Evaluating graph coloring on GPUs. - Andre Vincent Pascal Grosset; Peihong Zhu; Shusen Liu 001; Suresh Venkatasubramanian; Mary W. Hall (PPoPP, 2011)

Feature-Based Statistical Analysis of Combustion Simulation Data. - Janine Bennett; Vaidyanathan Krishnamoorthy; Shusen Liu 001; Ray W. Grout; Evatt R. Hawkes; Jacqueline H. Chen; Jason Shepherd; Valerio Pascucci; Peer-Timo Bremer (IEEE Trans. Vis. Comput. Graph., 2011)

Gaussian mixture model based volume visualization. - Shusen Liu 001; Joshua A. Levine; Peer-Timo Bremer; Valerio Pascucci (LDAV, 2012)

Multivariate volume visualization through dynamic projections. - Shusen Liu 001; Bei Wang 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Valerio Pascucci (LDAV, 2014)

Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data. - Shusen Liu 001; Bei Wang 001; Peer-Timo Bremer; Valerio Pascucci (Comput. Graph. Forum, 2014)

Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections. - Shusen Liu 001; Bei Wang 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Valerio Pascucci (Comput. Graph. Forum, 2015)

Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization. - Cameron Christensen; Ji-Woo Lee; Shusen Liu 001; Peer-Timo Bremer; Giorgio Scorzelli; Valerio Pascucci (LDAV, 2016)

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. - Shusen Liu 001; Peer-Timo Bremer; J. J. Jayaraman; Bei Wang 001; Brian Summa; Valerio Pascucci (Comput. Graph. Forum, 2016)

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study. - Dan Maljovec; Shusen Liu 001; Bei Wang 001; Diego Mandelli; Peer-Timo Bremer; Valerio Pascucci; Curtis L. Smith (Reliab. Eng. Syst. Saf., 2016)

Visualizing High-Dimensional Data: Advances in the Past Decade. - Shusen Liu 001; Dan Maljovec; Bei Wang 001; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2017)

Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections. - Shusen Liu 001 (2017)

Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension. - Shusen Liu 001; Tao Li 039; Zhimin Li; Vivek Srikumar; Valerio Pascucci; Peer-Timo Bremer (EMNLP (Demonstration), 2018)

Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections. - Jayaraman J. Thiagarajan; Shusen Liu 001; Karthikeyan Natesan Ramamurthy; Peer-Timo Bremer (Comput. Graph. Forum, 2018)

Visual Exploration of Semantic Relationships in Neural Word Embeddings. - Shusen Liu 001; Peer-Timo Bremer; Jayaraman J. Thiagarajan; Vivek Srikumar; Bei Wang 001; Yarden Livnat; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2018)

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. - Sam Ade Jacobs; Jim Gaffney; Tom Benson; Peter B. Robinson; J. Luc Peterson; Brian K. Spears; Brian Van Essen; David Hysom; Jae-Seung Yeom; Tim Moon; Rushil Anirudh; Jayaraman J. Thiagarajan; et al. (CLUSTER, 2019)

Generative Counterfactual Introspection for Explainable Deep Learning. - Shusen Liu 001; Bhavya Kailkhura; Donald Loveland; Yong Han (GlobalSIP, 2019)

Function Preserving Projection for Scalable Exploration of High-Dimensional Data. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CoRR, 2019)

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion. - Rushil Anirudh; Jayaraman J. Thiagarajan; Shusen Liu 001; Peer-Timo Bremer; Brian K. Spears (CoRR, 2019)

NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models. - Shusen Liu 001; Zhimin Li; Tao Li 039; Vivek Srikumar; Valerio Pascucci; Peer-Timo Bremer (IEEE Trans. Vis. Comput. Graph., 2019)

Actionable Attribution Maps for Scientific Machine Learning. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. - Anna M. Hiszpanski; Brian Gallagher; Karthik Chellappan; Peggy Li; Shusen Liu 001; Hyojin Kim 001; Jinkyu Han; Bhavya Kailkhura; David J. Buttler; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2020)

Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (Mach. Learn. Sci. Technol., 2020)

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. - Shusen Liu 001; Jim Gaffney; J. Luc Peterson; Peter B. Robinson; Harsh Bhatia; Valerio Pascucci; Brian K. Spears; Peer-Timo Bremer; Di Wang; Dan Maljovec; Rushil Anirudh; Jayaraman J. Thiagarajan; et al. (IEEE Trans. Vis. Comput. Graph., 2020)

