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MINSUK KAHNG

 * Home
 * CV (PDF)
 * Bio
 * Publications
 * Teaching

 * minsuk.kahng@oregonstate.edu
 * KEC 3043
 * minsuk.com
 * Google Scholar
 * Twitter

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 humans to
easily explore, interpret, and interact with machine learning systems and large
datasets. In doing this, I combine human-centered interactive approaches and
data-driven scalable techniques, by using methods in data visualization,
Explainable AI, human-computer interaction (HCI), and databases.

I earned a PhD in computer science from Georgia Tech with a Dissertation Award.
My work led to deployed technologies by Facebook (e.g., ActiVis) and an
open-sourced tool developed with Google (e.g., GAN Lab). My research has been
supported by NSF, DARPA, Google, and many.


RESEARCH INTERESTS

My students and I are currently working on research in the following topics:
 * Visual analytics for explaining errors in AI systems
 * Iterative ML development and debugging by interacting with datasets
 * Visual, interactive analysis of data bias and (un)fairness in AI
 * Intelligent user interfaces for exploring large multimodal and video data


NEWS

 * Dec 2021 Thanks NAVER AI Lab for their gift to support our research
   collaboration!
 * Nov 2021 New paper on video adaptation collaborated with KAIST conditionally
   accepted for CHI'22.
 * Oct 2021 New paper on explaining image classifiers by OSU XAI team accepted
   for NeurIPS'21.
 * July 2021
   Two 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
 * July 2021 Excited to be part of AgAID, a new NSF/USDA AI Institute ($20M
   funded).
 * June 2021 CNN Explainer is invited to present at ACM SIGGRAPH'21.
 * 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) provided OSU with $600K for AAR/AI
   research.


RESEARCH HIGHLIGHTS

ActiVis
Visual Exploration of Industry-Scale ML Models
Collaborated with Facebook. Presented at IEEE VIS'17
DOI PDF Video Website
GAN Lab
Playing with Generative Models in Browser
Collaborated with Google Brain's PAIR. Presented at IEEE VIS'18
DOI PDF Code Website
FairVis
Discovering Intersectional Bias in ML Results
Open-sourced; Presented at VIS'19
DOI PDF Blog Website
CNN Explainer
Interactive Education for Deep Learning
Presented at VIS'20 and SIGGRAPH'21
DOI PDF Website
AAR/AI
Workflow for Finding Fault's in RL Agents
Featured in DARPA XAI Workshop
Video VIS'21 TiiS paper
ETable
Interactive Navigation in Relational Databases
Presented at VLDB'16
DOI PDF Slides


EDUCATION

 * Ph.D. in Computer Science, Georgia Institute of Technology, USA 2013-2019
   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 2009-2011
   Thesis: Context-Aware Recommendation using Learning-to-Rank (Advisor:
   Sang-goo Lee)
 * 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


AWARDS (SELECTED)

 * 2021 College of Computing Dissertation Award, Georgia Tech 2021
 * Finalist, Facebook Research Award 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 2018
 * NSF Graduate Research Fellowship, National Science Foundation 2014-2017
 * Best Paper Award, PhD Workshop at CIKM 2011
 * National Scholarship for Science and Engineering, Korea Student Aid
   Foundation 2005-2009


PUBLICATIONS (LATEST & GREATEST) (H-INDEX: 19)

