About

Klaus Mueller is a Professor and Interim Chair in the Department of Computer Science at Stony Brook University. He holds a B.S. in Electrical Engineering from the Polytechnic University of Ulm, Germany, and an M.S. in Biomedical Engineering along with a Ph.D. in Computer Science from The Ohio State University. His research interests encompass visualization, visual analytics, data science, big data, virtual and augmented reality, medical imaging, and high-performance computing on GPUs. Mueller's work is supported by various agencies including NSF, NIH, DOE, DHS, and private industry, reflecting his active engagement in advancing computational and visualization technologies. He has authored over 160 peer-reviewed journal and conference papers, which have been cited more than 4,500 times. Mueller also holds adjunct faculty positions at the Biomedical Engineering and Radiology Departments and is an adjunct scientist at the Computational Science Center at Brookhaven National Laboratory. His contributions to the field have been recognized through awards such as the NSF CAREER award in 2001, the SUNY Chancellor Award for Excellence in Scholarship and Creative Activity in 2011, and his role as chair of the IEEE Technical Committee on Visualization and Computer Graphics. Additionally, he serves as an associate editor for IEEE Transactions on Visualization and Computer Graphics and is a senior member of IEEE.

Research topics

• Physics

Selected publications

• The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

  arXiv (Cornell University) · 2026-04-16

  preprintOpen access

  This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.

  PublisherDOI

• The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

  ArXiv.org · 2026-04-16

  articleOpen access

  This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.

  PublisherOA PDF

• <scp>FairPlay:</scp> A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 3 citations

  articleSenior author

  The issue of fairness in decision-making is a critical one, especially given the variety of stakeholder demands for differing and mutually incompatible versions of fairness. Adopting a strategic interaction of perspectives provides an alternative to enforcing a singular standard of fairness. We present a web-based software application, FairPlay, that enables multiple stakeholders to debias datasets collaboratively. With FairPlay, users can negotiate and arrive at a mutually acceptable outcome without a universally agreed-upon theory of fairness. In the absence of such a tool, reaching a consensus would be highly challenging due to the lack of a systematic negotiation process and the inability to modify and observe changes. We have conducted user studies that demonstrate the success of FairPlay, as users could reach a consensus within about five rounds of gameplay, illustrating the application's potential for enhancing fairness in AI systems.

  PublisherDOI

• What is the Color of Serendipity? Investigating the Use of Language Models for Semantically Resonant Color Generation

  IEEE Transactions on Visualization and Computer Graphics · 2025-11-21

  articleSenior author

  Humans inherently connect certain colors with particular concepts in semantically meaningful ways that facilitate visual communication. These colors are known as semantically resonant colors. For instance, we associate "sky" and "ocean" with shades of blue, and "cherry" with red. In this paper, we investigate how language models, including Word2Vec, RoBERTa, GPT-4o mini and the vision language model CLIP generate and represent nuanced semantically resonant colors for diverse concepts. To achieve this, we utilized a large dataset of color names and concepts, tailored models for the structure of each language model, and developed an interactive web interface, Concept2Color, as a use case. Additionally, we conducted experiments and a detailed analysis to assess the ability of these models to generate meaningful colors. Through these experiments, we examined how factors such as model design, training data and context affect the color output. Our findings reveal the capabilities and limitations of language models in processing and generating semantically resonant colors for concepts, thus contributing insights into how they depict semantic color-concept connections. These insights have implications for data visualization, design, and human-computer interaction, where leveraging effective semantic color generation can enhance communication and user experience.

  PublisherDOI

• Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework

  ArXiv.org · 2025-01-23 · 1 citations

  preprintOpen access

  We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality - AR, VR, MR - transitions, multi-user collaborative application scenarios, and the complexity of multimodal data. Explainable XR addresses these challenges by providing a virtuality-agnostic solution for the collection, analysis, and visualization of immersive sessions. We propose three main components in our framework: (1) A novel user data recording schema, called User Action Descriptor (UAD), that can capture the users' multimodal actions, along with their intents and the contexts; (2) a platform-agnostic XR session recorder, and (3) a visual analytics interface that offers LLM-assisted insights tailored to the analysts' perspectives, facilitating the exploration and analysis of the recorded XR session data. We demonstrate the versatility of Explainable XR by demonstrating five use-case scenarios, in both individual and collaborative XR applications across virtualities. Our technical evaluation and user studies show that Explainable XR provides a highly usable analytics solution for understanding user actions and delivering multifaceted, actionable insights into user behaviors in immersive environments.

  PublisherOA PDFDOI

• CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models

  IEEE Transactions on Visualization and Computer Graphics · 2025-08-25 · 3 citations

  articleSenior author

  Causal networks are widely used in many fields to model the complex relationships between variables. A recent approach has sought to construct causal networks by leveraging the wisdom of crowds through the collective participation of humans. While this can yield detailed causal networks that model the underlying phenomena quite well, it requires a large number of individuals with domain understanding. We adopt a different approach: leveraging the causal knowledge that large language models, such as OpenAI's GPT-4, have learned by ingesting massive amounts of literature. Within a dedicated visual analytics interface, called CausalChat, users explore single variables or variable pairs recursively to identify causal relations, latent variables, confounders, and mediators, constructing detailed causal networks through conversation. Each probing interaction is translated into a tailored GPT-4 prompt and the response is conveyed through visual representations which are linked to the generated text for explanations. We demonstrate the functionality of CausalChat across diverse data contexts and conduct user studies involving both domain experts and laypersons.