Reliable Graph Neural Network Explanations Through Adversarial Training. - Donald Loveland; Shusen Liu 001; Bhavya Kailkhura; Anna M. Hiszpanski; Yong Han (CoRR, 2021)

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations. - Harsh Bhatia; Timothy S. Carpenter; Helgi I. Ingólfsson; Gautham Dharuman; Piyush Karande; Shusen Liu 001; Tomas Oppelstrup; Chris Neale; Felice C. Lightstone; Brian Van Essen; James N. Glosli; Peer-Timo Bremer (Nat. Mach. Intell., 2021)

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems. - Zhimin Li; Harshitha Menon; Dan Maljovec; Yarden Livnat; Shusen Liu 001; Kathryn M. Mohror; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2021)

Sparsity Improves Unsupervised Attribute Discovery in Stylegan. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (ICASSP, 2022)

Interactively Assessing Disentanglement in GANs. - Sangwon Jeong; Shusen Liu 001; Matthew Berger (Comput. Graph. Forum, 2022)

"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (CoRR, 2022)

On-the-fly Object Detection using StyleGAN with CLIP Guidance. - Yuzhe Lu; Shusen Liu 001; Jayaraman J. Thiagarajan; Wesam Sakla; Rushil Anirudh (CoRR, 2022)

Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences Between Pretrained Generative Models. - Matthew L. Olson; Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Weng-Keen Wong (CVPR, 2023)

Concept Lens: Visually Analyzing the Consistency of Semantic Manipulation in GANs. - Sangwon Jeong; Mingwei Li; Matthew Berger; Shusen Liu 001 (IEEE VIS (Short Papers), 2023)

Instance-wise Linearization of Neural Network for Model Interpretation. - Zhimin Li; Shusen Liu 001; Bhavya Kailkhura; Timo Bremer; Valerio Pascucci (CoRR, 2023)

Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data. - Matthew L. Olson; Shusen Liu 001; Jayaraman J. Thiagarajan; Bogdan Kustowski; Weng-Keen Wong; Rushil Anirudh (CoRR, 2023)

RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models. - Gabriel J. Perin; Xuxi Chen; Shusen Liu 001; Bhavya Kailkhura; Zhangyang Wang; Brian Gallagher (ACL (Findings), 2024)

The Visualization JUDGE: Can Multimodal Foundation Models Guide Visualization Design Through Visual Perception? - Matthew Berger; Shusen Liu 001 (BELIV, 2024)

Text-based transfer function design for semantic volume rendering. - Sangwon Jeong; Jixian Li; Chris R. Johnson 001; Shusen Liu 001; Matthew Berger (IEEE VIS, 2024)

CAN: Concept-Aligned Neurons for Visual Comparison of Deep Neural Network Models. - Mingwei Li; Sangwon Jeong; Shusen Liu 001; Matthew Berger (Comput. Graph. Forum, 2024)

AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making. - Shusen Liu 001; Haichao Miao; Zhimin Li; Matthew L. Olson; Valerio Pascucci; Peer-Timo Bremer (Comput. Graph. Forum, 2024)

Text-based Transfer Function Design for Semantic Volume Rendering. - Sangwon Jeong; Jixian Li; Christopher Johnson; Shusen Liu 001; Matthew Berger (CoRR, 2024)

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. - Hongjun Choi; Jayaraman J. Thiagarajan; Ruben Glatt; Shusen Liu 001 (CoRR, 2024)

Visualization Literacy of Multimodal Large Language Models: A Comparative Study. - Zhimin Li; Haichao Miao; Valerio Pascucci; Shusen Liu 001 (CoRR, 2024)

A Visual Comparison of Silent Error Propagation. - Zhimin Li; Harshitha Menon; Kathryn M. Mohror; Shusen Liu 001; Luanzheng Guo; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2024)

See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs. - Zhimin Li; Haichao Miao; Xinyuan Yan; Valerio Pascucci; Matthew Berger; Shusen Liu 001 (CoRR, 2025)

ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use. - Shusen Liu 001; Haichao Miao; Peer-Timo Bremer (CoRR, 2025)

VERIRAG: Healthcare Claim Verification via Statistical Audit in Retrieval-Augmented Generation. - Shubham Mohole; Hongjun Choi; Shusen Liu 001; Christine Klymko; Shashank Kushwaha; Derek Shi; Wesam Sakla; Sainyam Galhotra; Ruben Glatt (CoRR, 2025)

"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2025)