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Minsuk Chang

I'm a Computer Science PhD student in the KIXLAB at KAIST advised by Juho Kim. Here's my CV (pdf).

My research in HCI focuses on techniques for discovering, capturing, and structuring user context in large-scale web data to create novel learning opportunities in the wild. Read more (seprate page)

Latest News and Travels
Feb 2019 : RecipeScape featured in KAIST School of Computing's research highlight!
Feb 2019 : Our work on subgoal labeling as feedback intervention has been accepted for CHI 19 LBW!
Jan 2019 : Will be spending the summer at Microsoft AI + Research @ Redmond. Super excited!
More News Dec 2018 : Voice interfaces for video tutorials I worked on during the summer @ Adobe Research has been conditionally accepted to CHI 2019. See you @ Glasgow!
Nov 2018 : Started winter internship at Autodesk Research @ Toronto!
Oct 14-19 2018 : Traveling to UIST 18 @ Berlin, Germany. SV'ing again!
Aug 22 2018 : Visiting University of British Columbia to give a talk on voice+video interfaces.
Jun 2018 : Started research internship at Adobe Research @ Seattle! Seattle people, let's meet!.
Apr 2018 : at CHI 2018 in Montreal, QC, Canada! Presenting RecipeScape on thursday morning.
Apr 2018 : Will present RecipeScape at SIGCHI Korea Local Chapter 2018 Spring Academic Workshop
Mar 2018 : Invited to CHI 2018 Sensemaking Workshop.
Mar 2018 : Excited to spend the summer at Adobe Research @ Seattle!
Feb 2018 : Presented a poster on RecipeScape at HCI@KAIST Winter Workshop. Received best poster award!
Dec 2017 : Our RecipeScape paper is conditionally accepted to CHI 2018!
Oct 22 - 24, 2017 : Student Volunteering for UIST 2017 @ Quebec City, QC, Canada
May 06 - 11, 2017 : Student volunteering for CHI 2017 @ Denver, CO, USA
Feb 2017 Our LBW paper on large scale recipe mining as been accepted to CHI 2017!
Mar 2016 Started my journey in HCI research with Juho!
Less News
Conference Papers (Selected)
CHI 2019
How to Design Voice Based Navigation for How-To Videos
When watching how-to videos related to physical tasks, users’ hands are often occupied by the task, making voice input a natural fit. To better understand the design space of voice interactions for how-to video navigation, we conducted three think-aloud studies using: 1) a traditional video interface, 2) a research probe providing a voice controlled video interface, and 3) a wizard-of-oz interface. Read More From the studies, we distill seven navigation objectives and their underlying intents: pace control pause, content alignment pause, video control pause, reference jump, replay jump, skip jump, and peek jump. Our analysis found that users’ navigation objectives and intents affect the choice of referent type and referencing approach in command utterances. Based on our findings, we recommend to 1) support conversational strategies like sequence expansions and command queues, 2) allow users to identify and refine their navigation objectives explicitly, and 3) support the seven interaction intents. Read Less
CHI 2018
RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale
Minsuk Chang, Leonore V. Guillain, Hyeungshik Jung, Vivian M. Hare, Juho Kim, Maneesh Agrawala
For cooking professionals and culinary students, understanding cooking instructions is an essential yet demanding task. Common tasks include categorizing different approaches to cooking a dish and identifying usage patterns of particular ingredients or cooking methods, all of which require extensive browsing and comparison. However, no existing system provides support for such in-depth and at-scale analysis. Read More We present RecipeScape, an interactive system for browsing and analyzing the hundreds of recipes of a single dish available online. We also introduce a computational pipeline that extracts cooking processes from recipe text and calculates a procedural similarity between them. To evaluate how RecipeScape supports culinary analysis at scale, we conducted a user study with cooking professionals and culinary students with 500 recipes for two different dishes. Results show that RecipeScape clusters recipes into distinct approaches, and captures notable usage patterns of ingredients and cooking actions. Read Less
Posters, Demos, Workshop Papers
CHI 2019 LBW
SolveDeep: A System for Supporting Subgoal Learning in Online Math Problem Solving