 * FitVid: Responsive and Flexible Video Content Adaptation.
   Jeongyeon Kim, Yubin Choi, Minsuk Kahng, Juho Kim
   ACM CHI Conference on Human Factors in Computing Systems (CHI'22), 2022.
   (Accepted with Minor Revision)
 * Finding AI's Faults with AAR/AI: An Empirical Study.
   Roli Khanna, Jonathan Dodge, Andrew Anderson, Rupika Dikkala, Jed Irvine,
   Zeyad Shureih, Kin-Ho Lam, Caleb R. Matthews, Zhengxian Lin, Minsuk Kahng,
   Alan Fern, Margaret Burnett
   ACM Transactions on Interactive Intelligent Systems (TiiS), 2021.
   PDF
 * One Explanation is Not Enough: Structured Attention Graphs for Image
   Classification.
   Vivswan Shitole, Li Fuxin, Minsuk Kahng, Prasad Tadepalli, Alan Fern
   35th Conference on Neural Information Processing Systems (NeurIPS'21), 2021.
   PDF arXiv
 * Contrastive Identification of Covariate Shift in Image Data
   Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit, Neale Ratzlaff, Weng-Keen
   Wong, Minsuk Kahng
   IEEE Visualization Conference (VIS'21), 2021.
   DOI PDF
 * 
   "Why did my AI agent lose?": Visual Analytics for Scaling Up After-Action
   Review
   Delyar Tabatabai, Anita Ruangrotsakun, Jed Irvine, Jonathan Dodge, Zeyad
   Shureih, Kin-Ho Lam, Margaret Burnett, Alan Fern, Minsuk Kahng
   IEEE Visualization Conference (VIS'21), 2021.
   DOI PDF
 * 
   CNN Explainer: Learning Convolutional Neural Networks with Interactive
   Visualization
   Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred
   Hohman, Minsuk Kahng, Duen Horng (Polo) Chau
   IEEE Transactions on Visualization and Computer Graphics, 26(2) (VIS'20),
   2021.
   DOI PDF Demo Video Code
 * How Does Visualization Help People Learn Deep Learning? Evaluation of GAN Lab
   with Observational Study and Log Analysis
   Minsuk Kahng, Duen Horng (Polo) Chau
   IEEE Visualization Conference (VIS'20), 2020.
   PDF
 * 
   FairVis: Visual Analytics for Discovering Intersectional Bias in Machine
   Learning
   Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie
   Morgenstern, Duen Horng (Polo) Chau
   IEEE Conference on Visual Analytics Science and Technology (VIS'19), 2019.
   DOI PDF arXiv Blog Code
   Featured in the Data Stories podcast (link)
 * 
   GAN Lab: Understanding Complex Deep Generative Models using Interactive
   Visual Experimentation
   Minsuk Kahng, Nikhil Thorat, Duen Horng (Polo) Chau, Fernanda Viégas, Martin
   Wattenberg
   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
   Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng (Polo) Chau
   IEEE Transactions on Visualization and Computer Graphics, 25(8), 2019.
   DOI PDF Website Medium
   Most cited paper among all papers published in the journal in 2017-2021
   (link)
 * 
   ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
   Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, Duen Horng (Polo) Chau
   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
   DOI PDF Video Slides Website
 * Chronodes: Interactive Multifocus Exploration of Event Sequences
   Peter J. Polack Jr., Shang-Tse Chen, Minsuk Kahng, Kaya De Barbaro, Rahul
   Basole, Moushumi Sharmin, Duen Horng (Polo) Chau
   ACM Transactions on Interactive Intelligent Systems (TiiS), 8(1), 2018.
   Best Paper, Honorable Mention
   DOI PDF
 * FACETS: Adaptive Local Exploration of Large Graphs
   Robert Pienta, Minsuk Kahng, Zhiyuan Lin, Jilles Vreeken, Partha Talukdar,
   James Abello, Ganesh Parameswaran, Duen Horng (Polo) Chau
   SIAM International Conference on Data Mining (SDM'17), 2017.
   DOI PDF
 * 
   Interactive Browsing and Navigation in Relational Databases
   Minsuk Kahng, Shamkant B. Navathe, John T. Stasko, Duen Horng (Polo) Chau
   Proceedings of the VLDB Endowment, 9(12) (VLDB'16), 2016.
   DOI PDF Slides
 * 
   Visual Exploration of Machine Learning Results using Data Cube Analysis
   Minsuk Kahng, Dezhi Fang, Duen Horng (Polo) Chau
   Workshop on Human-In-the-Loop Data Analytics (HILDA at SIGMOD'16), 2016.
   Deployed on Facebook ML Platform
   DOI PDF Slides
 * Understanding Variations in Pediatric Asthma Care Processes in the Emergency
   Department using Visual Analytics
   Rahul C. Basole, Mark Braunstein, Vikas Kumar, Hyunwoo Park, Minsuk Kahng,
   Duen Horng (Polo) Chau, Acar Tamersoy, Daniel A. Hirsh, Nicoleta Serban,
   James Bost, Burton Lesnick, Beth L. Schissel, Michael Thompson
   Journal of the American Medical Informatics Association, 22(2) (Special Issue
   on Visual Analytics in Healthcare), 2015.
   DOI
 * 
   GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive
   eXploration
   Charles D. Stolper, Minsuk Kahng, Zhiyuan Lin, Florian Foerster, Aakash Goel,
   John Stasko, and Duen Horng (Polo) Chau
   IEEE Transactions on Visualization and Computer Graphics, 20(12) (InfoVis as
   part of VIS'14), 2014.
   DOI PDF Video
 * PathRank: A Novel Node Ranking Measure on a Heterogeneous Graph for
   Recommender Systems
   Sangkeun Lee, Sungchan Park, Minsuk Kahng, Sang-goo Lee
   ACM Conference on Information and Knowledge Management (CIKM'12), 2012.
   DOI
 * Exploiting Paths for Entity Search in RDF Graphs
   Minsuk Kahng, Sang-goo Lee
   ACM SIGIR Conference on Research and Development in Information Retrieval
   (SIGIR'12 Poster), 2012.
   DOI PDF Poster
 * Ranking Objects by Following Paths in Entity-Relationship Graphs
   Minsuk Kahng, Sangkeun Lee, Sang-goo Lee
   ACM Workshop for Ph.D. Students in Information and Knowledge Management
   (Ph.D. Workshop at CIKM'11), 2011.
   Best Paper Award
   DOI PDF Slides
 * Ranking in Context-Aware Recommender Systems
   Minsuk Kahng, Sangkeun Lee, Sang-goo Lee
   International Conference on World Wide Web (WWW'11 Poster), 2011.
   DOI PDF Poster
 * Random Walk based Entity Ranking on Graph for Multidimensional Recommendation
   Sangkeun Lee, Sang-il Song, Minsuk Kahng, Dongjoo Lee, Sang-goo Lee
   ACM Conference on Recommender Systems (RecSys'11), 2011.
   DOI PDF

