Machine Learning Department
&
Auton Lab
Carnegie Mellon University
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8005 Gates Hillman Complex
Machine Learning Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
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Phone: 1-412-818-8754
Email:
Homepage:
http://www.cs.cmu.edu/~lxiong
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Jeff Schneider, Associate Research Professor, Auton Lab & Robotics Institute, Carnegie Mellon University.
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- 2008.9 - present
Carnegie Mellon University, Pittsburgh, PA
PhD Student, GPA 4.08
Machine Learning Department
- 2005.9 - 2008.7
Tsinghua University, Beijing, China
Master of Engineering
Major: Pattern Recognition and Intelligent Systems
- 2001.9 - 2005.7
Tsinghua University,
Beijing, China
Bachelor of Engineering, Outstanding Graduate
Major: Control Science and Engineering
Thesis: Personalized Synthesis of Hand-written Chinese Characters
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- 2012.6 - 2012.9
Yahoo! Labs
Intern
Worked on search engine log analysis and query rewriting for movie search for the Media Sciences and Search Sciences teams.
- 2009.6 - 2009.8
Google Inc. Santa Monica
Intern
Worked on Traffic Estimation for the AdWords team.
- 2007.3 - 2007.9
Intel China Research Center
Intern
Worked on computer vision and multimedia mining.
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Graduate Fellowship, Carnegie Mellon University, 2008 - present.
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Travel awards from several academic conferences, 2007 - present.
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JiangZhen scholarship, Tsinghua University, 2007. (Top 1%)
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Outstanding Graduate of Tsinghua University, 2005. (Top 10%)
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National scholarship, Tsinghua University, 2004. (Top 5%)
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Outstanding Freshman scholarship, Tsinghua University, 2001.
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My focus is on how to learn from collective data i.e. data
that are organized by groups. For example, an image is a
group of local patches, a video is a set of images, an
article is a group of paragraphs, a search result is a group
of links...
Particularly, I am trying to help the scientists discover
interesting phenomena from the huge amount of data they have
in e.g. astronomy and physics.
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Learning from collective data (Thesis), Carnegie Mellon
University
Developing general machine learning techniques for data
that are organized by groups. This theme of research
unifies several of my previous research topics and seems
quite useful and exciting. Applicable problems include
the processing of images, text, social network,
recommendation/rating, astronomy, and physics data.
Our standpoint is that the collective nature of these
data should be respected, and we should not reduce a
group into a point/vector for no good reason. We
approach this problem by either capturing the generative
process of groups using hierarchical models, or
measuring the similarity between groups directly. Now we
can to do classification, clustering, embedding, anomaly
detection on collective data.
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Novelty discovery for astronomy and
physics, Carnegie Mellon University
Developing algorithms to automatically discover unusual
and potentially valuable phenomena. In the Sloan Digital
Sky Survey (SDSS), the algorithms can discover both
interesting individual objects (stars, galaxies, etc)
and groups of objects (galaxy clusters, etc). Similar
techniques are also used to detect unusual things from
large-scale simulation systems in physics (e.g. fluid
and particle simulation). Collaborated with University
of Washington, and John Hopkins University.
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Query Rewriting for Movie Search, 2012.6 - 2012.9, Yahoo! Labs
Developed algorithms to enhance Yahoo!'s movie search
backend. The result is that we can replace users'
obscure/indirect queries with new ones that can trigger
the correct results from the existing blackbox
backend. This is achieved by analyzing the search engine
log, and learning to find and rank potential replacement
queries. Evaluations show that this work can drastically
increase the recall of the system without sacrificing
its precision. Awaiting deployment into production.
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Protein classification using cell
images, 2010.10 - 2012.12, Carnegie Mellon
University
Studied the problem of classifying proteins' location
pattern based on the cell images from the Human Protein
Atlas. Surpassed the state of the art
accuracy. Collaborated with the Biomedical Department.
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Internet Ads traffic estimation, 2009.6
- 2009.8, Google Inc.
As an intern, I worked in the AdWords team on developing
the new simulation-based traffic estimation backend
"Nostradamo". My main responsibility is to get the
predicted click-through rate given the ads and the
search query. To do that, I studied the details of the
advertising mechanism and the prediction algorithms,
processed massive log data, interfaced various internal
services for feature/signal extraction, and finally
communicated with the prediction service to get the
results.
