Hi, I am Hongduan Tian, a third-year Ph.D. student at Trustworthy Machine Learning and Reasoning (TMLR) Group in Department of Computer Science, Hong Kong Baptist University, advised by Dr. Bo Han and Dr. Feng Liu. Before that, I got my master degree from Nanjing University of Information Science and Technology (NUIST) and fortunately supervised by Prof. Xiao-Tong Yuan and Prof. Qingshan Liu.

Previously, my research topics mainly focused on few-shot/meta learning, cross-domain generalization. Currently, my research interests mainly focus on trustworthy efficient post-training/fine-tuning/inference of foundation models and foundation model agents. Moreover, I am also interested in learning dynamics of foundation models (see topics of Yi Ren) and model knowledge transfer (such as model merging etc.).

Please feel free to email me for research, collaborations, or a casual chat.

📣 News

$\frak{2025.11}$: Our paper “Cross-domain Few-shot Classification via Invariant-content Feature Reconstruction” is accepted by IJCV.
$\frak{2024.09}$: Our paper “Mind the gap between prototypes and images in cross-domain finetuning” is accepted by NeurIPS 2024.
$\frak{2024.05}$: Our paper “MOKD: Cross-domain finetuning for few-shot classification via maximizing optimized kernel dependence” is accepted by ICML 2024.

📖 Educations

2023.09 - present, Hong Kong Baptist University (HKBU), Ph.D. in Computer Science.
2018.09 – 2021.06, Nanjing University of Information Science and Technology (NUIST), M.E. in Control Engineering.
2014.09 - 2018.06, Nanjing University of Information Science and Technology (NUIST), B.E. in Automation.

📝 Publications on Few-shot / Meta Learning

✉️ Corresponding author.

Cross-domain Few-shot Classification via Invariant-content Feature Reconstruction.

Hongduan Tian, Feng Liu, Ka Chun Cheung, Zhen Fang, Simon See, Tongliang Liu, Bo Han✉️.

Mind the Gap Between Prototypes and Images in Cross-domain Finetuning.

Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han✉️.

[paper] [code] [slides] [poster] [CN-video] [EN-video]

MOKD: Cross-domain Finetuning via Maximizing Optimized Kernel Dependence.

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han✉️.

[paper] [code] [slides] [poster] [CN-video] [EN-video]

Meta-learning with network pruning.
Hongduan Tian✉️, Bo Liu, Xiao-Tong Yuan✉️, Qingshan Liu.

[paper] [code]

🎖 Awards

2024.11, Research Performance Award, HKBU CS Department.
2024.10, NeurIPS Scholar Award.

🎤 Invited Talks

2024.11, Mind the Gap Between Prototypes and Images in Cross-domain Finetuning @AI TIME, Online. [Video]
2024.06, MOKD: Cross-domain finetuning for few-shot classification via maximizing optimized kernel dependence @AI TIME, Online. [Video]

💻 GitHub Repositories

💻 Services

Conference Reviewer for ICML, NeurIPS, ICLR, AISTATS.
Journal Reviewer for TPAMI, TNNLS, TMLR, NEUNET.

🏫 Teaching

COMP7015 (G) Artificial Intelligence, Sem. 1, 2025 - 2026
COMP7250 (G) Machine Learning, Sem. 2, 2024 - 2025
COMP7180 (G) Quantitative Methods for Data Analytics and Artificial Intelligence, Sem. 1, 2024 - 2025
COMP7940 (G) Cloud Computing, Sem. 2, 2023 - 2024

📖 Academic Experiences

2023.09 - present, PhD student @HKBU-TMLR Group, advised by Dr. Bo Han.
2022.07 - 2023.05, Research intern @HKBU-TMLR Group, advised by Dr. Bo Han and Dr. Feng Liu.

🏢 Industrial Experiences

2022.07 - Present, Research Intern @NVIDIA NVAITC, host by Charles Cheung.
2024.11 - Now, Remote Research Intern @WeChat, supervised by Xiangyu Zhu and Rolan Yan
2024.06 - 2024.08, Research Intern @WeChat, host by Xiangyu Zhu and Rolan Yan
2023.07 - 2023.08, Research Intern @Alibaba.
2021.07 - 2022.07, Algorithm Engineer @ZTE Nanjing Research and Development Center.