Enming Luo (CV)
Video Processing Lab
In Apr. 2016, I joined Facebook (Menlo Park, CA) as a research scientist. I work on machine learning and computer vision under the Ads team.
In Feb. 2016, I passed my PhD defense and my PhD thesis title is Statistical and Adaptive Patch-based Image Denoising. My supervisor in UCSD is Prof. Truong Nguyen and my research focuses on image processing and computer vision with emphasis on ill-posed inverse problems including image denoising, deblurring and super-resolution. I have broad interests in machine learning, data mining and predictive analytics. I have hands-on experiences on data mining (text mining) including real dataset pre-processing, modeling and inference using R and Python.
In Sep. 2014, I passed my PhD qualifying exam and advanced to candidate of philosophy (CPhil).
In the summer of 2012, I interned at InterDigital in the San Diego office. (filed one US patent: Adaptive Upsampling for Multi-layer Video Coding)
In the summer of 2011, I interned at Cisco Systems in the Milpitas office.
From 2009 to 2010, I was an engineer in ASTRI, Hong Kong.
S. Parameswaran, E. Luo, and T. Q. Nguyen, "Patch Matching for Image Denoising Using Neighborhood-based Collaborative Filtering," IEEE Trans. Circuits and Sys. for Video Tech. (TCSVT'16), Aug. 2016. [paper]
S. H. Chan, E. Luo, and T. Q. Nguyen, "Adaptive Patch-based Image Denoising by EM-adaptation," in Proc. IEEE Global Conf. Signal and Information Process. (GlobalSIP'15), Dec. 2015. (Oral Presentation) [paper][slides][code]
E. Luo, S. H. Chan, and T. Q. Nguyen, "Image Denoising by Targeted External Databases," in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Process. (ICASSP'14), 2014. (Oral presentation, and NSF and ICASSP travel grants awarded) [paper][slides]
E. Luo, S. H. Chan, S. Pan, and T. Q. Nguyen, "Adaptive Non-local Means for Multiview Image Denoising: Searching for the Right Patches via a Statistical Approach," in Proc. IEEE Intl. Conf. Image Process. (ICIP'13), 2013. (Oral Presentation) [paper] [slides]
Experienced with programming languages: C/C++, Java, Python, R (Rattle), Matlab, SQL, HiveQL, PHP, HTML
Experienced with development tools: MS visual studio, RStudio, Eclipse, Netbeans, PyDev, Hadoop, LaTex, VIM
learning/Data mining techniques:
-- Supervised learning: Linear regression (ridge, LASSO, elastic net), decision tree, random forest, boosting, GBDT, perceptron, logistic regression, support vector machine, neural network, deep learning
-- Unsupervised learning: k-means, mixture models (EM algorithm), PCA
Last modified: 08/15/2016