My research interests are in computer vision and machine learning, with a focus on developing ideas that are both academically grounded and impactful in practice. I lead a young and dynamic research team at Universiti Malaya, with more than 100 publications in leading peer-reviewed conferences and journals, including CVPR and NeurIPS. I also had the privilege of serving as the founding Chair of the IEEE Computational Intelligence Society Malaysia Chapter in 2015.
I currently serve as an Associate Editor of Pattern Recognition and IEEE Transactions on Circuits and Systems for Video Technology, and have co-organized numerous conferences, workshops, tutorials, and challenges in computer vision and machine learning. Over the years, my work has been recognized through several honors, including the Malaysia Scopus Research Excellence Award (Research Innovations) in 2024, Top Research Scientists Malaysia (TRSM) in 2022, membership in the Young Scientists Network–Academy of Sciences Malaysia (YSN-ASM) in 2015, and the Hitachi Research Fellowship in 2013. I am also a Senior Member of IEEE and a Professional Engineer (BEM).
Between 2020 and 2022, I was seconded to the Ministry of Science, Technology and Innovation (MOSTI) as Undersecretary for the Division of Data Strategic and Foresight, where I contributed to national efforts in data strategy and future-oriented planning. I remain committed to nurturing the next generation of researchers, supporting Malaysian talent to excel globally, and advancing a responsible and inclusive AI ecosystem that reflects our nation’s values and aspirations.
Highlights:
04/2026: One(1) paper to appear in ACL-2026 (main, long paper)
02/2026: One(1) paper to appear in CVPR-2026 (main)
01/2026: One(1) paper to appear in ICLR-2026.
professor at Universiti Malaya
Latest Works
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis Star
Y. Xia, Z. Pan, A. Kamsin and C.S. Chan
ACL 2026 (main, long paper, acceptance rate: 2308/12,148 ~ 19%)
This paper proposes a single-pass, depth-selective reading framework for multi-aspect sentiment analysis, designed to reduce computational redundancy by separating shared sentence encoding from aspect-specific querying. Our work managed to reduce end-to-end computation by up to 60% in multi-aspect settings (M > 2).
pdf (coming soon) codeOneHOI: Unifying Human-Object Interaction Generation and Editing Star
J.T. Hoe, W. Hu, X. Jiang, Y-P. Tan and C.S. Chan
CVPR 2026 (main, acceptance rate: 4090/16,092 ~ 25.42%)
Rather than treating generation and editing as separate problems, this paper - OneHOI models both as a unified conditional denoising process grounded in structured ⟨person, action, object⟩ relations, enabling (i) Single-interaction generation; (ii) Identity-preserving editing, and (iii) Multi-HOI editing. All within one coherent framework.
pdf Project Page code HOI-Edit-44K DatasetTowards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure Star
H. Gu, H.X. Tae, L. Fan and C.S. Chan
ICLR 2026 (acceptance rate: 5339/18,949 ~ 28.18%)
We address label unlearning in Vertical Federated Learning (VFL), where labels are essential for training yet also privacy-sensitive. Our approach uses representation-level manifold mixup to generate synthetic embeddings for both forgotten and retained samples, enabling more effective and efficient gradient-based forgetting while preserving model performance
pdf codeGorgeous: Creating narrative-driven makeup ideas via image prompts Star
J.W. Sii and C.S. Chan
Multimedia Tools and Applications (2025)
Gorgeous is a diffusion-based makeup generator that turns any image prompt (like fire or moonlight) into creative, narrative-driven makeup on a face, rather than simply copying an existing style. From inspiration to personalized beauty in seconds to unlock scalable, AI-powered creativity for brands, artists, and consumers.
pdf codeMaverick: Collaboration-free Federated Unlearning for Medical Privacy Star
W.K. Ong and C.S. Chan
MICCAI 2025 (oral, acceptance rate: 76/3447 ~ 2.2%)
We propose Maverick, the first Collaboration-free Federated Unlearning framework, enabling unlearning based on a single client’s request for medical applications. That is, as soon as a client requests data removal, our method can act independently, eliminating the need for global collaboration from other clients
pdf poster slide code