Large-scale Machine Learning

Software Engineer, Machine Learning @ Dynamic pricing based on machine learning From March 2015 to Present (10 months) Senior Applied Research Engineer @ The application of deep learning in video technology. From January 2014 to February 2015 (1 year 2 months) Research Assistant @ Feature Learning for No-Reference Image Quality Assessment (NR-IQA) 1. Introduced CORNIA: a unsupervised feature learning framework for NR-IQA [CVPR2012]. 2. Introduced a supervised feature learning method for NR-IQA [CVPR 2013]. 3. Developed a unsupervised method for merging multiple Full-Reference (FR) IQA measures and a method for extending existing supervised NR-IQA models to unsupervised models [CVPR2014]. 4. Applied convolutional neural network for NR-IQA [CVPR2014]. Active Learning for Subjective Image Quality Assessment [CVPR2014]: 1. Developed a probabilistic model for combining absolute and preference judgements [ICML2013 workshop]. 2. Developed an active learning system for crowdsourcing information aggregation. 3. Designed and implemented web application for subjective test in a crowdsourced setting. Automatic Detection of Anomalies in Lexical Text 1. Developed a subtree search system for XML file using XPath. 2. Developed a semi-supervised Bayesian rule-based method for anomaly detection. 3. Developed a random forest based method for system combination. [EACL2012 workshop on hybrid system] 4. Assisted in developing GUI for manipulating digital dictionaries in XML format. From August 2009 to December 2013 (4 years 5 months) Research Intern @ Image Quality Assessment; Rectangular Object Detection From June 2011 to August 2011 (3 months) Research Assistant @ Confocal Microscopy System based on Compressive Sensing 1. Designed spectral aperture code for multi-shot compressive spectral imaging. 2. Proposed an optical system for confocal imaging using single pixel detector. 3. Implemented Discrete Cosine Transform (DCT)/IDCT based on CUDA for GPU. From July 2007 to July 2009 (2 years 1 month)

PHD, Electrical Engineering @ University of Maryland College Park From 2009 to 2014 MS, Electrical Engineering @ University of Delaware From 2007 to 2009 BE, Electrical Engineering @ Tsinghua University From 2003 to 2007 Peng Ye is skilled in: Computer Vision, Matlab, Image Processing, Machine Learning, Pattern Recognition, Signal Processing, Python, LaTeX, C, Algorithms, Artificial Intelligence, C++, Natural Language Processing, Data Mining, Statistics