Liang Lan

Advisory Researcher

Lenovo Big Data Lab, Hong Kong

Email: lanliang (at) temple.edu or llan (at) lenovo.com

 

Education

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

M.S., Computer and Information Science, Temple University, Philadelphia, USA, 2007~2009 (advisor: Dr.Slobodan Vucetic).

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

 

Research Interests

Machine Learning, Data Mining, Big Data Analytics Applications (Banking/Finance, Public Health, Predictive Maintenance)

 

EXPERIENCE

(07/2016 ~ present) Advisory Researcher, Lenovo Big Data Lab, Hong Kong.

(03/2016 ~ 06/2016) Scientist II, Institute for Infocomm Research, Singapore.

(05/2014 ~ 03/2016) Scientist I, 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.

 

Publications

  1. 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]
  2. Zhang, K., Lan, L., Kwok, T.J., Parvim, B., Vucetic, S., Large Scale Semi-Supervised Learning via Sparse Nonparametric Prototype Model, IEEE Transcations on Neural Networks and Learning Systems, 2014. [pdf]
  3. 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]
  4. Zhang, K., Wang, Q., Lan, L., Sun, Y., Marsic, I., Sparse semi-supervised learning on low-rank kernel, Neurocomputing, in press, 2013. [pdf]
  5. 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.
  6. Lan, L., Vucetic, S., Multi-task Feature Selection in Microarray Data by Binary Integer Programming, BMC Bioinformatics, Vol. 7 (Suppl. 7): S5, 2013.[pdf]
  7. 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]
  8. 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]
  9. Zhang, K., Lan, L., Liu, J., Rauber, A., Moerchen, F., Inductive Kernel Low-rank Decomposition with Priors, in Proceedings of the Twenty-Nineth International Conference on Machine Learning (ICML), 2012. [pdf]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]