activity
20112026
most citedFiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

15 citations · 36 across the 16 of their papers we have counts for

collaborators

16 papers

cs.HC2026

RaivenTracks: Branching Provenance for Conversational Visualization Workflows

Ella Hugie, Alexandra Irger, Grace Guo +4

As AI agents increasingly participate in scientific workflows, scientists are shifting from direct authorship toward oversight, inspection, and steering. LLM-driven visualization s…

cs.HC2026

Raiven: LLM-Based Visualization Authoring via Domain-Specific Language Mediation

Alexandra Irger, Ella Hugie, Minghao Guo +5

Visualization is central to scientific discovery, yet authoring tools remain split between information and scientific visualization, and expertise in one rarely transfers to the ot…

cs.DC2024

Parallelize Over Data Particle Advection: Participation, Ping Pong Particles, and Overhead

Zhe Wang, Kenneth Moreland, Matthew Larsen +3

Particle advection is one of the foundational algorithms for visualization and analysis and is central to understanding vector fields common to scientific simulations. Achieving ef…

cs.HC2024

An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

Robert Sisneros, Tushar M. Athawale, David Pugmire +1

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the pro…

cs.GR2024

Uncertainty-Informed Volume Visualization using Implicit Neural Representation

Shanu Saklani, Chitwan Goel, Shrey Bansal +5

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive general…

cs.GR2024

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

Tushar M. Athawale, Zhe Wang, David Pugmire +5

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fund…