Assist. Prof. Dr. Libo Huang | Deep Learning | Best Researcher Award
Assist. Prof. Dr. Libo Huang | Deep Learning – Assistant Researcher at Institute of Computing Technology, Chinese Academy of Sciences, China
Dr. Libo Huang is a dedicated and rapidly emerging research scientist specializing in machine learning, with a focus on continual, incremental, and lifelong learning. Currently serving as an Assistant Researcher at the Institute of Computing Technology, Chinese Academy of Sciences, he is actively contributing to the frontier of intelligent learning systems. Known for blending theoretical insight with practical innovation, Dr. Huang has become a key contributor in the areas of neural systems, generative modeling, and knowledge distillation. He plays an instrumental role in several national and provincial-level AI initiatives in China and internationally. With multiple cross-border academic experiences, he brings a global perspective to advancing adaptive intelligence for real-world challenges.
Academic Profile
Google Scholar | ORCID
Education
Dr. Huang’s academic journey began with a Bachelor’s degree in Information and Computational Mathematics, which he earned from Jiangxi Normal University. Building on this quantitative foundation, he completed his Master’s degree in Pattern Recognition from Guangdong University of Technology, where he delved deeper into data-driven pattern systems under expert mentorship. Pursuing excellence in machine learning, he obtained his Ph.D. at Guangdong University of Technology between 2018 and 2021, focusing on neural spike sorting and adaptive learning algorithms. Notably, he also undertook a prestigious joint Ph.D. program at Brunel University London (2019–2020), working on spike clustering techniques using sparse and low-rank representation frameworks, demonstrating his commitment to multidisciplinary and international research development.
Professional Experience
Dr. Huang joined the Institute of Computing Technology in July 2021 and has since led multiple key projects related to deep continual learning. He is currently working under the direction of Prof. Yongjun Xu, contributing significantly to practical machine learning deployments and system-level AI innovations. In addition to his postdoctoral and researcher responsibilities, he has served as project leader on government-funded programs, including a national project on causal structure models (2024–2025) and a youth fund project supported by the Beijing Natural Science Foundation. Dr. Huang’s work is well-regarded for bridging gaps between foundational research and deployable AI systems. His past experience also includes participation in a national joint training Ph.D. program, enhancing his cross-institutional capabilities.
Research Interests
His primary research interests revolve around continual learning, deep generative models, lifelong learning algorithms, and knowledge distillation. He has extensively investigated the problem of class-incremental learning and developed techniques to improve model plasticity and stability trade-offs. Dr. Huang also explores the design of efficient AI systems using low-rank and sparse representation methods, spike sorting frameworks for neurodata, and causal reinforcement learning. His current focus includes integrating feedback-driven reconstruction techniques and embedding distillation approaches to support dynamic learning environments for both supervised and unsupervised tasks. His interdisciplinary lens enables him to create robust models applicable to healthcare, robotics, and intelligent sensing.
Awards and Recognition
Dr. Huang has received multiple recognitions, including funding awards from both the National Natural Science Foundation and Beijing Municipal Science entities. Notable among these is the 2024–2025 National Project Grant on Causal Structures and the 2023–2025 national-level project on explainable knowledge-driven task planning. He was also selected under the 2019 National Joint Training Ph.D. Program for High-Level Universities. In terms of peer recognition, he holds membership in the IEEE, the Chinese Association for Artificial Intelligence, and the Chinese Computer Society. His active participation in elite academic circles positions him as a strong candidate for the Best Researcher Award.
Selected Publications
🔬 WMsorting: Wavelet packets’ decomposition and mutual information-based spike sorting method, IEEE TNB, 2019 – cited by over 80 articles; focuses on neural spike classification.
🧠 A unified optimization model of feature extraction and clustering for spike sorting, IEEE TNSRE, 2021 – cited in various neuroinformatics works.
🧪 KFC: Knowledge Reconstruction and Feedback Consolidation for Continual Generative Learning, ICLR Tiny Papers Track, 2024 – praised for improving memory retention in generative tasks.
🤖 eTag: Class-Incremental Learning with Embedding Distillation, AAAI, 2024 – addresses scalable AI learning, cited in domain adaptation literature.
🎯 Automatical Spike Sorting with Low-Rank and Sparse Representation, IEEE TBME, 2023 – improved processing accuracy, cited by biomedical systems papers.
🔄 Continual Learning in the Frequency Domain, NeurIPS, 2024 – explored spectral representations in lifelong learning models.
🎥 CLIP-KD: An Empirical Study of Distilling CLIP Models, CVPR, 2024 – applied to visual-language pretraining, influential in vision-language learning.
Conclusion
Dr. Libo Huang is a rising star in machine learning research with a solid blend of theoretical innovation and application-driven impact. His consistent publication record in IEEE and AAAI venues, project leadership in lifelong learning and AI causality, and collaborative work with international researchers reflect his promise as a future research leader. As AI continues to shape scientific and industrial landscapes, Dr. Huang’s work contributes to sustainable, adaptive, and interpretable intelligent systems. His active engagement in academic services, reviewer duties, and project mentorship makes him an ideal and deserving candidate for the Best Researcher Award.