Wenbin He
Research Scientist at Bosch
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News
01/2020. Our paper on visual analytics for deep reinforcement learning models has been accepted by PacificVis 2020.
07/2019. Our paper on deep learning based parameter space exploration of ensemble simulations won IEEE VIS 2019 Best Paper Award.
10/2018. Our paper on kernel density estimation of surfaces has been accepted by IEEE TVCG.
10/2018. Our paper on parallel reduction won IEEE LDAV 2018 Best Paper Honorable Mention Award.
10/2018. I'm going to present our paper on parallel reduction in IEEE LDAV 2018.
About
Wenbin He (何文彬) is a research scientist at Bosch. He received his Ph.D. degree from the Department of Computer Science and Engineering at The Ohio State University, advised by Prof. Han-Wei Shen. Before that, he received his B.Eng. degree in Software Engineering from Beijing Institute of Technology in 2012.
Research Interests
Broadly speaking, Wenbin He's research interests include data analysis and visualization, computer graphics, machine learning, and high performance computing. More specifically, his research mainly focuses on large-scale scientific data visualization, ensemble simulation data visualization, uncertainty quantification and visualization, and visual computing with machine learning.
Current Research Projects
Machine learning for data analysis and visualization
Visual analytics on machine learning models
Work Experience
Bosch
Research Scientist
Mar 2020 – Present
Human Machine Interaction, Visual Computing
The Ohio State University
Graduate Research Associate
May 2013 – December 2016, May 2017 - December 2019
Worked on exploration and analysis of ensemble datasets with statistical and deep learning models
Graduate Teaching Associate
January 2017 - May 2017
Teaching associate for CSE 5542 Real-Time Rendering and CSE 5544 Introduction to Data Visualization
Mitsubishi Electric Research Laboratories
Summer Intern
May 2019 – July 2019
Worked on developing visual analytics techniques to interpret and diagnose deep reinforcement learning models for robot control tasks, especially focusing on studying transfer failures from simulations to real robots
Argonne National Laboratory
Research Aide
May 2016 – August 2016
Worked on developing parallel reduction techniques to visualize and analyze extreme-scale datasets on supercomputers
Research Aide
May 2015 – July 2015
Worked on analyzing and visualizing the finite-time Lyapunov exponents and Lagrangian coherent structures of uncertain unsteady flows using statistical models
Publications
Wenbin He, Teng-Yok Lee, Jeroen van Baar, Kent Wittenburg, and Han-Wei Shen
DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies
In Proceedings of 2020 IEEE Pacific Visualization Symposium, 2020. (Accepted)
| DOI | PDF | Video |Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef S. G. Nashed, and Tom Peterka
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
IEEE Transactions on Visualization and Computer Graphics (SciVis 2019), vol. 26, no. 1, pp. 23-33, 2020. Best Paper Award
| DOI | PDF | Video | GitHub |Wenbin He, Hanqi Guo, Han-Wei Shen, and Tom Peterka
eFESTA: Ensemble Feature Exploration with Surface Density Estimates
IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 4, pp. 1716-1731, 2020.
| DOI | PDF | Video | GitHub |Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka
Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 9, pp. 2710-2724, 2019.
| DOI | PDF |Wenbin He, Hanqi Guo, Tom Peterka, Sheng Di, Franck Cappello, and Han-Wei Shen
Parallel Partial Reduction for Large-Scale Data Analysis and Visualization
In Proceedings of 2018 IEEE Symposium on Large Data Analysis and Visualization, pp. 45-55, 2018. Best Paper Honorable Mention
| DOI | PDF |Wenbin He, Xiaotong Liu, Han-Wei Shen, Scott M. Collis, and Jonathan J. Helmus
Range Likelihood Tree: A Compact and Effective Representation for Visual Exploration of Uncertain Data Sets
In Proceedings of 2017 IEEE Pacific Visualization Symposium, pp. 151–160, 2017.
| DOI | PDF | Video |Hanqi Guo, Wenbin He, Tom Peterka, Han-Wei Shen, Scott M. Collis, and Jonathan J. Helmus
Finite-Time Lyapunov Exponents and Lagrangian Coherent Structures in Uncertain Unsteady Flows
IEEE Transactions on Visualization and Computer Graphics (PacificVis 2016), vol. 22, no. 6, pp. 1672–1682, 2016.
| DOI | PDF |Wenbin He, Chun-Ming Chen, Xiaotong Liu, and Han-Wei Shen
A Bayesian Approach for Probabilistic Streamline Computation in Uncertain Flows
In Proceedings of 2016 IEEE Pacific Visualization Symposium, Visualization Notes, pp. 214–218, 2016.
| DOI | PDF |Ayan Biswas, David Thompson, Wenbin He, Qi Deng, Chun-Ming Chen, Han-Wei Shen, Raghu Machiraju, and Anand Rangarajan
An Uncertainty-Driven Approach to Vortex Analysis Using Oracle Consensus and Spatial Proximity
In Proceedings of 2015 IEEE Pacific Visualization Symposium, pp. 223–230, 2015.
| DOI | PDF |
Services
Reviewer
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2020
Journal of Visualization (JOV), 2017
IEEE VIS, 2018, 2019
EG/VGTC Conference on Visualization (EuroVis), 2018, 2020
IEEE Pacific Visualization Symposium (PacificVis), 2020
China Visualization and Visual Analytics Conference (ChinaVis), 2018, 2019