Liping Liu

Ph.D. candidate, on job market
School of Electrical Engineering and Computer Science
1148 Kelley Engineering Center
Oregon State University
Corvallis, Oregon 97331

E-mail: liuli [@] eecs [.] oregonstate [.] edu

Page Contents: Job Search Research Publications Materials Bio Sketch

Job Search

I am now actively searching for research jobs(faculty positions, postdoc postions, or research positions in industry). Here are my curriculum vitae, research statement, and teaching statement.

Current Research

My research is mainly focused on machine learning problems in computational sustainability. I have worked on the following problems: superset label learning, probabilistic modeling at the population level, and integration of prediction and decision-making. I have put many machine learning tools in my toolbox, such as inference and learning of graphical models, learning theory, and optimization (convex analysis and mixed integer programming).



Liu, L.-P., Dietterich, T.G., Li, N., and Zhou, Z.-H.. Transductive Optimization of Top k Precision.arXiv:1510.05976 [cs.LG]. 2015.

Liu, L.-P., Quanz, B., Xing, D., Deshpande, A., and Liu, X.. Predicting Weeks-Of-Supply via Sequence Aggregating. In: Proceedings of the 2015 INFORMS Workshop on Data Mining and Analytics. 2015.

Pei, Y, Liu, L.-P., and Fern, X.. Bayesian Active Clustering with Pairwise Constraints. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'2015).

Liu, L.-P., Sheldon, D., and Dietterich, T.. Gaussian Approximation of Collective Graphical Models. In: The 31st International Conference on Machine Learning (ICML'2014). [code]

Liu, L.-P. and Dietterich, T.. Learnability of the Superset Label Learning Problem. In: The 31st International Conference on Machine Learning (ICML'2014).

Liu, L.-P. and Dietterich, T.. A Conditional Multinomial Mixture Model for Superset Label Learning. In: 2012 Conference on Neural Information Processing Systems (NIPS'2012). [supp. marterial][code]

Liu, L.-P. and Fern, X.. Constructing Training Set for Outlier Detection. In: Proceedings of the 12th SIAM International Conference on Data Mining (SDM'12).

Hutchinson, R., Liu, L.-P., and Dietterich, T.. Incorporating Boosted Regression Trees into Ecological Latent Variable Models. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'2011)

Liu, L.-P., Jiang, Y., and Zhou, Z.-H.. Least Square Incremental Linear Discriminant Analysis. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09).

Liu, L.-P., Yu, Y., Jiang, Y., and Zhou, Z.-H.. TEFE: A Time-Efficient Approach to Feature Extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08)

Jiang, Y. and Liu, L.-P.. Survey of Data Mining on Data Stream. Journal of Jiangnan University(Natural Science Edition).

Materials and Links

Bio Sketch

I received my B.S. in computer science from Hebei University of Technology in 2006. After three years' study in LAMDA group, I received my M.S. from Nanjing Univeristy in 2009. My advisor was Prof. Jiang and Zhi-Hua Zhou, Yuan. Then I worked in Alibaba for one year and a half. After that, I began to pursue my Ph.D. in Oregon State University. My advisor is Prof. Thomas Dietterich.

Last updated 01/29/2016, Liping Liu