My name is Hyeonsu Kang and I am a PhD student at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University. I am very fortunate to be advised by Dr. Niki Kittur.

My research interests are in human-computer interaction and AI, with an emphasis on building future interactive systems that augment human creativity and support learning in complex knowledge domains. Previously I have explored this challenge in various topics and domains such as scientific creativity, open innovation contests, peer feedback for visual designs, and programming. My approach to addressing the challenge involved developing various mechanisms including: computationally retrieving items that have the potential to analogically inspire the human mind to break out of fixation; matching teams that may bring together complementary strengths and transfer of ideas; an interactive interface that encourages users to browse useful examples and supplement their written feedback by choosing examples that concretely demonstrate the core feedback points; a programming environment that provides two very different modes of representation at the same time, thereby allowing users to doubly encode the information and learn more effectively; and novel interaction techniques for lowering the cognitive cost associated with information foraging and synthesis during sensemaking.

Prior to CMU I was at MIT, UC San Diego (The Design Lab), and Seoul National University. I have been fortunate to intern at Allen Institute for AI (Allen AI) and Tableau Software. I am excited to return to Allen AI as a research intern for Summer 2022!

I was named a Google Cloud Research Innovator and was previously supported by the South Korean National Scholarship for Science and Engineering.

My curriculum vitae can be found here.

I am available via email, [my first name]k@andrew.cmu.edu


Journal Papers

Hyeonsu B. Kang, Xin Qian, Tom Hope, Dafna Shahaf, Joel Chan, and Aniket Kittur
TOCHI 2022
Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific papers continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific papers and evaluate its effectiveness with scientists' own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.

Refereed Conference Proceedings Papers

Hyeonsu B. Kang, Rafal Kocielnik, Andrew Head, Jiangjiang Yang, Matt Latzke, Aniket Kittur, Daniel Weld, Doug Downey, and Jonathan Bragg
CHI 2022
Finding and engaging with the relevant scientific knowledge is foundational for intellectual progress in a society. Yet, with an exponential growth in publication rates, this becomes a challenging task. While personalized recommendations can help, they still may lack explanations of how certain papers are relevant and thus should be prioritized or attended to. To combat this, we developed a citation-based and two kinds of social relation-based approaches to boost user engagement with scholarly paper recommendations. For users who opted in, these approaches augmented paper recommendations included in email alerts with textual relevance descriptions underneath the recommendations. We evaluated our approaches in a randomized field experiment that ran for over two months and with 7,000+ users, and also in a controlled lab study (N=14) for deeper qualitative insights. We report on our findings and implications for the design of future approaches that aim to augment scholarly recommendations.
Tom Hope, Ronen Tamari, Hyeonsu Kang, Daniel Hershcovich, Joel Chan, Aniket Kittur, and Dafna Shahaf
CHI 2022 
We explore a novel representation for automatically breaking up product ideas described in natural language into fine-grained functional aspects. This representation can capture the core purposes and mechanisms in ideas, and support the backbone interactions (e.g., functional search of ideas, mapping and exploration of the design space around a focal problem) for augmenting human intelligence and accelerating the rate of innovation.
Hyeonsu B. Kang, Gabriel Amoako, Neil Sengupta, Steven Dow
CHI 2018
“A picture is worth a thousand words.” We developed Paragon, a system that supports crowdworkers and peers during feedback exchange by enabling search of design examples that supplement the written feedback. In two lab studies, we found that i) feedback providers select poster examples that complement their feedback and align with a provided rubric and that ii) feedback providers give significantly more specific, actionable, and novel input when using an example-centric approach, as opposed to text alone.
Hyeonsu Kang, Philip Guo
UIST 2017
We developed Omnicode, a programming environment with an always-on run-time visualization that helps novice programmers directly see how the variables and their relations change in real-time, in response to the changes they make in the program code. In our lab study, we found Omnicode to be useful for debugging, forming proper mental models, explaining their code to others, and discovering moments of serendipity that would not have been likely within an ordinary IDE.

Lightly Refereed Workshop Papers

Hyeonsu B. Kang*, Sheshera Mysore*, Kevin Huang*, Haw-Shiuan Chang, Thorben Prein, Andrew McCallum, Aniket Kittur, Elsa Olivetti
NAACL 2022 HCI + NLP Workshop
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas outside such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can 'zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.
Matching Open Innovation Projects for Analogical Feedback Exchange
Hyeonsu Kang, Felicia Ng, Aniket Kittur
Collective Intelligence 2019
We developed an algorithm for matching teams in open innovation contests that tackle related conservataion challenges using diverse approaches, thereby encouraging the transfer of analogical inspirations between teams. To this end, our algorithm used pre-trained language models to encode the natural language text descriptions of team challenges and their solution approaches into a vector similarity space, then computed semantic similarity between them to systematically find teams tackling similar problems using diverse approaches, shown as a conducive mechanism for the transfer.
Custom Blocks in StarLogo Nova: A Template-Based Approach to Abstraction for Improved Ease of Use and Expressive Power
Hyeonsu Kang, David Wu, David Wendel
We developed a general extension to the StarLogo Nova language to support end-user programming in various disciplines such as evolutionary biology, physics, and ecosystem sciences. This extension allowed end-users to select blocks that correspond to low-level programming constructs such as looping and variable assignment statements, and group them to create abstraction blocks that hide the low-level implementation details that oft-times distract learners from disciplinary learning objectives and system-level conceptual understanding. Using such abstraction blocks can also reduce the complexity of the programming language itself and lower the barrier to entry for novice learners.