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Sales prediction, 2008.9 - 2009.8,
Carnegie Mellon University
I worked on the sales prediction problem. The problem is
how to predict future orders based on existing sales
data. This research mainly involves collaborative
filtering (recommendation system) to tackle the
lack-of-feature problem, and temporal analysis to
accommodate market changes. Collaborated with ECCO.
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Railroad image synthesis, 2007.6 -
2008.6, Tsinghua University
As the leader and coordinator, I worked on developing an
image synthesis system that were used to test a hazard
detection system for railroad. Collaborated with
Mitsubishi Heavy Industries.
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Contextual visual learning, 2007.3 -
2007.9, Intel China Research Center
As an intern, I worked on utilizing contextual
information in vision problems. Studied problems
include: scene analysis, contextual probing,
context-aided detection, and part-based models.
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Robot vision and learning system,
2006.1 - 2007.4, Tsinghua University
Designed and developed the vision and learning system
for a cognitive robot. This robot uses a camera to
automatically detect strangers, and learn their
identities from a tutor. Then, it can recognize the
previouly seen people and greet them. As it saw more and
more people, its recognition ability improved over time.
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Hand-written character synthesis,
2005.2 - 2005.7, Tsinghua University
Co-developed a system that generates hand-written
Chinese characters whose style is learned from images of
a particular person's hand writing. In charge of
designing the learning process and implementing the
synthesis module.
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Liang Xiong, Fei Wang and Changshui Zhang, Guide Manifold
Alignment by Relative Comparisons, In: J. Wang ed.
Encyclopedia of Data Warehousing and Mining 2nd Edition,
Hershey, PA: IGI Publishing.
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Scott F. Daniel, Andrew Connolly, Jeff Schneider, Jake Vanderplas, Liang Xiong,
Classification of Stellar Spectra with LLE, Astronomical Journal, 142, 203.
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Liang Xiong, Jeff Schneider, Learning from Point Sets
with Observational Bias, Uncertainty in Artificial Intelligence (UAI) 2014.
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Liang Xiong, Jieyue Li, Robert F. Murphy, Jeff Schneider,
Protein Subcellular Location Pattern Classification in Cellular Images Using Latent Discriminative Models, Intelligent Systems for Molecular Biology (ISMB), 2012.
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Barnabas Poczos, Liang Xiong, Dougal Sutherland, Jeff Schneider,
Nonparametric Kernel Estimators for Image Classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
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Liang Xiong, Xi Chen, Jeff Schneider,
Direct Robust Matrix Factorization for Anomaly Detection, IEEE International Conference on Data Mining (ICDM), 2011.
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Liang Xiong, Barnabas Poczos, Jeff Schneider,
Group Anomaly Detection using Flexible Genre Models, Neural Information Processing Systems (NIPS), 2011.
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Barnabas Poczos, Liang Xiong, Jeff Schneider,
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions,
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), 2011.
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Liang Xiong, Barnabas Poczos, Jeff Schneider, Hierarchical Probabilistic
Models for Group Anomaly Detection, AI and Statistics (AISTATS), 2011.
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Liang Xiong, Xi Chen, Tzu-kuo Huang, Jeff Schneider, and Jaime
Carbonell, Temporal Collaborative Filtering with Bayesian
Probabilistic Tensor Factorization, SIAM Data Mining (SDM), 2010.
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Liang Xiong, Fei Wang and Changshui Zhang, Multilevel Belief
Propagation for Fast Inference on Markov Random Fields, IEEE International Conference on Data Mining (ICDM), 2007.
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Liang Xiong, Fei Wang and Changshui Zhang, Semi-definite
Manifold Alignment, European Conference on Machine Learning (ECML)
2007.
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Liang Xiong, Jianguo Lee and Changshui Zhang, Discriminant
Additive Tangent Space for Object Recognition. IEEE Conference
on Computer Vision and Pattern Recognition (CVPR) 2007.
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- Intermediate Statistics, 2011
- Machine Learning, 2012
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- Journal of Machine Learning Research
- Pattern Recognition
- IEEE Trans on System, Man, and Cybernatics (SMCB)
- Journal of Information Retrieval
- Journal of Data Mining and Knowledge Discovery
- Neural Computing
- International Conference on Machine Learning (ICML)
- International Joint Conference on Artificial Intelligence (IJCAI)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- International Conference on Latent Variable Analysis and Source Separation
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