Liang LAN 蘭亮 Senior Lecturer
Ph.D., Temple University
(852) 3411-8008
lanliang@hkbu.edu.hk
CVA ROOM 919

Dr. Liang Lan is a senior lecturer in the Department of Communication Studies, School of Communication, Hong Kong Baptist University. He received his Ph.D. degree in Computer and Information Sciences from Temple University, Philadelphia, USA in 2012. He was an assistant professor in the department of computer science at Hong Kong Baptist University from 2018 to 2021.
His research interests include Artificial Intelligence (AI), machine learning and their applications in social science, media and communications. His work has been published in the top AI journals and conferences, such as AIJ, JMLR, TNNLS, ICML, AISTATS and AAAI. He is the recipient of the Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2015 and 2016 and the ASEAN Outstanding Engineering Achievements Award 2016.
Research InterestsArtificial Intelligence
Machine Learning
AI in Media and Communications
Computational Fact Checking
PublicationsLan, W., Lan, L., Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters. in Proceeding of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), pages 8235-8242, 2021.
Lei, Z., Lan, L., Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients. in Proceeding of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), pages 8316-8323, 2021.
Lei, Z., Lan, L., Improved Subsampled Randomized Hadamard Transform for Linear SVM. In Proceeding of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), pages 4519-4526, 2020.
Lan, L., Geng, Y., Accurate and interpretable factorization machines. In Proceeding of the Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, pages 4139–4146, 2019.
Lan, L., Wang Z., Zhe S., Cheng W., Wang J., and Zhang K., Scaling up kernel SVM on limited resources: A low-rank linearization approach. IEEE Transactions Neural Networks and Learning Systems, 30(2):369–378, 2019.
Lan, L., Zhang, K., Ge, H., Cheng, W., Zhang, J., Liu, J., Rauber, A., Li, X., Wang, J., Zha, H., Low-rank Decomposition Meets Kernel Learning: A Generalized Nystrom Method, Artificial Intelligence Vol. 250, pp. 1–15, 2017.
Djuric, N., Lan, L., Vucetic, S., Wang, Z., BudgetedSVM: A Toolbox for Large-Scale Non-linear
SVM, Journal of Machine Learning Research, 14, 3813-3817, 2013.
Zhang, K., Lan, L., Liu, J., Rauber, A., Moerchen, F., Inductive Kernel Low-rank Decomposition with Priors, in Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML), 2012.
Zhang, K., Lan, L., Wang, Z., Moerchen, F. Scaling up Kernel SVM on Limited Resources: a Low-rank Linearization Approach, Int. Conf. on Artificial Intelligence and Statistics (AISTATS), JMLR W&CP 22: 1425-1434, 2012.
Awards/Grants/HonorsAwards:
ASEAN Outstanding Engineering Achievements Award 2016
Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2016, Singapore
Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2015, Singapore
Research Grants:
2021: Interpretable Machine Learning Models for Fake News Detection and Intervention, AI-Info Communication Study (AIS) Scheme, The School of Communication, Hong Kong Baptist University, 2021/07 – 2022/12 (PI)
2019: Towards Improving the Scalability of Kernel Support Vector Machine via Random Projection, NSFC Young Scientist Fund, 2020/01~2022/12 (PI)