Extensive hands-on experience with various computer vision and machine learning algorithms: PCA, LDA, SVM, LBP/Gabor/SIFT features, Adaboost, Normalized cut, Structure from motion, Hough transform, Dictionary Learning, Manifold learning etc. Specialties: Computer Vision, Image Processing, Machine Learning, Pattern Recognition. Programming: C++, C, Matlab, Python, OpenCV

Senior Applied Researcher @ Research and development in computer vision, image processing and computer graphics for a next generation product feature. From January 2015 to Present (11 months) San Jose, CAResearch Asistant @ -Face Recognition in Remote Acquisition Condition: Built a face database acquired in remote and unconstrained outdoor environment. Investigated various artifacts resulting from long range acquisitions, e.g. poor illumination, pose variation, blur and atmospheric artifacts. Evaluated state-of-the-art face recognition algorithms on this dataset. -Image Deconvolution Using Example-driven Manifold Prior: proposed a patch-manifold prior which is formed by incorporating unlabeled image data of natural images for regularizing the image deconvolution problem. Extended the framework for handling combined illumination and blur variations for Lambertian objects. -Unsupervised Domain Adaptation for Face and Object Recognition: proposed an unsupervised domain adaptation approach through interpolating subspaces via dictionary learning to link the source and target domains. Evaluated the method on face recognition across pose, blur and illumination variations, and cross dataset object classification. From 2009 to 2014 (5 years) Research Intern @ -Proposed a method for 3D geometric inference of indoor scenes from time lapse sequences. From May 2012 to August 2012 (4 months) Cambridge, MassachusettsResearch Intern @ -Pathological Image Analysis using Geometric Statistics of Nuclei: Proposed a generalized fast radial symmetry transform (GFRS) for ellipse detection. Built a nucleus detector based on the GFRS transform. Exploited the geometric statistics of nuclei for distinguishing between benign and malignant stained biopsy samples. From June 2011 to September 2011 (4 months) Princeton, New JerseyTeaching Assistant @ -Assisted in teaching undergraduate courses: Communication systems (ENEE 420) and Fundamental Electric and Digital Circuit Laboratory(ENEE 206). From September 2008 to May 2009 (9 months)

Doctorate, Electrical and Computer Engineering @ University of Maryland From 2008 to 2014 Bachelor of Science (B.S.), Automation @ Tsinghua University From 2004 to 2008 Jie Ni is skilled in: Computer Vision, Machine Learning, Matlab, Pattern Recognition, Image Processing, Digital Image Processing, Statistics, C++, Digital Signal..., Artificial Intelligence, C, Linux, OpenCV, Algorithms