Klaus Mueller

Klaus Mueller is affiliated with Stony Brook University in the United States and has contributed extensively to the field of computer science, with a focus on computer vision and pattern recognition, artificial intelligence, and related subfields. The scientist's research encompasses several key areas, including data visualization and analytics, explainable artificial intelligence (XAI), ethics and social impacts of AI, generative adversarial networks and image synthesis, video analysis and summarization, and machine learning techniques.

The recent publications by Klaus Mueller demonstrate involvement in interdisciplinary and technologically advanced topics. Notable papers include "Development of metaverse for intelligent healthcare" (2022, Nature Machine Intelligence), which explores the intersection of virtual environments and healthcare applications. Other significant works are "Explainable Active Learning (XAL)" (2021, Proceedings of the ACM on Human-Computer Interaction), "D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias" (2022, IEEE Transactions on Visualization and Computer Graphics), "Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making" (2021, IEEE Transactions on Visualization and Computer Graphics), and "Networks never rest: An investigation of network evolution in three species of animals" (2021, Social Networks).

Throughout the career, Klaus Mueller has frequently collaborated with several coauthors, including Bhavya Ghai, Ge Wang, Darius Coelho, Anjul Tyagi, and Heyi Li. These partnerships have resulted in multiple publications, indicating ongoing research cooperation and shared contributions in the relevant scientific communities.

The scientist's work has been published majorly in IEEE Transactions on Visualization and Computer Graphics, accounting for 31 publications, followed by extensive contributions to arXiv (Cornell University) with 28 publications, Proceedings of the ACM on Human-Computer Interaction with 4 publications, as well as Computer Graphics Forum and ACM Symposium on Eye Tracking Research and Applications with 2 publications each.

Klaus Mueller's main fields and subfields of study are outlined as follows:

• Computer Science
• Computer Vision and Pattern Recognition
• Artificial Intelligence
• Safety Research
• Radiology, Nuclear Medicine and Imaging
• Information Systems

The primary research topics focus on:

• Data Visualization and Analytics
• Explainable Artificial Intelligence (XAI)
• Ethics and Social Impacts of AI
• Generative Adversarial Networks and Image Synthesis
• Video Analysis and Summarization
• Machine Learning and Data Classification
• Machine Learning and Algorithms