Liang Lan

Assistant Professor
Department of Computer Science
Hong Kong Baptist University

DLB644, Level 6, David C. Lam Building,
Shaw Campus, Hong Kong Baptist University, Kowloon Tong Kowloon, Hong Kong

Tel: +852 3411 5818
Email: lanliang (at)



Ph.D., Computer and Information Science, Temple University, Philadelphia, USA, 2007~2012 (advisor: Dr.Slobodan Vucetic).

B.E., Bioinformatics, Huazhong University of Science and Technology, Wuhan, China, 2003~2007.


Research Interests

Machine Learning, Deep Neural Networks, Data Mining, Big Data Analytics Applications (Health Informatics, Bioinformatics, Banking/Finance)



(03/2018 ~ present) Assistant Professor, Department of Computer Science, Hong Kong Baptist University, Hong Kong.

(07/2016 ~ 02/2018) Advisory Researcher and Senior Researcher, Lenovo Machine Intelligence Research Center, Hong Kong.

(05/2014 ~ 06/2016) Scientist I and Scientist II, Institute for Infocomm Research, Singapore.

(06/2013 ~ 04/2014) Researcher, Huawei Noah's Ark Lab, Hong Kong.

(05/2012 ~ 01/2013) Research Scientist Intern, Siemens Corporate Research, Princeton.

(05/2011 ~ 08/2011) Research Scientist Intern, Siemens Corporate Research, Princeton.

(09/2007 ~ 12/2012) Research Assistant, Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia.

(09/2009 ~ 05/2012) Teaching Assistant for various courses at the Computer and Information Sciences Department, Temple University, Philadelphia.

(07/2006 ~ 09/2006) Intern, Beijing Institute of Genomics, Beijing, China.


Group Members

Current Students

Past Students


  1. Lan, 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), 2021, Accepted.
  2. 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), 2021, Accepted.
  3. Zhang, X, Lan, L., Chan, C. P., Zhong, L.D., Cheng, C. W., Lam, W. C., Tian, R., Zhao, C., Wu, T. X., Shang H., C.,, Lyu, A. P., and Bian, Z. X., WHO Trial Registration Data Set (TRDS) extension for traditional Chinese medicine 2020: recommendations, explanation, and elaboration. BMC Med Res Methodol 20, 192 (2020).
  4. Lei, Z., Lan, L., Improved Subsampled Randomized Hadamard Transform for Linear SVM. in Proceeding of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
  5. Lan, L., Geng, Y., Accurate and Interpretable Factorization Machines. in Proceeding of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019. [pdf]
  6. 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 on Neural Networks and Learning Systems, Vol. 30, no. 2, pp. 369-378, Feb. 2019. [pdf]
  7. 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. [pdf]
  8. Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F., Promises and Challenges of Big Data Computing in Health Sciences, Big Data Research 2(1):2-11, 2015. [pdf]
  9. Zhang, K., Lan, L., Kwok, T.J., Vucetic, S., Parvim, B., Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines, IEEE Transactions on Neural Networks and Learning Systems, Vol. 26 (3), pp. 444-457, 2015. [pdf]
  10. Lan, L., Malbasa, V., Vucetic, S., Spatial Scan for Disease Mapping on a Mobile Population, in Proceeding of the AAAI Conference on Artificial Intelligence (AAAI), 2014. [pdf]
  11. Zhang, S., Yang, Y., Fan, W., Lan, L., Yuan, M., OceanRT: Real-Time Analytics over Large Temporal Data, in Proceedings of the ACM conference on the Management of Data (SIGMOD), 2014.(demo track) [pdf]
  12. Zhang, K., Wang, Q., Lan, L., Sun, Y., Marsic, I., Sparse semi-supervised learning on low-rank kernel, Neurocomputing 129:265-272, 2014. [pdf]
  13. Djuric, N., Lan, L., Vucetic, S., Wang, Z., BudgetedSVM: A Toolbox for Large-Scale Non-linear SVM [open source software] Journal of Machine Learning Research, 14, 3813-3817, 2013. [pdf]
    This toolbox is designed for training non-linear SVM on large scale, high-dimensional data when it cannot fit into memory. It can be treated as a missing link between LibLinear and LibSVM, combining efficiency of linear SVM with accuracy of kernel SVM models.
  14. Lan, L., Vucetic, S., Multi-task Feature Selection in Microarray Data by Binary Integer Programming, BMC Bioinformatics, Vol. 7 (Suppl. 7): S5, 2013.[pdf]
  15. Lan, L., Djuric, N., Guo, Y., Vucetic, S., MS-kNN: Protein Function Prediction by Integrating Multiple Data Sources, BMC bioinformatics, Vol. 14 (Suppl. 3): S8, 2013. [pdf]
  16. Radivojac, P., Clark, WT., ..., Lan, L.,, Djuric, N., Guo, Y., Vucetic, S., ..., Friedberg, I., A Large-scale Evaluation of Computational Protein Function Prediction. Nature Methods, Vol. 10 (3): pp. 221-229, 2013. [pdf]
  17. 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. [pdf]
  18. 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. [pdf][code]
  19. Wang, Z., Lan, L., Vucetic, S. Mixture Model for Multiple Instance Regression and Applications in Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 50, no. 6, pp.2226-2237 2012 [pdf]
  20. Lan, L., Djuric, N., Guo, Y., Vucetic, S., Protein Function Prediction by Integrating Different Data Sources, Automated Function Prediction SIG 2011 featuring the CAFA Challenge: Critical Assessment of Function Annotations (AFP/CAFA 2011), Vienna, Austria, 2011. (Our team got the best AUC accuracy among 45 groups) [pdf]
  21. Lan, L., Vucetic, S., Improving Accuracy of Microarray Classification by a Simple Multi-Task Feature Selection Filter, International Journal of Data Mining and Bioinformatics, Vol. 5 (2), pp. 189-208, 2011. [pdf]
  22. Lan, L., Shi, H., Wang, Z., Vucetic, S., An Active Learning Algorithm Based on Parzen Window Classification, JMLR W&C Proc. Workshop on Active Learning and Experimental Design (2010 AISTATS Active Learning Challenge), 2010. (Our results ranked as the 5th in the competition) [pdf]
  23. Lan, L., Vucetic, S., A Multi-Task Feature Selection Filter for Microarray Classification, IEEE Int’l Conf. on Bioinformatics and Biomedicine (BIBM), Washington, D.C., 2009. [pdf]