Quan Wang     王泉    

I am Quan Wang, a Software Engineer at Google, New York City, NY. I was a Machine Learning Scientist at Amazon, Boston, MA during 2014-2015. I did my Ph.D. in Computer & Systems Engineering at Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute. My Ph.D. advisor is Professor Kim L. Boyer, and I have been working in the Signal Analysis and Machine Perception Laboratory (SAMPL), located in JEC 6304, RPI. I received my B.Eng. in Automation from Tsinghua University in 2010.

My research interests focus on:

My Curriculum Vitae: click here

Email: quanrpi at gmail dot com

2015.11.17 — I joined Google as a software engineer.

2015.04.2 — I received the Allen B. Dumont Prize from Rensselaer Polytechnic Institute for my graduate research.

2014.12.31 — I received my Ph.D. degree officially.

2014.11.17 — I joined Amazon as a Machine Learning Scientist.

2014.4.28 — The version 2.3 of the AGSM toolkit has been released! Great new features include: (1) line fitting and circle fitting demos; (2) C++/MEX implementation of GVF and force/torque computation; (3) smart initialization; (4) the AGSM Canvas app! This new version is significantly FASTER and MORE ROBUST than version 1.0.

Active Geometric Shape Models and CSF Detection

I have been working with Prof. Kim L. Boyer to develop a novel approach, the Active Geometric Shape Models, to fit parametric shapes to data and images. Our paper is published on CVIU.

COSBOS: COlor-Sensor-Based Occupancy Sensing

With multiple color-controllable LED fixtures and color sensors, our COSBOS technique enables low-cost and privacy-preserving occupancy distribution estimation. The direct application of this technique is occupancy-sensitive smart lighting, in which the system automatically delivers the light that best suits the occupancy scenario in an indoor space.

Learning-Based Knee Cartilage Segmentation in 3D MR

The automatic segmentation of human knee cartilage from 3D MR images is challenging due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. We present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. High accuracy and robustness is achieved on 176 volumes from the OAI dataset.

HighBeam Research news about my work on AGSM

HighBeam Research news about my patent on Semantic Context Forests

Issues in Computer Engineering: 2013 Edition article about my work on AGSM

Technology Org article about my work on COSBOS

One News Page article about my work on COSBOS

Tracking Tetrahymena Pyriformis Cells using Decision Trees

We approach the cell tracking problem by interpreting it as a classification problem. Our paper is published on ICPR 2012.

Segmentation and Disease Detection in Echocardiogram Images

This is my work as an Research Intern at IBM. It is part of the Medical Sieve project.

Manifold Learning for Image Classification

We apply manifold learning techniques including multidimensional scaling (MDS) and Isomap on high-level semantics-sensitive pairwise image distances, such as IMED, SPM, IRM and their variants to learn fixed-length vector representations of images. We are looking at applications including style categorization, scene classification and object recognition.

GPU Implementation for GVF Force Field

This is a project I have been working on when I was in Prof. Badrinath Roysam's lab. My work is part of the FARSIGHT project . Here is the

Implementation and Study of Light-field-based 3D Object Retrieval System

This is my research for undergraduate thesis at Tsinghua University, under the guidance of Prof. Qionghai Dai and Prof. Guihua Er.

GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation

This is the final project of Prof. Qiang Ji's course Introduction to Probabilistic Graphical Models. In this project, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectationmaximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.
Download the paper here.
Download the Matlab code here.

Hidden Markov Random Field Model, its Expectation-Maximization Algorithm, Implementation, and Applications in Edge-Prior-Preserving Image Segmentation

This is the final project of Prof. Birsen Yazıcı's course Detection and Estimation Theory. In this project, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.
Download the paper here.
Download the HMRF-EM-image Matlab toolbox here.

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

This is the final project of Prof. Qiang Ji's course Pattern Recognition. In this paper, we discussed the theories of PCA, kernel PCA and ASMs. Then we focused on the pre- image reconstruction for Gaussian kernel PCA, and used this technique to design kernel PCA based ASMs. We tested kernel PCA on synthetic data and human face images, and found that Gaussian kernel PCA succeeded in revealing more complicated structures of data than traditional PCA and achieving much lower classification error rate. We also implemented the Gaussian kernel PCA based ASMs and tested it on human face images. We found that Gaussian kernel PCA based ASMs is promising in providing more deformation patterns than traditional ASMs.
Download the paper here.
Download the PPT here.
Download the Matlab code here.

Tracking Based 3D Visualization from 2D Videos

This is the final project of Prof. Qiang Ji's course Computer Vision. In this project, we established a framework to convert 2D videos to pseudo 3D videos. Our basic idea is to track the moving objects in the video and separate them from the background. Then we give different depth information to the objects and the background, and visualize them in 3D. Download the report here.

Here is a demo of our 3D animations. Please wear blue-red 3D glasses.

Hästens app on iPhone

This is the final project for the course Software Design & Documentation at RPI. This is our project website.

I have been working as the teaching assistant of these courses:

MY GPA at RPI is 4.0 out of 4.0. I have been taking these courses at RPI: