Sangho Suh

200 University Avenue

Waterloo, ON, Canada

I am a postdoctoral researcher in the Design Lab and Creativity Lab at the University of California, San Diego. I earned my Ph.D. in Computer Science from the University of Waterloo where I was advised by Edith Law and was a member of the Human-Computer Interaction Lab.

With programming, data, and AI permeating every aspect of our lives and the tools we use, literacy in these fields is becoming a critical part of our training and education in the 21st century. My research aims to support this need by augmenting our intelligence in areas such as learning, creativity, and sensemaking. I am particularly interested in creating new techniques, tools, and systems that leverage dynamic, layered representations and computational methods/AI.

During my Ph.D., I investigated this in the context of teaching programming, exploring how we can learn and make sense of abstract concepts, languages, and procedures in programming with comics. Specifically, I introduced a new concept called coding strip, a form of comic strip with corresponding code, and developed tools for designing, creating, and using coding strips—design methods, creativity support tools, and visual programming environment.

Research Interests:
Human-Computer/AI Interaction, Computing Education, Creativity Support, Concept-driven/Collaborative Storytelling, Creative Coding, Learning Analytics

Previously:
I received B.S. in Computer Science from Korea University where I worked with Jaegul Choo to develop data mining algorithm and visual analytics for convolutional neural network.

For more details, check my cv.

highlights [archive]

Jun 30, 2022	“CodeToon: Authoring Tool for Creating Comics from Code with Story Ideation and Automatic Comic Generation” is conditionally accepted at UIST 2022!
Jun 13, 2022	Our DIS 2022 paper received Best Paper Honorable Mention Award!🎖
Jun 8, 2022	Taught Experiment Design at Toronto Human-AI Interaction Summer Research School
Apr 30, 2022	Accepted offer to join Design Lab @ UC San Diego as Postdoc!
Apr 29, 2022	@ CHI 2022 (New Orleans) until May 6 (Fri) - Let’s connect!
Apr 27, 2022	Talk @ Google - “Creating Opportunities to Move Up and Down the Ladder of Abstraction”: Generating Comics from Code with Story Ideation, Auto Comic Generation, and Generative AI

selected publications [full list]

DIS Best Paper
Honorable
Mention Award

PrivacyToon: Concept-driven Storytelling with Creativity Support for Privacy Concepts 🎖

Suh, Sangho, Lamorea, Sydney, Law, Edith, and Zhang-Kennedy, Leah

In ACM SIGCHI Conference on Designing Interactive Systems (DIS) 2022

Abs PDF Website

With privacy-related concepts often abstract and difficult to define, comics can be an effective visual storytelling medium for explaining and raising awareness about privacy. However, existing privacy and security educational comics do not support content creation. To address this, we contribute PrivacyToon, a comic-based authoring tool that leverages concept-driven storytelling and ideation cards to help users create customizable privacy-related visual content. Our exploratory user study with 23 students and teachers shows PrivacyToon’s potential as a creative tool for communicating privacy concepts and stories. Our results show that a wide range of creativity preferences and contexts must be considered when designing systems that integrate ideation card-based design processes.
IUI

Leveraging Generative Conversational AI to Develop a Creative Learning Environment for Computational Thinking

Suh, Sangho, and Pengcheng, An

In 27th Annual Conference on Intelligent User Interfaces (IUI) 2022

Abs PDF Talk

We explore how generative conversational AI can assist students’ learning, creative, and sensemaking process in a visual programming environment where users can create comics from code. The process of visualizing code in terms of comics involves mapping programming language (code) to natural language (story) and then to visual language (of comics). While this process requires users to brainstorm code examples, metaphors, and story ideas, the recent development in generative models introduces an exciting opportunity for learners to harness their creative superpower and researchers to advance our understanding of how generative conversational AI can augment our intelligence in creative learning contexts. We provide an overview of our system and discuss interaction scenarios to demonstrate ways we can partner with generative conversational AI in the context of learning computer programming.
SIGCSE

Using Comics to Introduce and Reinforce Programming Concepts in CS1

Suh, Sangho, Latulipe, Celine, Lee, Ken Jen, Cheng, Bernadette, and Law, Edith

In ACM Technical Symposium on Computer Science Education SIGCSE 2021

Abs arXiv PDF Slides Website Talk Blog

Recent work investigated the potential of comics to support the teaching and learning of programming concepts and suggested several ways coding strips, a form of comic strip with its corresponding code, can be used. Building on this work, we tested the recommended use cases of coding strip in an undergraduate introductory computer science course at a large comprehensive university. At the end of the course, we surveyed students to assess their experience and found they benefited in various ways. Our work contributes a demonstration of the various ways comics can be used in introductory CS courses and an initial understanding of benefits and challenges with using comics in computing education gleaned from an analysis of students’ survey responses and code submissions.
VL/HCC

Coding Strip: A Pedagogical Tool for Teaching and Learning Programming Concepts through Comics

Suh, Sangho, Lee, Martinet, Xia, Gracie, and Law, Edith

In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2020

Abs PDF Supp Slides Website Blog

The abstract nature of programming makes learning to code a daunting undertaking for many novice learners. In this work, we advocate the use of comics—a medium capable of presenting abstract ideas in a concrete, familiar way—for introducing programming concepts. Particularly, we propose a design process and related tools to help students and teachers create coding strips, a form of comic strips that are associated with a piece of code. We conducted two design workshops with students and high school computer science teachers to evaluate our design process and tools. We find that our design process and tools are effective at supporting the design of coding strips and that both students and teachers are excited about using coding strip as a tool for learning and teaching programming concepts.
CHI

Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents

Law, Edith, Baghaei Ravari, Parastoo, Chhibber, Nalin, Kulic, Dana, Lin, Stephanie, Pantasdo, Kevin D, Ceha, Jessy, Suh, Sangho, and Dillen, Nicole

In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems 2020

Abs PDF Blog

Learning by teaching is an established pedagogical technique; however, the exact process through which learning happens remains difficult to assess, in part due to the variability in the tutor-tutee pairing and interaction. Prior research proposed the use of teachable agents acting as students, in order to facilitate more controlled studies of the learning by teaching phenomenon. In this work, we introduce a learning by teaching platform, Curiosity Notebook, which allows students to work individually or in groups to teach a conversational agent a classification task in a variety of subject topics. We conducted a 4-week exploratory study with 12 fourth and fifth grade elementary school children, who taught a conversational robot how to classify animals, rocks/minerals and paintings. This paper outlines the architecture of our system, describes the lessons learned from the study, and contributes design considerations on how to design conversational agents and applications for learning by teaching scenarios.
NIPS

Re-VACNN: Steering Convolutional Neural Network via Real-time Visual Analytics

Chung, Sunghyo, Park, Cheonbok, Suh, Sangho, Kang, Kyeongpil, Choo, Jaegul, and Kwon, Bum Chul

In Future of interactive learning machines workshop at the 30th annual conference on neural information processing systems (NIPS) 2016

Abs PDF Poster Slides Talk Demo Blog

Recently, deep learning has become exceptionally popular due to its outstanding performances in various machine learning and artificial intelligence applications. Convolutional neural network (CNN), a representative model of deep learning, has been successfully applied to solve computationally burdening tasks in various applications like computer vision. Despite its outstanding capability, training a CNN model properly is time-consuming and prone to overfitting and/or bad local minima. To address these issues, this study aims at improving the interpretation of the training process and using it for subsequent human intervention, specifically steering the training process of CNN model in the real time. In this paper, we present ReVACNN, a real-time visual analytic system for CNN, where (1) the overall training process (e.g., the amount of activations and that of changes each filter/layer has at a particular iteration/epoch) is visualized in network view and (2) the 2D embedding of trained filters within layers is visualized to show the relationships between filters as well as layers. In particular, ReVACNN allows users to perform several interactions in real time: (1) skipping the gradient descent update on the sub-part of a CNN model to reduce the subsequent training time and (2) steering filters interactively in the 2D embedding view to avoid bad local minima. At the end, we present several use cases that demonstrate the benefits users can gain from ReVACNN.
ICDM Best Student
Paper Award

L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization 🏆

Suh, Sangho, Choo, Jaegul, Lee, Joonseok, and Reddy, Chandan K

In 2016 IEEE 16th International Conference on Data Mining (ICDM) 2016

Abs PDF Code Slides Talk Blog

Nonnegative matrix factorization (NMF) has been widely applied in many domains. In document analysis, it has been increasingly used in topic modeling applications, where a set of underlying topics are revealed by a low-rank factor matrix from NMF. However, it is often the case that the resulting topics give only general topic information in the data, which tends not to convey much information. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model, which has been successful in supervised learning, into an unsupervised topic modeling context. That is, our model successively performs NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. Our algorithm for updating the input matrix has novelty in two aspects. The first lies in utilizing the residual matrix inspired by a state-of-the-art gradient boosting model, and the second stems from applying a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users. We evaluate our proposed method by comparing it against other topic modeling methods, such as a few variants of NMF and latent Dirichlet allocation, in terms of various evaluation measures representing topic coherence, diversity, coverage, computing time, and so on. We also present qualitative evaluation on the topics discovered by our method using several real-world data sets.