SlideShare a Scribd company logo
Salt Lake City, UT, 84109 iinstry@gmail.com
http://www.linkedin.com/in/yuliliang1/ Yu-Li Liang 720-938-7905
(Green Card holder. Do not need sponsorship)
Education
• PhD, Computer Science (GPA: 3.86 of 4)
Sep. 2007 ~ Dec. 2012, University of Colorado Boulder, CO, USA; with 1 year Fellowship.
Thesis: “Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line
Videos.”
• MS, Biomedical Engineering (GPA: 3.83 of 4)
Sep. 2003 ~ May 2005, Drexel University, PA, USA; with 2-year Fellowship.
Thesis: “Effects of Caged Compound and Electrical Stimulation in Single Layer V Cortical Pyramidal Neuron: A
Computational Study.”
• BS, Electrical Engineering (GPA: 3.54 of 4)
Sep. 1996 ~ Jun. 2000; National Tsing Hua University, Taiwan.
Experience
• Postdoctoral Scholar (Electrical Engineering Dep., California Institute of Technology, CA, Feb. 2013 – Jul. 2013 )
• Advanced Imaging Intern (Imaging Lab, Analytical Services, Alticor Inc., MI, May 2009 – Aug. 2009 )
• Research Assistant (3D Cardiac Imaging Lab, Univ. of Colorado Health Science Center, CO, Jul. 2006 – Jun. 2007)
Skills
• Programming languages: Matlab, C++, Python, R, Matlab GUI, WEKA, Java, Visual Basic, Unix script, OpenGL,
LABVIEW, XPPAUT, etc.
• Language: English (fluent), Mandarin / Chinese (native language)
• Programming, Problem Solving:
Selected Projects
Computer
Vision, Image
processing,
Machine
Learning
( July 2007 –
present)
Deep Neural Network
Self-Learning the online course: Neural Networks for Machine Learning, Instructed by Geoffrey
Hinton. (https://www.coursera.org/course/neuralnets)
Detect Obscene Content in of Online Videos
Develop an algorithm to detect obscene content in large multimedia image
dataset extracted from on-line video chatting services, Chatroulette. (code in
C++, OpenCV, & Matlab). The algorithm classifies users based on multiple
features, such as facial features, motion, and skin. We further investigate other
generic feature (i.e., SIFT features, HOG, etc.) in obscene content detection.
Evaluate Skin Conditions by Head Photos
Given facial photos, develop a procedure to automatically estimate the facial
skin condition quantitatively. The core part is an algorithm integrates Active
Appearance Model and feature points registering. (code in C++ & Matlab)
Geographical Feature Detection in Satellite Images
Develop an algorithm to automatically detect geographic features (i.e., lakes)
above Greenland ice sheet in a massive set of satellite images (i.e., *.hdf files for
10 years). The algorithm achieves high accuracy (0.99 recall and 0.963
precision) to robustly detect lakes in both cloudy and cloud-free images (code in
Matlab)
Head Pose Estimation
Apply two methods to estimate head pose in images: (1) Extracting pixel
gradient feature and classified it by Support Vector Machine (2) Extracted a
pixel comparison feature and classified it by AdaBoost algorithm. (code in C++,
OpenCV & Matlab).
Color Texture Segmentation
Achieve color-texture segmentation in 3 steps: (1) Segment color by Mean-Shift (2) Segment texture
by Steerable Filter pyramid ( http://www.cns.nyu.edu/~lcv/texture/), and (3) Integrate both results into
final segmentation map (code in Matlab).
Data Analysis,
Machine
Learning
(Winter 2008 -
Present)
High Dimensional Data Index (Locality Sensitive Hashing)
Study and try some variance of Locality Sensitive Hashing that based on p-stable distribution (Datar et
al., 2004) by modifying the code from “LSHKIT” (http://lshkit.sourceforge.net/).
High Dimensional Data Analysis
In a course project, extracting movie preference information (Netflix challenge) from a large and high-
dimension data set (i.e., a sparse matrix with dimension: 2649429 x 17770) in three steps: (1) Embed
high-dimensional input data into a lower dimension format (by Laplacian Eigenmap or Random
Projection), (2) Find meaningful user clusters (by K-means or Mix of Gaussian), and (3) Use those
information to predict score of query movie. (coded in Matlab)
Analyze data by WEKA
With given data, using WEKA (http://www.cs.waikato.ac.nz/ml/weka/) to build an optimal model by
investigating several machine learning methods (i.e., Random Forest, AdaBoost, Linear Regression,
Ridge Regression, and Support Vector Machine).
