Long-Kai Huang 

        Email: HLONGKAI at gmail dot com


I am a senior researcher in AI for Science Center, Tencent AI Lab. Prior to that, I obtained my Ph.D. in SCSE at NTU, advised by Prof. Sinno Jialin Pan.  I received B.Eng. from Sun Yat-Sen University and did my undergraduate thesis with Prof. Wei-Shi Zheng.

My research focuses on foundational theory and applications of meta-learning, continual learning, and efficient pre-training and fine-tuning of LLMs. I am also interested in their applications to AI for science, particularly in drug discovery and protein design.


Recent Publications  |  Full Publications (Google Scholar) 

# indicates corresponding authors. * indicates equal contributions. Underlined authors are research interns mentored by me.     


Steering Protein Language Models

Long-Kai Huang#, Rongyi Zhu, Bing He, Jianhua Yao

International Conference on Machine Learning (ICML), 2025

[Model Editing][AI4Sci]

IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck

Tian Bian, Yifan Niu, Chaohao Yuan, Chengzhi Piao, Bingzhe Wu, Long-Kai Huang, Yu Rong, Tingyang Xu, Hong Cheng, Jia Li

International Conference on Machine Learning (ICML), 2025

[LLMs][Explainable AI]

SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning

Yichen Wu*, Hongming Piao*,  , Long-Kai Huang#, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng,

Kede Ma#, Ying Wei#

International Conference on Learning Representations (ICLR) 2025 (Oral) 

[Continual Learning]

Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization

Zhanfeng Mo, Long-Kai Huang, Sinno Jialin Pan

International Conference on Learning Representations (ICLR) 2025

[LLMs][Efficient Learning]

Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Gen-

eration

Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong

International Conference on Learning Representations (ICLR) 2025

[AI4Sci]

Learning Where to Edit Vision Transformers

Yunqiao Yang, Long-Kai Huang#, Shengzhuang Chen, Kede Ma, Ying Wei#

Advances in Neural Information Processing Systems (NeurIPS) 2024
[Model Editing][Meta Learning]

Towards Understanding Evolving Patterns in Sequential Data

Qiuhao Zeng, Long-Kai Huang, Xi Chen, Charles Ling, Boyu Wang

Advances in Neural Information Processing Systems (NeurIPS) 2024 (Spotlight

[Sequential Learning]

Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations Via Pareto Optimization

Yichen Wu*, Hong Wang*, Peilin Zhao, Yefeng Zheng, Ying Wei#, Long-Kai Huang#

International Conference on Machine Learning (ICML), 2024

[Continual Learning][Optimization Theory]

Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction [paper]

Yichen Wu, Long-Kai Huang#, Renzhen Wang, Deyu Meng, Ying Wei#

International Conference on Learning Representations (ICLR) 2024 (Oral, Outstanding Paper Award Honorable Mention

[Continual Learning][Meta Learning][Optimization Theory]

Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time [paper]

Qiuhao Zeng, Changjian Shui, Long-Kai Huang, Peng Liu, Xi Chen, Charles Ling, Boyu Wang

International Conference on Learning Representations (ICLR) 2024  (Oral

[Sequential Learning]

  Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling

Fang Wang*, Fan Yang*, Long-Kai Huang, Wei Li, Jiangning Song, Robin B Gasser, Ruedi Aebersold, Guohua Wang, Jianhua Yao

Nature Machine Intelligence 2023

[Transfer Learning][AI4Sci]

Retaining Beneficial Information from Detrimental Data for Neural Network Repair [paper]

Long-Kai Huang, Peilin Zhao, Junzhou Huang, Sinno Jialin Pan

Advances in Neural Information Processing Systems (NeurIPS) 2023

[Model Editing][Transfer Learning]

Secure Out-of-Distribution Task Generalization with Energy-Based Models [paper]

Shengzhuang Chen, Long-Kai Huang, Jonathan Richard Schwarz, Yilun Du, Ying Wei

Advances in Neural Information Processing Systems (NeurIPS) 2023

[Meta Learning]

Concept-wise Fine-tuning Matters in Preventing Negative Transfer [paper]

Yunqiao Yang, Long-Kai Huang, Ying Wei

International Conference on Computer Vision (ICCV) 2023

[Transfer Learning]

Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Shuang Zhou, Xiao Huang, Ninghao Liu, Huachi Zhou, Fu-Lai Chung, Long-Kai Huang#

IEEE Transactions on Knowledge and Data Engineering (TKDE) 2023

[Meta Learning]

  DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery–a Focus on Affinity Prediction Problems with Noise Annotations

Yuanfeng Ji, Lu Zhang [and 15 others, including Long-Kai Huang]

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2023 

[AI4Sci]

Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization [paper] [code]

Long-Kai Huang, Ying Wei

Advances in Neural Information Processing Systems (NeurIPS) 2022

[Meta Learning]

Adversarial Task Up-sampling for Meta-learning [paper]

Yichen Wu, Long-Kai Huang#, Ying Wei#

Advances in Neural Information Processing Systems (NeurIPS) 2022

[Meta Learning]

Frustratingly Easy Transferability Estimation [paper] [code]

Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei

International Conference on Machine Learning (ICML), 2022

[Transfer Learning]

    Improving Generalization in Meta-learning via Task Augmentation [paper] [code]

Huaxiu Yao*, Long-Kai Huang*, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li

International Conference on Machine Learning (ICML), 2021

[Meta Learning][AI4Sci][Optimization Theory]

   Communication-Efficient Distributed PCA by Riemannian Optimization [paper]

Long-Kai Huang and Sinno Jialin Pan

International Conference on Machine Learning (ICML), 2020

[Optimization Theory]

Accelerate Learning of Deep Hashing With Gradient Attention

Long-Kai Huang, Jianda Chen, Sinno Jialin Pan

International Conference on Computer Vision (ICCV), 2019
[Meta Learning]