Chunsheng (Victor ) FANG, Ph.D


Research Scientist,

Riverain Medical Group LLC

riverain amazonUC CCHMC CAS ustc







Selected Research Projects

Link prediction in dynamic and multiple graph, a spectral graph framework

On-going research project. Different from traditional "static" link prediction approach, we seek for a more general framework for modeling the dynamic graph (social network, biological network), which can incorporate historical data. Another interest focuses on how to integrate heterogeneous domain graph knowledge, which is modeled by multiple graphs?

  • Chunsheng Fang, Jason Lu, Anca Ralescu, "Graph Spectra Regression with Low-Rank Approximation for Dynamic Graph Link Prediction",NIPS2010 Workshop on Low-rank Methods for Large-scale Machine Learning,Vancouver, Canada, December, 2010. [PDF].
  • Minlu Zhang, Chunsheng Fang, Jason Lu, “Integrative scoring approach to identify transcriptional regulations controlling lung surfactant homeostasis ", International Conference on Data Mining 2010 (ICDM2010), Sydney, Australia, Dec 2010; [link]
  • M Zhang, J Deng, Chunsheng Fang, X Zhang, Jason Lu, "Molecular Network Analysis and Applications", Chapter 11 of "Knowledge-Based Bioinformatics.", John Wiley & Sons, Ltd, July 2010 [link];

Image mining & retrieval in developmental gene expression patterns

Developed  innovative algorithms, Curve Profiling Feature, for embryonic images, and utilizing Kernel SVM, manifold learning to mine the implicit spatial-temporal relationship, achieves 98% accuracy and high ROC-AUC in keyword predictions, beat state-of-the-art while require less in space and time complexity. (Ranked as Top 1 in University Research Council Summer Award, 2010)

  • Chunsheng Fang, Minlu Zhang, Anca Ralescu, Jason Lu: Curve Profiling Feature, in International Conference for Data Mining, Workshop 2010, Sydney, Australia. [link]

MRI structural changes in Parkinsons Diseased brain

Aim to automatically identify Parkinson’s related volumetric patterns. Co-project with UC College of Medicine. We developed a regional ensemble learning algorithm for detecting and classifying diseased regional pattern, achieving high AUC-ROC scores, and the results are consistent with neuropathelogy evidence.

  • Submitted to Human Brain Mapping 2011

Probability based similarity for heterogeneous data

We consider a probability based approach according to which the similarity of two values (in the same domain) is the probability of value pairs whose components are rather apart than the two values under consideration. Similarities across the attributes of the heterogeneous data are combined using Fisher transformation. Results of applying this approach to an image retrieval problem are also presented.

  • Chunsheng Fang, Anca Ralescu,"ProbSim-Annotation: a novel image annotation algorithm using probability based similarity", 20th Midwest Artificial Intelligence & Cognitive Science Conference (MAICS), Fort Wayne , Indiana, Apr 18-19, 2009; [PDF]
  • Chunsheng Fang, Anca Ralescu, "Experiments on Probability based Similarity Measures Applied to Image Similarity", 19th International Conference on Pattern Recognition (ICPR2008) Sensing Web workshop, Tampa, FL, Dec 7 -11, 2008; [PDF]

Real time object detection and classification in video stream

Project funded by NSF-China during my R&D software engineer position in National Lab of Pattern Recognition, Chinese Academy of Sciences, Beijing, China, 2006-2007.

Developed Abnormal Human Behavior Detection, Perimeter Intrusion Detection algorithm modules based on Gaussian Mixture Model and AdaBoost, optimized the system for multi-camera real-time video analysis. Implemented in C++ (MFC) and DirectX.


Selected Development Projects

UCBIR: image search engine

Developed UCbir web image search engine on Beowulf cluster. Integrate Heritrix as image crawler, PHP for feature extraction, MPI parallel computing with manager-worker model as backend archiving and similarity searching. Collected 30,000 images within , retrieval time less than 1 second for a query image. Scalable test performed on 18 nodes (36 cores).

  • Chunsheng Fang, Ryan Anderson, “A Parallel Implementation of Content©\Based Image Retrieval”, Parallel computing, 2008 Fall; [PDF]
  • Chunsheng Fang, Ryan Anderson, Anca Ralescu, "UCbir: Large-scale content based web image retrieval and tagging system", Excellence Award in ECECS Grad Symposium, 2008. [POSTER]

Human Motion Capture with Particle Swarm Optimation

How to robustly and efficiently map the human motion capture 3D passive sensor point clouds to a defined template skeleton? We utilize the Particle Swarm Optimization algorithm to solve this challenging problem. First we formulate this problem as a 3D rigid body registration problem; secondly the search space is pruned down from 6 to 3 by physical heuristics; Finally, the PSO multi-agents converge to the global optimal in the objective function in < 35 iterations done in 100ms.

  • Chunsheng Fang, Yunzheng He, and Michael Tolston, "Flying Sparrows Capture Poses: Using PSO to Map Appropriate Nomenclature to Motion Capture Markers",Complex System; Networks, 2010 Winter;[PDF]

Genome Wide Association DataBase for large-scale SNP visualization (GWADB@CCHMC)

SNP population stratification and visualization. Co-developed back-end job submission using Torque scheduler to perform PCA on HapMap Single Nucleotide Polymorphism microarray data. Frontend integrates AJAX and Google Web Toolkit for visualizing.(Internal web services. CCHMC VPN access required.)

Walking in a 3D world !

Final project for my Computer Graphics course 2008. Feature: First person viewpoint, real world texture mapping, dynamic 3D scene definition from a structured file, sound effects, natural viewpoint swaying when walking. [download]

Sequential Minimal Optimization for Kernel Support Vector Machine

Project in Kernel Method course, 2009. A matlab implementation for learning Kernel Support Vector Machine parameters using Sequential Minimal Optimization. [download]

Technologies for Sale:

Curb detection in 3D point cloud, fast and robustly

Project implemented in 2009. Given a 3D surveying point cloud dataset, my software can identify the curb structures in the scene, fast and robustly. Tested on various surveying datasets, optimized performance can finish scanning 5 million point cloud (real world scale: 200x100 ft) in < 1 second on a standard PC. Demo and pricing on request.



Best way to reach me is my Gmail : 'vicfcs'

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