Hi, I'm an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University, where I lead the Data Interaction and Visualization (DIV) Lab.
My research focuses on building novel visual analytics tools for people to easily explore, interpret, and interact with machine learning systems and large datasets. To do this, I combine data-driven scalable techniques and human-centered interactive approaches, by using methods in data visualization, Explainable AI, human-computer interaction (HCI), and databases.
I earned a PhD in computer science at Georgia Tech with a Dissertation Award. I was fortunate to work with amazing people at Google and Facebook, resulting in deployed and open-sourced systems. My research has been supported by NSF, DARPA, Google, and Facebook.
Research InterestsMy students and I are currently working on research in the following topics:
- Visual analytics for explaining errors in AI systems
- Intelligent user interfaces for exploring large video data
- Interactive analysis of bias and unfairness in AI
- Iterative ML development and debugging by interacting with datasets
July 2021Two papers accepted for short presentations at IEEE VIS'21.
- Contrastive Identification of Covariate Shift in Image Data
- "Why did my AI agent lose?": Visual Analytics for Scaling Up AAR/AI
- Apr 2021 Honored to receive the 2021 Georgia Tech College of Computing Dissertation Award.
- Mar 2021 Serving as a Program Committee member for IEEE VIS'21.
- Mar 2021 Our DARPA Explainable (XAI) grant has been extended for another year.
- Dec 2020 Our research will be supported by the NSF IUCRC PPI Center.
- Sept 2020 New paper on evaluting GAN Lab, accepted for IEEE VIS'20.
- Aug 2020 New paper, CNN Explainer, accepted for IEEE VIS'20 (TVCG track).
- Feb 2020 Our Chronodes paper nominated for the 2018 ACM TiiS Best Paper.
- July 2019 New paper, FairVis, on visual analysis of ML bias accepted to IEEE VIS'19.
Ph.D. in Computer Science,
Georgia Institute of Technology, USA
Thesis: Human-Centered AI through Scalable Visual Data Analytics
Committee: Polo Chau, Sham Navathe, Alex Endert, Martin Wattenberg, Fernanda Viégas
M.S. in Computer Science and Engineering,
Seoul National University, South Korea
Thesis: Context-Aware Recommendation using Learning-to-Rank
- B.S. in Electrical and Computer Engineering, Seoul National University, South Korea 2005-2009
Industry Research Experience
- Google, Software Engineering Intern at Google Brain's People+AI Research Group Summer 2017
- Facebook, Research Intern at Applied ML Research Group Summer 2016
- Facebook, Research Intern at Applied ML Research Group Summer 2015
- 2021 College of Computing Dissertation Award, Georgia Tech 2021
- ACM Trans. Interactive Intelligent Systems (TiiS) 2018 Best Paper, Honorable Mention 2020
- Google PhD Fellowship, Google AI 2018-2019
- Graduate TA of the Year in School of Computer Science, Georgia Tech Apr 2018
- NSF Graduate Research Fellowship, National Science Foundation 2014-2017
- Best Paper Award, PhD Workshop at CIKM Oct 2011
- National Scholarship for Science and Engineering, Korea Student Aid Foundation 2005-2009
Publications (Latest & Greatest) Full list (h-index: 18)
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
IEEE Transactions on Visualization and Computer Graphics, 26(2) (VIS'20), 2021.
DOI PDF Demo Video Code
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
IEEE Conference on Visual Analytics Science and Technology (VIS'19), 2019.
DOI PDF arXiv Blog Code
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
IEEE Transactions on Visualization and Computer Graphics, 25(1) (VIS'18), 2019.
Open sourced with Google AI
DOI PDF Slides Code Website
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
IEEE Transactions on Visualization and Computer Graphics, 25(8), 2019.
DOI PDF Website Medium
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
IEEE Transactions on Visualization and Computer Graphics, 24(1) (VIS'17), 2018.
Deployed on Facebook ML Platform; Invited to present at SIGGRAPH'18 as a top VIS paper (4 total)
DOI PDF Video Slides Website
Chronodes: Interactive Multifocus Exploration of Event Sequences
ACM Transactions on Interactive Intelligent Systems (TiiS), 8(1), 2018.
Best Paper, Honorable Mention
Interactive Browsing and Navigation in Relational Databases
Proceedings of the VLDB Endowment, 9(12) (VLDB'16), 2016.
DOI PDF Slides
Visual Exploration of Machine Learning Results using Data Cube Analysis
Workshop on Human-In-the-Loop Data Analytics (HILDA at SIGMOD'16), 2016.
Deployed on Facebook ML Platform
DOI PDF Slides
CS 499/599. Special Topics: Visual Analytics
This new course introduces "visual analytics" which combine automated analysis techniques with interactive visualizations for understanding, reasoning, and decision making on the basis of large and complex data. Students will learn how to design and build interactive visual interfaces for humans to easily and effectively explore data and discover insights. This course is open to both undergraduate and graudate students.
CS 565. Human-Computer Interaction
Spring 2022, Spring 2021, and Spring 2020
This course provides students with basic principles of and research methods in Human-Computer Interaction (HCI). Students will learn how to design and prototype user interfaces and interactive systems, based on the needs of users, and how to evaluate such interfaces and systems rigorously.
CS 539. Selected Topics in AI: Data Visualization for ML
This course introduces advanced state-of-the-art research on interactive data visualization for machine learning. Students will learn how to design and develop interactive data visualization methods and tools that are interpretable to complex ML models (e.g., deep learning models), scalable to large data, and usable to a variety of users (e.g., ML researchers, practitioners like ML engineers and data scientists, non-expert learners).
- Montaser Hamid, CS PhD, Spring 2021 - present Advisor
- Delyar Tabatabai, CS MS, Winter 2021 - present
CS MS, Spring 2020 - Spring 2021 (Graduated)
Thesis: Assessing and Finding Faults in AI: Two Empirical Studies
- Dayeon Oh, CS MS, Spring 2020 - present
- Kin-Ho Lam, CS MS (Advisor: Alan Fern), Fall 2020 - present Co-advisor
- Matthew Olson, CS MS/PhD (Advisor: Weng-Keen Wong), Winter 2020 - present Collaborator
- Donny Bertucci, CS, Winter 2020 - present
- Mark Ser, CS, Fall 2020 - Spring 2021 (OSU STEM Leader Program)
- Kristina Lee, CS, Fall 2020 - Winter 2021
- Anita Ruangrotsakun, CS BS/MS (AMP), Summer 2020 - present
- Thuy-Vy Nguyen, CS, Summer 2020 - Spring 2021 (Graduated; Now at Oracle)
- Junhyeok Jeong, CS, Winter 2020 - Fall 2020