Full list of papers


TEACHING

 * CS 499/549. Visual Analytics (Special Topic; Selected Topic in Data Science &
   Systems)
   Winter 2022
   This new course introduces "visual analytics", a field of study on combining
   interactive data visualization with automated analysis techniques for
   understanding, reasoning, and decision making on the basis of large and
   complex data. Students will learn how to design and build interactive data
   visualization interfaces for human users to effectively explore and analyze
   data and discover insights. There is no required pre-requisite, but web
   development experience (CS 290) is strongly recommended, because the
   programming assignments and group project include developing web-based data
   visualization interfaces using JavaScript and HTML/CSS.
 * 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. Data Visualization for ML (Selected Topic in AI)
   Fall 2020
   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).


STUDENT ADVISING

GRADUATE STUDENTS

 * Eric Slyman, CS/AI PhD (co-advised by Stefan Lee), Fall 2021 - present
 * Yashwanthi Anand, CS MS/PhD, Fall 2021 - present
 * Montaser Hamid, CS PhD, Spring 2021 - present
 * Delyar Tabatabai, CS MS, Winter 2021 - present
 * Kin-Ho Lam, CS MS (co-advised with Alan Fern), Fall 2020 - present
 * Dayeon Oh, CS MS, Spring 2020 - present
 * Roli Khanna, CS MS, Spring 2020 - Spring 2021 (Graduated; Now at Microsoft)
   Thesis: Assessing and Finding Faults in AI: Two Empirical Studies

