Changhoon Kim

Bio

Changhoon Kim completed his Ph.D. in Computer Engineering at Arizona State University under the advisement of Professor Yezhou Yang. His primary research focuses on the creation of secure machine learning systems. He has dedicated his efforts to developing user-attribution methods for generative models, a critical area of research in the age of AI-generated hyper-realistic content for tracing malicious usage, and machine UNlearning for removing private or harmful content from AI models. Kim’s pioneering research has been recognized at prestigious conferences such as ICLR, ICML, ECCV, and CVPR. Additionally, he holds a U.S. patent for user-attribution in generative models. To further contribute to the community, he organizes tutorials and workshops at leading conferences to emphasize the importance of secure generative AI.

About Me

I have completed my Ph.D. in the School of Computing and Augmented Intelligence (SCAI), where I worked with Prof. Yezhou Yang.

Research Keywords

- Computer Vision

- Generative Models

- Attribution of Synthesized Data

- Robust and Reliability for Generative Models 

News

Nov. 6, 2024            TripletCLIP is accepted at NeurIPS.

Aug. 12, 2024        R.A.C.E. received an ORAL presentation slot at ECCV.

Jul. 14, 2024 As an organizer, our NeurIPS Workshop on Responsibly Building the Next Generation of Multimodal Foundation Models has been accepted.

Jul. 1, 2024 R.A.C.E. is accepted at ECCV.

Jun. 24, 2024 I have successfully defended my thesis.

Invited Talks

• ECCV Doctoral Consortium (Oct. 2024)

Topic: Robust Concept Erasure for T2I Models

• ASU Frontiers of GenAI (Sep. 2024)

Topic: Strengthening Image Generative AI: Integrating Fingerprinting and Revision Methods for Enhanced Safety and Control

• CVPR Doctoral Consortium (Jun. 2024)

Topic: Risk Management in Image Generative Models

• Samsung AI Center - Toronto (Apr. 2024)

Topic: Strengthening Image Generative AI

• AAAI Doctoral Consortium (Feb. 2024)

Topic: Risk Management in Image Generative Models through Model Fingerprinting

• 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

Work Experience

• Research Scientist (Summer Intern)

KRAFTON, South Korea, 2022 Summer

• Computer Vision Engineer

GEOMania Co., South Korea, 2011-2014