Changhoon Kim
Ph.D. Candidate
Arizona State University
Tempe, Arizona
Email: kch AT asu DOT edu
Bio
Changhoon Kim is nearing the completion of his Ph.D. in Computer Engineering at Arizona State University, under the advisory of Professor Yezhou Yang. His primary research is centered on the creation of trustworthy and responsible machine learning systems. He has devoted recent years to the development of user-attribution methods for generative models—an indispensable area of research in the age of AI-generated hyper-realistic content. His research extends to various modalities, including image, audio, video, and multi-modal generative models. Recognition of Kim’s pioneering research comes from highly-regarded conferences such as ICLR, ICML, and CVPR . Further evidence of his innovative prowess is a U.S. patent he holds for user-attribution in generative models.
About Me
I am a last year Ph.D. Candidate in SCAI. I work with Prof. Yezhou Yang.
Research Keywords
- Computer Vision
- Generative Models
- Attribution of Synthesized Data
- Robust and Reliability for Generative Models
News
Apr. 12, 2024 Tutorial on "Responsibly Building Generative Models" is accepted at ECCV 2024
Mar. 29, 2024 Selected as a participant in CVPR 2024 Doctoral Consortium
Feb. 26, 2024 Two papers are accepted to the CVPR 2024 (WOUAF, ECLIPSE)
Feb. 24, 2024 Talk about Reliable Image Gen-AI at the AAAI Doctoral Consortium 2024
Jan. 12, 2024 Interview with AIhub is available at Link
Jan. 9, 2024 Presented "Tutorial on Reliability of Generative Models in Vision" WACV 2024 Tutorial
Publications & Patents
Changhoon Kim*, Kyle Min*, Maitreya Patel, Sheng Cheng, Yezhou Yang
CVPR 2024, NeurIPS 2023 Workshop, Demo
Maitreya Patel , Changhoon Kim, Sheng Cheng, Chitta Baral, Yezhou Yang
CVPR 2024
Guangyu Nie*, Changhoon Kim*, Yezhou Yang, Yi Ren
International Conference on Machine Learning (ICML 2023)
Patent
Changhoon Kim, Yi Ren, Yezhou Yang
US Patent App. 17/544,201
Yongbaek Cho*, Changhoon Kim*, Yezhou Yang, Yi Ren
International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2022)
Changhoon Kim*, Yi Ren*, Yezhou Yang
International Conference on Learning Representations (ICLR 2021)
Changhoon Kim, Mengyuan Zhang, Chongjie Zhang, Xi Jessie Yang
Human Factors and Engineering Society (HFES 2018)
Invited Talks
Interview with AIhub (Jan. 2024)
Topic: Enhancing the reliability of image generative AI
Mayo Clinic (July 2023)
Topic: Generative Model's Risk Management in Medical Domain
Intel Labs (June 2023)
Topic: Text-to-Image Generative Model's Risk Management with User Attribution
Korea University (July 2022)
Intelligent Architecture & Systems Research lab.
Topic: Decentralized Attribution of Generative Models
Honor
CVPR 2024 Doctoral Consortium
AAAI 2024 Doctoral Consortium
ASU Graduate College Completion Fellowship Spring'24
ASU GPA Group Travel Funding for NeurIPS 2023
ASU GPA Individual Travel Funding for ICML 2023
Service
Organizer
Tutorial on Responsibly Building Generative Models ECCV 2024 Tutorial
Tutorial on Reliability of Generative Models in Vision WACV 2024 Tutorial
Reviewer
Various ML Conferences
Work Experience
Research Scientist (Summer Intern)
KRAFTON, South Korea, 2022 Summer
Computer Vision Engineer
GEOMania Co., South Korea, 2011-2014
Teaching Experience
Graduate Research Associate (2019 May - )
Graduate Teaching Associate
CSE 110 - Principles of Programming (2018 Fall, 2019 Spring)
CSE 100 - Principles of Programming (2019 Spring)
Collaborators
I am fortunate to work with these great people: Kyle Min (Intel Labs.), Maitreya Patel (ASU), Sheng Cheng (ASU)