Biological
System modeling
(Apr. 2004 –
Sep. 2005)
Neuronal System Modeling
Investigate the response of a Pyramidal Neuron under electrical stimulation and
the artificial neurotransmitters by building a mathematical model (i.e., HH
model) to simulate Dopaminergic Neuron signal pattern. This model
successfully replicated the in vivo experimental results. (code in Matlab and
NEURON simulation program)
Selected Publications
• X. Xing*, Y.L. Liang*, H. Cheng, J. Dang, S. Huang, R. Han, X. Liu, Q. Lv, S. Mishra, "SafeVchat: Detecting
Obscene Content and Misbehaving Users in Online Video Chat Services", 20th
International World Wide Web
(WWW 2011), conference paper. (* equal contribution).
• Y.L. Liang, W. Colgan, Q. Lv, K. Steffen, W. Abdalati, J. Stroeve, D. Gallaher, N. Bayou, "A decadal investigation
of supraglacial lakes in West Greenland using a fully automatic detection and tracking algorithm", Remote Sensing
of Environment, 123, p.127-138 (2012).
• H. Cheng, Y.L. Liang, X. Xing, R. Han, X. Liu, Q. Lv and S. Mishra. “FGC: Fine-Grained Cascaded Classification
for Efficient Misbehaving User Detection in Online Video Chat Services”, Pro. of the 5th ACM international
conference on Web search and data mining, p.23-32, 2012 (WSDM 2012).
• X. Xing, Y.L. Liang, S. Huang, H. Cheng, R. Han, Q. Lv, X. Liu, S. Mishra, Y. Zhu, "Scalable Misbehavior
Detection in Online Video Chat Services", in Proceedings of the 18th ACM SIGKDD Conference on Knowledge
Discovery and Data Mining (KDD'12), Beijing, China, 2012.
News
“Nudity filter helps Chatroulette clean up”, CBC News, January 20, 2011,
http://www.cbc.ca/technology/story/2011/01/20/algorithm-tech-nudity-chatroulette-filter-flashers.html
“Greenland May Be Slip-Sliding Away Due to Surface Lake Melting”, Science Daily, April, 16, 2012
http://www.sciencedaily.com/releases/2012/04/120416135056.htm

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Resume Yu-Li Liang

  • 1. Salt Lake City, UT, 84109 iinstry@gmail.com http://www.linkedin.com/in/yuliliang1/ Yu-Li Liang 720-938-7905 (Green Card holder. Do not need sponsorship) Education • PhD, Computer Science (GPA: 3.86 of 4) Sep. 2007 ~ Dec. 2012, University of Colorado Boulder, CO, USA; with 1 year Fellowship. Thesis: “Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos.” • MS, Biomedical Engineering (GPA: 3.83 of 4) Sep. 2003 ~ May 2005, Drexel University, PA, USA; with 2-year Fellowship. Thesis: “Effects of Caged Compound and Electrical Stimulation in Single Layer V Cortical Pyramidal Neuron: A Computational Study.” • BS, Electrical Engineering (GPA: 3.54 of 4) Sep. 1996 ~ Jun. 2000; National Tsing Hua University, Taiwan. Experience • Postdoctoral Scholar (Electrical Engineering Dep., California Institute of Technology, CA, Feb. 2013 – Jul. 2013 ) • Advanced Imaging Intern (Imaging Lab, Analytical Services, Alticor Inc., MI, May 2009 – Aug. 2009 ) • Research Assistant (3D Cardiac Imaging Lab, Univ. of Colorado Health Science Center, CO, Jul. 2006 – Jun. 2007) Skills • Programming languages: Matlab, C++, Python, R, Matlab GUI, WEKA, Java, Visual Basic, Unix script, OpenGL, LABVIEW, XPPAUT, etc. • Language: English (fluent), Mandarin / Chinese (native language) • Programming, Problem Solving: Selected Projects Computer Vision, Image processing, Machine Learning ( July 2007 – present) Deep Neural Network Self-Learning the online course: Neural Networks for Machine Learning, Instructed by Geoffrey Hinton. (https://www.coursera.org/course/neuralnets) Detect Obscene Content in of Online Videos Develop an algorithm to detect obscene content in large multimedia image dataset extracted from on-line video chatting services, Chatroulette. (code in C++, OpenCV, & Matlab). The algorithm classifies users based on multiple features, such as facial features, motion, and skin. We further investigate other generic feature (i.e., SIFT features, HOG, etc.) in obscene content detection. Evaluate Skin Conditions by Head Photos