UNDERGRADUATE STUDENTS

 * Donny Bertucci, CS, Winter 2020 - present
 * Anita Ruangrotsakun, CS BS/MS (AMP), Summer 2020 - present
 * Mark Ser, CS, Fall 2020 - Spring 2021 (OSU STEM Leader Program)
 * Kristina Lee, CS, Fall 2020 - Winter 2021
 * Thuy-Vy Nguyen, CS, Summer 2020 - Spring 2021 (Graduated; Now at Oracle)
 * Junhyeok "Derek" Jeong, CS, Winter 2020 - Fall 2020


GRANTS & FUNDING

 * NSF National AI Research Institute Grant & Gifts
   Title: USDA-NIFA Institute for Agricultural AI for Transforming Workforce and
   Decision Support
   Senior Personnel (Lead PI: Ananth Kalyanaraman)
   Total Amount: $20,000,000 (OSU: $6,500,000) (Period: 2021-26)
 * DARPA Explainable Artificial Intelligence (XAI)
   Title: xACT: Explanation-Informed Acceptance Testing of Deep Adaptive
   Programs
   Co-PI (Lead PI: Alan Fern)
   Total Amount: $6,500,000 + $600,000 (Period: 2017-21, 2021-22)
 * NAVER AI Lab
   Unrestricted Gift
   Amount: $100,000 for Year 1, 2021
 * NSF Industry-University Collaboration Research Center on Pervasive
   Personalized Intelligence
   Project: Visual Analytics for Scalable AI Debugging
   Project PI (Site PI: Weng-Keen Wong): Collaboration with NEC and Intel
   Total Amount for OSU for Year 1: $64,000 (Period: 2021-present)
 * Google Cloud Research Credit Credit
   Amount: $5,000, 2021
 * Google PhD Fellowship Fellowship
   Full Tuition + $35,000 for 2 years, 2018-2019
 * NSF Graduate Research Fellowship
   Full Tuition + $34,000 for 3 years, 2014-2017


PROFESSIONAL SERVICE

WORKSHOP CO-ORGANIZER

 * KDD 2018 Workshop on Interactive Data Exploration and Analytics (IDEA 2018)

JOURNAL CO-EDITOR

 * ACM Transactions on Interactive Intelligent Systems (TiiS), Special Issue
   Highlights of IUI 2019

CONFERENCE ORGANIZING COMMITTEE: WEBMASTER AND WEB DESIGNER

 * WSDM 2016

CONFERENCE PROGRAM COMMITTEE

 * IUI 2019-22
 * AAAI 2021–22
 * IEEE VIS 2021
 * IEEE VIS (Short Papers) 2020
 * SDM 2020
 * CIKM (Demo) 2019
 * IUI (Poster and Demo) 2019

WORKSHOP PROGRAM COMMITTEE

 * Workshop on Visualization Meets AI (at PacificVis 2020-21)
 * Symposium on Visualization in Data Science (at VIS 2018–19)
 * Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific
   Discovery (at BigData 2019)
 * Workshop on Visualization for AI Explainability (at VIS 2018)
 * KDD Workshop on Interactive Data Exploration and Analytics (IDEA 2016–17)
 * Workshop on Visual Analytics for Deep Learning (at VIS 2017)

JOURNAL REVIEWER

 * IEEE Transactions on Visualization and Computer Graphics (TVCG) (2019,
   2021-22)
 * ACM Transactions on Interactive Intelligent Systems (TiiS) (2020)
 * ACM Transactions on Intelligent Systems and Technology (TIST) (2020)
 * Distill (2019)
 * ACM Transactions on Computer-Human Interaction (TOCHI) (2015, 2018)
 * Expert Systems with Applications (2015)

CONFERENCE REVIEWER

 * CHI 2014, 2017–19, 2021-22
 * CSCW 2020
 * VIS 2018–20
 * EuroVis 2018
 * SDM 2014, 2016–17
 * KDD 2014–16
 * IUI 2016
 * RecSys 2016
 * SIGMOD 2013
 * DASFAA 2011

(c) 2022 Minsuk Kahng