  PublisherDOI

• XplainAct: Visualization for Personalized Intervention Insights

  ArXiv.org · 2025-07-19

  articleOpen accessSenior author

  Causality helps people reason about and understand complex systems, particularly through what-if analyses that explore how interventions might alter outcomes. Although existing methods embrace causal reasoning using interventions and counterfactual analysis, they primarily focus on effects at the population level. These approaches often fall short in systems characterized by significant heterogeneity, where the impact of an intervention can vary widely across subgroups. To address this challenge, we present XplainAct, a visual analytics framework that supports simulating, explaining, and reasoning interventions at the individual level within subpopulations. We demonstrate the effectiveness of XplainAct through two case studies: investigating opioid-related deaths in epidemiology and analyzing voting inclinations in the presidential election.

  PublisherOA PDF

• NTIRE 2025 Challenge on Image Super-Resolution ($\times 4$): Methods and Results

  2025-06-11 · 1 citations

  article

  This paper presents the NTIRE 2025 image super-resolution (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times 4$</tex>) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025. The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times 4$</tex> scaling factor. The objective is to develop effective network designs or solutions that achieve state-of-the-art SR performance. To reflect the dual objectives of image SR research, the challenge includes two sub-tracks: (1) a restoration track, emphasizes pixel-wise accuracy and ranks submissions based on PSNR; (2) a perceptual track, focuses on visual realism and ranks results by a perceptual score. A total of 286 participants registered for the competition, with 25 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, the main results, and methods of each team. The challenge serves as a benchmark to advance the state of the art and foster progress in image SR.

  PublisherDOI

• Guided synthesis of annotated lung CT images with pathologies using a multi-conditioned denoising diffusion probabilistic model (mDDPM)

  Physics in Medicine and Biology · 2025-02-24 · 1 citations

  articleOpen accessSenior author

  Abstract Objective . The training of AI models for medical image diagnostics requires highly accurate, diverse, and large training datasets with annotations and pathologies. Unfortunately, due to privacy and other constraints the amount of medical image data available for AI training remains limited, and this scarcity is exacerbated by the high overhead required for annotation. We address this challenge by introducing a new controlled framework for the generation of synthetic images complete with annotations, incorporating multiple conditional specifications as inputs. Approach . Using lung CT as a case study, we employ a denoising diffusion probabilistic model to train an unconditional large-scale generative model. We extend this with a classifier-free sampling strategy to develop a robust generation framework. This approach enables the generation of constrained and annotated lung CT images that accurately depict anatomy, successfully deceiving experts into perceiving them as real. Most notably, we demonstrate the generalizability of our multi-conditioned sampling approach by producing images with specific pathologies, such as lung nodules at designated locations, within the constrained anatomy. Main results . Our experiments reveal that our proposed approach can effectively produce constrained, annotated and diverse lung CT images that maintain anatomical consistency and fidelity, even for annotations not present in the training datasets. Moreover, our results highlight the superior performance of controlled generative frameworks of this nature compared to nearly every state-of-the-art image generative model when trained on comparable large medical datasets. Finally, we highlight how our approach can be extended to other medical imaging domains, further underscoring the versatility of our method. Significance . The significance of our work lies in its robust approach for generating synthetic images with annotations, facilitating the creation of highly accurate and diverse training datasets for AI applications and its wider applicability to other imaging modalities in medical domains. Our demonstrated capability to faithfully represent anatomy and pathology in generated medical images holds significant potential for various medical imaging applications, with high promise to lead to improved diagnostic accuracy and patient care.

  PublisherOA PDFDOI

• FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness

  ArXiv.org · 2025-04-22

  preprintOpen accessSenior author

  The issue of fairness in decision-making is a critical one, especially given the variety of stakeholder demands for differing and mutually incompatible versions of fairness. Adopting a strategic interaction of perspectives provides an alternative to enforcing a singular standard of fairness. We present a web-based software application, FairPlay, that enables multiple stakeholders to debias datasets collaboratively. With FairPlay, users can negotiate and arrive at a mutually acceptable outcome without a universally agreed-upon theory of fairness. In the absence of such a tool, reaching a consensus would be highly challenging due to the lack of a systematic negotiation process and the inability to modify and observe changes. We have conducted user studies that demonstrate the success of FairPlay, as users could reach a consensus within about five rounds of gameplay, illustrating the application's potential for enhancing fairness in AI systems.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21EB004099

  NIH · $405k · 2008

• CGV: Small: Illustration Inspired Visualization: A Gateway to Interacting with High-Dimensional Data

  NSF · $500k · 2011–2015

• Point-Based and Image-Based Volumetric Rendering and Detail Modeling For Volume Graphics

  NSF · $373k · 2001–2007

• Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging

  NIH · $2.5M · 2022–2026

• III: Small: Collaborative Research: ANTE - A Four-Tier Framework to Boost Visual Literacy for High Dimensional Data

  NSF · $435k · 2015–2019

Frequent coauthors

• Robert Moorhead

  269 shared

• Miriah Meyer

  264 shared

• Aditi Majumder

  University of California, Irvine

  263 shared

• Hanspeter Pfister

  Harvard University

  237 shared

• Amitabh Varshney

  University of Maryland, College Park

  234 shared

• Vice Chair

  University of Utah

  233 shared

• James Ahrens

  Los Alamos National Laboratory

  169 shared

• Arie Kaufman

  161 shared

Education

• PhD, Computer Science

  Ohio State University

  1998

Awards & honors

• NSF CAREER award in 2001
• SUNY Chancellor Award for Excellence in Scholarship and Crea…
• Stony Brook University Undergraduate College Faculty Fellow