Given facial photos, develop a procedure to automatically estimate the facial skin condition quantitatively. The core part is an algorithm integrates Active Appearance Model and feature points registering. (code in C++ & Matlab) Geographical Feature Detection in Satellite Images Develop an algorithm to automatically detect geographic features (i.e., lakes) above Greenland ice sheet in a massive set of satellite images (i.e., *.hdf files for 10 years). The algorithm achieves high accuracy (0.99 recall and 0.963 precision) to robustly detect lakes in both cloudy and cloud-free images (code in Matlab)
  • 2. Head Pose Estimation Apply two methods to estimate head pose in images: (1) Extracting pixel gradient feature and classified it by Support Vector Machine (2) Extracted a pixel comparison feature and classified it by AdaBoost algorithm. (code in C++, OpenCV & Matlab). Color Texture Segmentation Achieve color-texture segmentation in 3 steps: (1) Segment color by Mean-Shift (2) Segment texture by Steerable Filter pyramid ( http://www.cns.nyu.edu/~lcv/texture/), and (3) Integrate both results into final segmentation map (code in Matlab). Data Analysis, Machine Learning (Winter 2008 - Present) High Dimensional Data Index (Locality Sensitive Hashing) Study and try some variance of Locality Sensitive Hashing that based on p-stable distribution (Datar et al., 2004) by modifying the code from “LSHKIT” (http://lshkit.sourceforge.net/). High Dimensional Data Analysis In a course project, extracting movie preference information (Netflix challenge) from a large and high- dimension data set (i.e., a sparse matrix with dimension: 2649429 x 17770) in three steps: (1) Embed high-dimensional input data into a lower dimension format (by Laplacian Eigenmap or Random Projection), (2) Find meaningful user clusters (by K-means or Mix of Gaussian), and (3) Use those information to predict score of query movie. (coded in Matlab) Analyze data by WEKA With given data, using WEKA (http://www.cs.waikato.ac.nz/ml/weka/) to build an optimal model by investigating several machine learning methods (i.e., Random Forest, AdaBoost, Linear Regression, Ridge Regression, and Support Vector Machine). Biological System modeling (Apr. 2004 – Sep. 2005) Neuronal System Modeling Investigate the response of a Pyramidal Neuron under electrical stimulation and the artificial neurotransmitters by building a mathematical model (i.e., HH model) to simulate Dopaminergic Neuron signal pattern. This model successfully replicated the in vivo experimental results. (code in Matlab and NEURON simulation program) Selected Publications • X. Xing*, Y.L. Liang*, H. Cheng, J. Dang, S. Huang, R. Han, X. Liu, Q. Lv, S. Mishra, "SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services", 20th International World Wide Web (WWW 2011), conference paper. (* equal contribution). • Y.L. Liang, W. Colgan, Q. Lv, K. Steffen, W. Abdalati, J. Stroeve, D. Gallaher, N. Bayou, "A decadal investigation of supraglacial lakes in West Greenland using a fully automatic detection and tracking algorithm", Remote Sensing of Environment, 123, p.127-138 (2012). • H. Cheng, Y.L. Liang, X. Xing, R. Han, X. Liu, Q. Lv and S. Mishra. “FGC: Fine-Grained Cascaded Classification for Efficient Misbehaving User Detection in Online Video Chat Services”, Pro. of the 5th ACM international conference on Web search and data mining, p.23-32, 2012 (WSDM 2012). • X. Xing, Y.L. Liang, S. Huang, H. Cheng, R. Han, Q. Lv, X. Liu, S. Mishra, Y. Zhu, "Scalable Misbehavior Detection in Online Video Chat Services", in Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012. News “Nudity filter helps Chatroulette clean up”, CBC News, January 20, 2011, http://www.cbc.ca/technology/story/2011/01/20/algorithm-tech-nudity-chatroulette-filter-flashers.html “Greenland May Be Slip-Sliding Away Due to Surface Lake Melting”, Science Daily, April, 16, 2012 http://www.sciencedaily.com/releases/2012/04/120416135056.htm