Xiaoli Z. Fern, Ph.D

   Associate Professor

 School of Electrical Engineering and Computer Science
Oregon State University

To contact me:

Office:   Kelly 3073
Phone:  (541)737-2557
e-mail:  xfern AT eecs.oregonstate.edu

Quick links: Teaching, Research Projects, Students, CV, Publication list, The bioacoustic project , Career Grant


Ph.D, Computer Engineering, ECE, Purdue University, Indiana, USA. 2005
M.S. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai, China 2000
B.S. Automation, Shanghai Jiao Tong University, Shanghai, China 2000

Short Biography:

Dr. Xiaoli Fern is an assistant professor at the School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, since 2005.  She received her Ph.D. degree in Computer Engineering from Purdue University, West Lafayette, IN, in 2005 and her M.S. degree from Shanghai Jiao Tong university (SJTU), Shanghai China in 2000. Her general research interest is in the area of machine learning and data mining. She received an NSF Career Award in 2011. She co-organized the first International Workshop on Discovering, Utilizing and Summarizing Multiple Clustering (MultiClust KDD 2010), and served as the publicity chair for International Conference on Machine Learning in 2007. Dr. Xiaoli Fern is currently an editorial board member of the Machine Learning Journal and serves regularly on the program committee for a number of top tier international conferences on machine learning and data mining such as ICML, ECML, AAAI, KDD, ICDM, SIAM SDM. 


My primary research interest is in the areas of machine learning and data mining. My research is largely driven by practical applications and the challenges they present to traditional machine learning and data mining techniques. Below is a sample of my current and past research projects and some selected publications related to these projects. See my publication list for a more complete list of publications.
Explorative data clustering involves grouping objects into clusters such that similar objects are grouped together. My research attemps to advance the field of unsupervised clutsering in a number of directions.
First, motivated by the fact that objects in a data set maybe similar to each other in multiple different ways, and different clustering structures may exist in the same data. I am interested in exploratively examining data in different ways to produce different clusterings. Such clusterings can be sometimes combined to provide a more reliable view of the structure of the data via cluster ensemble methods, or other times examined individually as they may provide different insights (non-redundant clustering).

Clustering with partial supervision and active learning.  Due to the inherent embiguous nature of the clustering task, in practice it is very useful to consider some user-provided side information such that a learning algorithm can seek to find a underlying clustering struture to that is most consistent with the side information. For example such information can be expressed in terms of pairwise constraints requiring some objects to be placed together or apart. I am interested in developing clustering techniques that can take into consideration richer forms of user constraints (e.g., comparative constraints involving multiple objects), and active learning frameworks that effectively acquire various form of user inputs without imposing heavy burden on the users. This work is supported by  my NSF career grant. For more details, please see the project page here.

Selected publications:

Wei Zhang, Akshat Surve, Xiaoli Z. Fern and Thomas Ditteriech, Learning Non-redundant Codebooks for Classifying Complex Objects,  In Proceedings of International Conference on Machine Learning, ICML 2009. PDF

Javad Azimi and Xiaoli Fern, Adaptive Cluster Ensemble Selection, In Proceedings of International Joint Conference on Artificial Intellegence, IJCAI 2009. PDF

Xiaoli Z. Fern and Wei Lin, Cluster Ensemble Selection, Journal of Statistical Analysis and Data Mining, Special Issue on Best of SDM08, Volume 1, Issue 3 , Pages128 - 141, 2008 Preprint

Xiaoli Z. Fern and Wei Lin, Cluster Ensemble Selection, In Proceedings of 2008 SIAM International Conference on Data Mining (SDM08). pdf

Ying Cui, Xiaoli Z. Fern and Jennifer Dy, Non-redundant multi-view clustering via orthogonalization, in Proceedings of 7th IEEE International Conference on Data Mining (ICDM07) pdf.

Xiaoli Z. Fern and Carla E. Brodley,  "Cluster ensembles for high dimensional data clustering: An empirical study", Techenical report  CS06-30-02.

Xiaoli Z. Fern and Carla E. Brodley,  "Solving cluster ensemble problems by bipartite graph partitioning", in Proceedings of 21th International Conference on Machine learning (ICML2004), PDF file, Matlab implementation of the algorithm ( Note: this code is provided on "as is" basis for research use only. )

Xiaoli Z. Fern and Carla E. Brodley,  "Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach",  in Proceedings of 20th International Conference on Machine learning (ICML2003), PDF file


    CS434: Introduction to Machine Learning and Data Mining Fall 2012

    CS519 IGERT Ecosystem Informatics

    CS534 Machine Learning Spring 2012

    CS325 Analysis of Algorithms Winter 2012

Current Students:

Forrest Briggs (Ph.D)
Yuanli Pei (Ph.D)
Jun Xie (Master)
Lu Qin (Master)
Teresa Tjahja (Master)

Former students:

Sicheng Xiong, Master 2013, currently at ebay
Javad Azimi, Ph.D 2012, currently at Microsoft
Wei Lin, Master 2008
Akshat Suave, Master 2009
Anshul Dube, Master 2009
Arunkumar Puppala, Master
Chaitanya Komireddy, Master

Useful links

Engeering TEACH
OSU class listing
Useful matlab tips from Kevin Murphy at UBC