Aritra Dasgupta



Visualization Researcher
School of Engineering
New York University
dasgupta.aritra@gmail.com

Research Interests: Information Visualization, Visual Analytics, & Human-Computer Interaction

Recent talks: VAST 2014, EHRVis Workshop 2014, EuroVis 2014, Invited Talk at Rutgers University (June 2014)

Career timeline:
- Research Assistant Professor,(Nov 2014-) NYU
-Postdoctoral Researcher (Nov 2012 - Oct 2014), NYU
-PhD., Computing and Information Systems (Fall 2012), UNC Charlotte

Similarity analysis of multifaceted, multidimensional data objects is a complex problem. How to enable domain experts in transparent visual comparison of similarity from different perspectives?
We have developed techniques that enable climate scientists to explore models at different scales of space and time, and reconcile their similarity with respect to diverse facets, such as binary features and spatiotemporal output variables .

High-dimensional data exploration involves visual search for patterns among different combinations of dimensions. Can visualization systems offer guidance to analysts for finding those patterns?
By devising metrics that quantify salient patterns & subspace clusters in the high-dimensional data space, we are able to provide guidance to the analysts for discovering and exploring these patterns further.

Analytical interpretations are affected by the optimality of visualization design. How do we quantify the impact of design choices and trade-offs in visualizations?
 

A design problem taxonomy that helps map analysts' intents and tasks to design trade-offs in visualization, and thereby impact the choice of an optimal encoding strategy, that will efficiently communicate the information to the target audience.


Handling sensitive data is a problem in real-world applications. How can we control information loss on screen to constrain visual representations and enable privacy-preserving visual analysis?
 

Privacy-preserving visualization adapts to different attack scenarios that may lead to privacy breach by using different metrics, which also help optimize the loss of utility due to data anonymization.


I am a Research Assistant Professor at the NYU Polytechnic School of Engineering, working with Prof. Enrico Bertini and Prof. Claudio Silva on visualization and visual analytics related research projects. Previously, I was a Postdoctoral Research Fellow working on projects related to climate data visualization and high-dimensional data analysis as part of the NSF funded DataONE initiative at NYU.

My research interests lie at the cross-cutting areas of information visualization, visual analytics and human-computer interaction. In particular, I am interested in leveraging perceptually motivated models and designs for building exploratory visual analysis technques and systems that help domain experts make data-driven decisions.

CV         Dissertation Thesis        
Peer Reviewed Publications ( j: journal, c: conference proceedings )
Interacting with Complex Information Spaces Visualization Systems and Techniques for Exploration of
Multifaceted, High-dimensional Data.

new j8 VIMTEX: A Visualization Interface for Multivariate, Time-Varying Geological Data Exploration, Computer Graphics Forum (EuroVis) 2015, in publication paper

j6 Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling, IEEE Transactions on Visualization and Computer Graphics (VAST) 2014 paper

j5  SimilarityExplorer: A Visual Inter-Comparison Tool for Multifaceted Climate Data, vol. 33, Issue 3, pp. 341-350, Computer Graphics Forum (EuroVis) 2014. paper

j1 Pargnostics: Screen-Space Metrics for Parallel Coordinates, IEEE Transactions on Visualization and Computer Graphics, (InfoVis) 2010, vol. 16, no. 6, pp. 1017-1026 paper

c1 Meta Parallel Coordinates For Visualizing Features in Large, High-Dimensional, Time-Varying Data, IEEE Symposium on Large Data Analysis and Visualization, pp. 85-89, 2012. paper
Perceptually Aware Visual Analysis
Models and Studies for Optimal Visual Representations of Data

new j7 Bridging Theory With Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison, in publication, IEEE Transactions of Visualization and Computer Graphics, 2015. paper

j3 Conceptualizing Visual Uncertainty in Parallel Coordinates, Computer Graphics Forum, (EuroVis), vol. 31, no. 3pt2, pp. 1015-1024, 2012. paper

c2 The Importance of Tracing Data Through the Visualization Pipeline, Beyond Time and Errors-Novel Evaluation Methods for Visualization (BELIV), 2012. paper

c3 The Need for Information Loss Metrics in Visualization, Workshop on The Role of Theory in Information Visualization, VisWeek 2010. paper
Privacy-Preserving Visualization
Adaptive Visual Representations and Interactions for Protecting Sensitive Attributes Against Attack Scenarios

new c4 Opportunities and Challenges for Privacy-Preserving Visualization of Electronic Health Record Data, In Proceedings, IEEE VIS 2014 Workshop Electronic Health Records Visualization paper

j4 Measuring Privacy and Utility in Privacy-Preserving Visualization”, Computer Graphics Forum, vol. 32, no.8, pp. 35-47, 2013. paper

j2 Adaptive Privacy-Preserving Visualization Using Parallel Coordinates, IEEE Transactions on Visualization and Computer Graphics, (InfoVis), vol. 17, no. 7, pp. 2241-2248, 2011. paper

c5 Privacy-Preserving Data Visualization Using Parallel Coordinates, In Proceedings, Visualization and Data Analysis, vol. 7868 pp. 78680O-1-78680O-12, 2011. paper