Liping Liu, Ph.D.Curriculum Vitae
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: Research Publications Materials Bio Sketch
My research mainly focuses on machine learning problems in computational sustainability. My collaborators and I have applied various machine learning techniques to problems in the study of sustainability, especially ecosystem management. I include the following three projects in my thesis:
Future research: I will still focus on machine learning problems in computational sustainability. Particularly, I will focus on topics such as the impact of climate change on ecosystems and long-term ecosystem management. On the machine learning side, I will pay more attention to interactive machine learning robust decision-making.
Principle of my research: application-driven fundamental machine learning research.
Liu, L.-P.. Machine Learning Methods for Computational Sustainability. PhD Thesis, Oregon State University, 2016. [pdf]
Liu, L.-P., Dietterich, T.G., Li, N., and Zhou, Z.-H.. Transductive Optimization of Top k Precision.arXiv:1510.05976 [cs.LG], Accepted by IJCAI 2016.
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).
|Variable definition||Use after definition||Use after definition, or use with immediate explanation|
|Variable name||Multiple characters, straightforward meaning for easy debugging||Single character with different fonts and cases, uniform styles for easy reading|
|Variable semantics||Expressing program logic, can be simple types or objects||As simple as possible, e.g. real number, vector, set, ...|
|Functions/Equations||Flow of clauses, such as if, for, and functions||Composition of simple operations, such as min, max, sum, and product|
|Emphasis||Efficiency and clearness||Clearness|
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. Yuan Jiang and Zhi-Hua Zhou. Then I worked in Alibaba for one year and a half. After that, I spent six years to finish my PhD at Oregon State University. My PhD advisor was Prof. Thomas Dietterich.
Last updated 06/13/2016, Liping Liu