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Klaus Mueller
Professor

Director, Visual Analytics and Imaging (VAI) Lab
Liaison, SUNY Korea CS Program

Center for Visual Computing
Computer Science Department
Stony Brook University - State University of New York

mueller{remove_this}@cs.stonybrook.edu

Quick Bio

I received a PhD in computer science from The Ohio State University in 1998. I am currently a professor in the Computer Science Department at Stony Brook University and I am also a senior scientist at the Computational Science Initiative at Brookhaven National Lab. From 2012-2015, I served as the founding chair of the Computer Science Department at SUNY Korea and I was also VP for Academic Affairs and Finance at SUNY Korea for two years. My current main research interests are visual analytics, explainable machine learning and AI, algorithmic fairness and transparency, data science and computational and medical imaging. I won the US National Science Foundation Early Career award in 2001, the SUNY Chancellor Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and the Golden Core Award of the IEEE Computer Society in 2016. In 2018 I was inducted into the US National Academy of Inventors. To date, I have authored more than 170 peer-reviewed journal and conference papers, which have been cited more than 11,000 times. I am a frequent speaker at international conferences, have organized or participated in 18 tutorials on various topics, chaired the IEEE Visualization Conference in 2009, and was the elected chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015. I currently serve as the Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics. I am a senior member of the IEEE. (Please see here for a bio in the third person and here is a real pic of me).

Research Interests
     
research word art Visual analytics -- Visualization, visual data science, visual storytelling, explainable AI, infovis, HCI
Computational fairness: Human in the loop bias detection, exploration, and mitigation
Computational imaging -- Computed tomography, low-dose, GAN-synthesis, GPU-acceleration
Volume visualization -- Medical and scientific visualization, multivalued data w/geo-reference
Virtual reality -- Virtual, augmented, mixed reality, display walls, immersive visualization
Cognitive computer graphics -- Color, texture, details, points, perception, cognition, semiotics
Filters and grids -- Sampling, hexagonal amd body-centerered lattices, extensions to N-D
Eyetracking -- Visualization for eye tracking data, acquisition, applications
Natural phenomena -- Simulation, urban security applications,GPU-acceleration
Face recognition (this is no longer an active research topic)
All my publications via Google Scholar

Recent Developments

 

 
I'm a member of the Stony Brook Institute for Advanced Computational Science (IACS) and its NSF-funded STRIDE program
  If you are a graduate student and interested in translating science into informed decision and policy-making consider joining STRDE
Stars of CSE 564 Spring 2021: Check out this playlist for videos of the best projects of this year's grad visualization course
Recent papers published or accepted:
  Our latest IEEE TVCG paper, on interpretabe machine learning and AI: Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making by Md. N. Hoque and K. Mueller
  Best Paper Runner-up Award at EuroVis 2020: Infomages: Embedding Data into Thematic Images with D. Coelho
  Guest Editorial in IEEE Trans. on Medical Imaging: Image Reconstruction Is a New Frontier of Machine Learning with G. Wang, J. Fessler, and J.C. Ye
  COVID-19 can tell us a lot about the causal relations in US socio-economics data. Read Using Demographic Pattern Analysis to Predict COVID-19 Fatalities on the US County Level by K. Mueller and E. Papenhausen in ACM Digital Government: Research and Practice
  Nutrition labels are still hard to read. Better visual representations can help. Read our VisComm 2020 paper: Eating with a Conscience: Toward a Visual and Contextual Nutrition Facts Label by D. Coelho, H. He, M. Baduk, K. Mueller
  Intersectional bias is a tremendous problem. We studied it here: WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings by B. Ghai, N. Hoque, and K. Mueller in ACM CHI 2021
Recently presented at ACM CSCW (Computer-Supported Cooperative Work and Social Computing):
  Making live video telecasts more balanced in gender and skin tone: Toward Interactively Balancing the Screen Time of Actors Based on Observable Phenotypic Traits in Live Telecast by N. Hoque, S. Billah, N. Squib, and K. Mueller
  Teach machines better and more effectively: Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers by B. Ghai, Q. V. Liao, Y. Zhang, R. Bellamy, and K. Mueller

Teaching Portfolio

 

     
CSE 332 Introduction to Visualization (undergraduate level) CSE 591 GPU Programming (Special Topics course)
CSE 377 Introduction to Medical Imaging (undergraduate level) CSE 577 Medical Imaging (graduate level)
CSE 323 Human Computer Interaction (co-taught at graduate level) CSE 523 Master's Projects (continued as CSE 524)
CSE 564 Visualization and Visual Analytics (graduate level) CSE 648 Visual Analytics Seminar (every semester)
CSE 590 Data Science Fundamentals (graduate level) More... Complete set of courses

Also known as Klaus Müller (German spelling)
http://www3.cs.stonybrook.edu/~mueller