picture of me

Jun Wang

PhD candidate at
Computer Science Deparment
Stony Brook University

About me

I am currently a fifth year Ph.D. student at Computer Science Department in Stony Brook University while working as a research assistant at the Visual Analytics and Imaging (VAI) Lab following Professor Klaus Mueller. I interned at VISA Research during the summer of 2016. Before coming to the US, I obtained both my Master degree in Computer Science and Bachelor degree in Software Engineering at Shandong University, China.

My research interests are in visual analytics, visualization, data mining, machine learning, and high-performance computing on GPUs.


Selected Projects

Causal Structure Investigator

Causal Structure Investigator

A comprehensive VA system for causality analysis, capable for analysing multiple models inhabiting in different data subdivisions.

Visual Causality Analyst

Visual Causality Analyst

A prototype VA framework for causality analysis, features interactive faciliteis for testing causal models derived by inference algorithms.

Spectra Miner

Spectra Miner

Analyzing hierarchical relations embedded in big data, equipped with parallel algorithms implemented on GPU for fast clustering and hierarchy building.

*Click the picture to know more about each project

Publications (Refereed)

Journal Papers

  • J. Wang, A. Zelenyuk, D. Imre, and K. Mueller, "Big Data Management with Incremental K-Means Trees–GPU-Accelerated Construction and Visualization," Informatics, vol. 4, no. 3, pp. 24, 2017 pdf / doi

  • J. Wang and K. Mueller, "The Visual Causality Analyst: An Interactive Interface for Causal Reasoning," IEEE Transaction on Visualization and Computer Graphics (VAST15), vol. 22, no. 1, pp. 230-239, 2016 pdf / talk / doi / website / video

  • A. Zelenyuk, D. Imre, J. Wilson, Z. Zhang, J. Wang, and K. Mueller, "Airborne Single Particle Mass Spectrometers (SPLAT II & miniSPLAT) and New Software for Data Visualization and Analysis in a Geo-Spatial Context," Journal of The American Society for Mass Spectrometry, vol. 26, no. 2, pp. 257-270, 2015 / doi

Conference Papers

  • J. Wang and K. Muller, "Visual Causality Analysis Made Practical," in IEEE Proc. Visual Analytics Science and Technology (VAST17), Pheonix, AZ, Oct. 2017 pdf / talk / website / video

  • J. Wang, E. Papenhausen, B. Wang, S. Ha, A. Zelenyuk, and K. Mueller, "Progressive Clustering of Big Data with GPU Acceleration and Visualization," in IEEE Proc. New York Scientific Data Summit (NYSDS17), New York, Aug. 2017 pdf / talk / doi

  • J. Wang, F. Zhong, G. Wang, Q. Peng, and X. Qin, “Visual Tracking via Subspace Motion Model,” in Proc. British Machine Vision Conference (BMVC13), Bristol, UK, Sept. 2013 pdf

Poster and Workshop Papers

  • J. Wang, "Visual Causality Analysis", in IEEE VIS Doctoral Colloquium, Pheonix, AZ, Oct. 2017 pdf

  • S. Cheng, B. Wang, W. Zhong, C. Xie, S. Mahmood, J. Wang, Klaus Mueller, "Model-driven Visual Analytics for Big Data," in New York Scientific Data Summit (NYSDS16), New York, Aug. 2016 / doi