prior re-
search) while reading an individual paper, and ‘expert committees’ (i.e., a group of
authors representing a particular perspective on a topical area) to make sense of
the knowledge landscape and discover diverse and relevant results. I propose two
approaches that each centers threads and committees as first-class objects in the in-
teraction design. These approaches leverage citation graphs, AI-based retrieval, and
GenAI-based summarization to enable broader exploration beyond relevant research
examined in the source papers. The objects also act as boundary objects, translating
users’ intents to AI during exploration and augmenting the discovery process. They
help users to find other significant papers, identify high-level themes that emerge
from them, and generate relevance explanations for retrieved results that enhance
users’ engagement.
In the second part of the proposal, I introduce a simple schema that abstracts users’
synthesis during scientific research ideation. Using this schema, I develop an ana-
logical search engine for scientists to retrieve analogical papers addressing similar
high-level challenges as the user query despite differences in low-level specifics, and
demonstrate that they trigger creative adaptation ideas.
In the proposed work, I plan to extend this paradigm to the industrial design domain,
which involves designers’ continuous explorations to gain design inspirations and
emphasizes processing of information from the visual modality during exploration.