Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

ANTE - A Four-Tier Framework to
Boost Visual Literacy for High Dimensional Data

This material is based upon work supported by the US National Science Foundation under Grant No 1527200
Award ttitle: III: Small: Collaborative Research: ANTE - A Four-Tier Framework to Boost Visual Literacy for High Dimensional Data
Project director: Dr. Klaus Mueller, Professor, Computer Science Department, Stony Brook University
The award is in collaboration with project co-director: Dr.Kristina Striegnitz, Associate Professor, Computer Science Department, Union College under NSF Grant No 1527112. Efforts particular to the collaboration are reported on this webpage

The start date of the award was September 1, 2015 and the duration is 3 years until August 31, 2018 (estimated)

Project goals:
The overall mission of this proposal is to devise new interactive visualization paradigms to help mainstream users gain insight from complex highdimensional (ND) data. The tools we propose are formally anchored in a new framework and methodology we call ANTE. ANTE comprises the following four elements:
- Appeal (to innate visual literacy)
- Narrate (tell stories, explain)
- Transform (into innate visual literacy)
- Engage (attract, involve, interact)
in equal parts to raise the visual literacy of stake holders across the board – scientists, business people, consumers, etc. Our work makes ample use of machine learning to derive the visual results.

The bekow list are the papers that have been funded by this grant. Many items of this list lead to webpages (indcated by the blue information icons) that offer further detail in form of videos and presentaton files from invited talksgiven at conferences.The remaining items offfer author copies of the assocuated papers that were published.
Big Data Management with Incremental K-Means Trees–GPU-Accelerated Construction and Visualization
J. Wang. A. Zelenyuk, D, Imre, K. Mueller
(to appear) 2017
The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces
B. Wang, K. Mueller
IEEE Trans. on Visualization and Computer Graphics
(to appear) 2017
Graphoto: Aesthetically Pleasing Charts for Casual Information Visualization
J. Park, A. Kaufman, K. Mueller
IEEE Computer Graphics & Applications
(to appear) 2017
Visual Causality Analysis Made Practical
J. Wang, K. Mueller
IEEE Visual Analytics Science and Technology (VAST)
Phoenix, AZ, Ocober 2017
Visualization of Multivariate Data with Network Constraints using Multi-Objective Optimization
B. Ghai, A. Mishra, K. Mueller
EEE Visualization (Extended Abstracts)
Phoenix, AZ, Ocober 2017
Applying Multi-Player Rating Schemes to Manage User Studies of Visual Analytics Designs
S, Mahmood, K. Mueller
IEEE Visualization (Extended Abstracts)
Phoenix, AZ, Ocober 2017
Progressive Clustering of Big Data with GPU Acceleration and Visualization
J. Wang, E. Papenhausen. B. Wang. S. Ha, A. Zelenyuk, K. Mueller
New York Scientific Data Summit (NYSDS)
New York, NY, August 2017
Evolutionary Visual Analysis of Deep Neural Networks
W. Zhong. C. Xie, Y. Zhong, Y. Wang, W. Xu, S. Cheng, K. Mueller
Workshop on Visualization for Deep Learning (co-located with International Conference on Machine Learning, ICML)
Sidney, Austraila, August 2017
A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections
C. Xie, W. Zhong, K. Mueller
IEEE Trans. on Visualization and Computer Graphics (won an Honorary Mention Award)
23(1): 2017
Color Bands: Visualizing Dynamic Eye Movement Patterns
M. Burch, A. Kumar, K. Mueller, D. Weiskopf
Workshop on Eye Tracking and Visualization (ETVIS) (held in conjunction with IEEE VIS)
Baltimore, MD, 2016
Multi-Similarity Matrices of Eye Movement Data
A. Kumar, R. Netzel, M. Burch, D. Weiskopf, K. Mueller
Workshop on Eye Tracking and Visualization (ETVIS) (held in conjunction with IEEE VIS)
Baltimore, MD, 2016
Analyzing Hillary Clinton’s Emails
V.  Dehiya, K. Mueller
IEEE VIS Poster Abstracts
Baltimore, MD, 2016
Extending Scatterplots to Scalar Fields
S. Cheng, P. Cui, K. Mueller
IEEE VIS Poster Abstracts (won an Honorary Mention Award)
Baltimore, MD, 2016
Google Glass for Personalized Augmentations of Data Visualizations
D. Zhang, D. Coelho, K. Mueller
IEEE VIS Poster Abstracts
Baltimore, MD, 2016
A Data-Driven Approach for Mapping Multivariate Data to Color
S. Cheng, W. Xu. W. Zhong, K. Mueller
IEEE VIS Poster Abstracts
Baltimore, MD, 2016

In addition, we have also worked on broadening the use abd application of the developed machine learning tools and visual interfaces. Here we have focused on involving human users in tasks related to image processing and medical imaging. The related research artifacts are listed below.Some of the code was made publicly available on github where indicated.
A Look-Up Table-Based Ray Integration Framework for 2D/3D Forward and Back-projection in X-ray CT
S. Ha, K. Mueller
IEEE Transactions on Medical Imaging
(to appear), 2017
Adaptive Multispectral Demosaicking Based on Frequency Domain Analysis of Spectral Correlations
S. Jaiswal, L. Fung, V. Jakhetiya, J. Pang, K. Mueller, O. Au
IEEE Transactions on Image Processing
(to appear) 2017
Low-Dose CT Streak Artifacts Removal using Deep Residual Neural Networks
H. Li, K. Mueller
Fully 3D Image Reconstruction in Radiology and Nuclear Medicine
Xi'an, China, June 2017

There are several papers under review at journals and conferences. Once accepted they will be posted and llinked to here.

Software platforms for use and experimentation:
Visual Causality Analyst: a first prototype is available here
Similar interactive platforms for some of the other tools are currently under development and are expected to be operational soon.

Studies and presentations:
We have recently given a talk at Decision Camp 2016 entitled "The Decision Boundary Map: An Interactive Visual Interface to Make Informed Decisions and Selections in the Presence of Tradeoffs” and we are currently expanding on this initial effort.
The PI has also recently given an invited talk at Texas A&M Universty (among others) which serves as a good overview of the research outrcomes of this (and other) grants. This talk is available in this downloadable presentation file.

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Last update: 8/22/2017