Hyoungwook Jin, Minsuk Chang, Juho Kim
Learner-driven subgoal labeling helps learners form a hierarchical structure of solutions with subgoals, which are conceptual units of procedural problem solving. While learning with such hierarchical structure of a solution in mind is effective in learning problem solving strategies, the development of an interactive feedback system to support subgoal labeling tasks at scale requires significant expert efforts, making learner-driven subgoal labeling difficult to be applied in online learning environments. We propose SolveDeep, a system that provides feedback on learner solutions with peer-generated subgoals. Read More SolveDeep utilizes a learnersourcing workflow to generate the hierarchical representation of possible solutions, and uses a graph-alignment algorithm to generate a solution graph by merging the populated solution structures, which are then used to generate feedback on future learners' solutions. We conducted a user study with 7 participants to evaluate the efficacy of our system. Participants did subgoal learning with two math problems and rated the usefulness of system feedback. The average rating was 4.86 out of 7 (1: Not useful, 7: Useful), and the system could successfully construct a hierarchical structure of solutions with learnersourced subgoal labels. Read Less
CHI 2018 Sensemaking Workshop
Sensemaking around How-to-cook Videos
Minsuk Chang, Seayeon Lee, Kyungje Jo, Juho Kim
We conducted a series of exploratory studies on sensemaking behaviors people exhibit while watching how-to-cook videos. The three different scenarios we examined are a) when people seek for alternatives in ingredients, tools and actions, b) when people seek for explanations or more detail on certain instructions, and c) when people use text search and when people use video when learning how to cook a dish. Read More We found a) people often make arbitrary decisions on substituting ingredients, cooking tools, or cooking actions while following instructions, b) people satisfice by verifying knowledge with little data and not wanting to deviate from the initially chosen video, and c) people use text search for definitions and confirmation of substitutions while they use video search for explanations and precise details for instruction steps Read Less
CHI 2017 LBW
RecipeScape: Mining and Analyzing Diverse Processes in Cooking Recipes
Minsuk Chang, Vivian M. Hare, Juho Kim, Maneesh Agrawala
In culture analytics, it is important to ask fundamental questions that address salient characteristics of collective human behavior. This paper explores how analyzing cooking recipes in aggregate and at scale identifies these characteristics in the cooking culture, and answer fundamental questions like ”what makes a chocolate chip cookie a chocolate chip cookie?”. Read More Aspiring cooks, professional chefs and cooking hobbyists share their recipes online resulting in thousands of different procedural instructions towards a shared goal. However, existing approaches focus merely on analysis at the ingredient level, for example, extracting ingredient information from individual recipes. We introduce RecipeScape, a prototype interface which supports visually querying, browsing and comparing cooking recipes at scale. We also present the underlying computational pipeline of RecipeScape that scrapes recipes online, extracts their ingredient and instruction information, constructs a graphical representation, and computes similarity between pairs of recipes. Read Less
Services
Program Committee
CHI 2019 LBW
Reviewer
CHI 2017, 2018, 2019
CSCW 2018
UIST 2017, 2018
MobileHCI 2019
Student Volunteer
CHI 2017
UIST 2017, 2018
In the past,

I studied Computer Science, Financial Engineering from KAIST, Finance from Simon Business School @ University of Rochester, and Statistics from Rutgers University. I have worked at an Hedge Fund in NYC trying to beat the market by relentlessly crunching numbers prior to coming (back) to KAIST. I've spent two years in the reinforcement learning (as a subfield of machine learning) research group at KAIST as a Ph.D student before joining KIXLAB (the KAIST Interaction Lab).

I taught lab sessions for the mandatory Introduction to Programming course at KAIST from 2015-2018 as a Head TA. I enjoyed working with 40 TAs and interacting with 450-500 students each semester.