Michael E. Papka

selected publications

1. Andrew E. Johnson, Luc Renambot, G. Elisabeta Marai, Daria Tsoupikova, Michael E. Papka, Lance Long, Dana Plepys, Jonas Talandis, Maxine D. Brown, Jason Leigh, Daniel J. Sandin, and Thomas A. DeFanti, Electronic Visualization Laboratory’s 50th Anniversary Retrospective: Look to the Future, Build on the Past, PRESENCE: Virtual and Augmented Reality,Apr, 2024

   Abstract BibTeX

   September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL’s introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL’s culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person’s expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL’s past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

   @article{JoReMa24,
     title = {{Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past}},
     author = {Johnson, Andrew E. and Renambot, Luc and Marai, G. Elisabeta and Tsoupikova, Daria and Papka, Michael E. and Long, Lance and Plepys, Dana and Talandis, Jonas and Brown, Maxine D. and Leigh, Jason and Sandin, Daniel J. and DeFanti, Thomas A.},
     year = {2024},
     month = apr,
     journal = {PRESENCE: Virtual and Augmented Reality},
     pages = {1--51},
     doi = {10.1162/pres_a_00421},
     issn = {1054-7460},
     url = {https://doi.org/10.1162/pres_a_00421},
   }

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2. Ratanond Koonchanok, Michael E. Papka, and Khairi Reda, Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations, IEEE Transactions on Visualization and Computer Graphics, 2024

   Abstract BibTeX

   People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making.

   @article{KoPaRe24,
     title = {Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations},
     author = {Koonchanok, Ratanond and Papka, Michael E. and Reda, Khairi},
     year = {2024},
     journal = {IEEE Transactions on Visualization and Computer Graphics},
     pages = {1--11},
     doi = {10.1109/tvcg.2024.3456182},
     keywords = {Bayes methods;Data visualization;Uncertainty;Correlation;Accuracy;Decision making;Biological system modeling;Visual inference;statistical rationality;human-machine collaboration},
   }

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3. Victor A. Mateevitsi, Michael E. Papka, and Khairi Reda, Science in a Blink: Supporting Ensemble Perception in Scalar Fields , In 2024 IEEE Visualization and Visual Analytics (VIS),Oct, 2024

   Abstract BibTeX

   Visualizations support rapid analysis of scientific datasets, allowing viewers to glean aggregate information (e.g., the mean) within split-seconds. While prior research has explored this ability in conventional charts, it is unclear if spatial visualizations used by computational scientists afford a similar ensemble perception capacity. We investigate people’s ability to estimate two summary statistics, mean and variance, from pseudocolor scalar fields. In a crowd- sourced experiment, we find that participants can reliably characterize both statistics, although variance discrimination requires a much stronger signal. Multi-hue and diverging colormaps outperformed monochromatic, luminance ramps in aiding this extraction. Analysis of qualitative responses suggests that participants often estimate the distribution of hotspots and valleys as visual proxies for data statistics. These findings suggest that people’s summary interpretation of spatial datasets is likely driven by the appearance of discrete color segments, rather than assessments of overall luminance. Implicit color segmentation in quantitative displays could thus prove more useful than previously assumed by facilitating quick, gist- level judgments about color-coded visualizations.

   @inproceedings{MaPaKh24,
     title = {{ Science in a Blink: Supporting Ensemble Perception in Scalar Fields }},
     author = {Mateevitsi, Victor A. and Papka, Michael E. and Reda, Khairi},
     year = {2024},
     month = oct,
     booktitle = {2024 IEEE Visualization and Visual Analytics (VIS)},
     publisher = {IEEE Computer Society},
     address = {Los Alamitos, CA, USA},
     pages = {216--220},
     doi = {10.1109/vis55277.2024.00051},
     url = {https://doi.IEEEcomputersociety.org/10.1109/VIS55277.2024.00051},
     keywords = {Image color analysis;Visual analytics;Aggregates;Data visualization;Spatial databases;Reliability},
   }

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4. Amey Salvi, Kecheng Lu, Michael E. Papka, Yunhai Wang, and Khairi Reda, Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps, In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

   Abstract BibTeX

   Quantitative data is frequently represented using color, yet designing effective color mappings is a challenging task, requiring one to balance perceptual standards with personal color preference. Current design tools either overwhelm novices with complexity or offer limited customization options. We present ColorMaker, a mixed-initiative approach for creating colormaps. ColorMaker combines fluid user interaction with real-time optimization to generate smooth, continuous color ramps. Users specify their loose color preferences while leaving the algorithm to generate precise color sequences, meeting both designer needs and established guidelines. ColorMaker can create new colormaps, including designs accessible for people with color-vision deficiencies, starting from scratch or with only partial input, thus supporting ideation and iterative refinement. We show that our approach can generate designs with similar or superior perceptual characteristics to standard colormaps. A user study demonstrates how designers of varying skill levels can use this tool to create custom, high-quality colormaps. ColorMaker is available at: colormaker.org

   @inproceedings{SaLuPa24,
     title = {Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps},
     author = {Salvi, Amey and Lu, Kecheng and Papka, Michael E. and Wang, Yunhai and Reda, Khairi},
     year = {2024},
     booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},
     location = {Honolulu, HI, USA},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     series = {Chi '24},
     doi = {10.1145/3613904.3642265},
     isbn = {9798400703300},
     url = {https://doi.org/10.1145/3613904.3642265},
     articleno = {145},
     keywords = {Mixed-initiative systems, color design, colormaps, simulated annealing.},
     numpages = {17},
   }

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5. Boyang Li, Yuping Fan, Michael E. Papka, and Zhiling Lan, Encoding for Reinforcement Learning Driven Scheduling, In Job Scheduling Strategies for Parallel Processing, 2023

   Abstract BibTeX

   Reinforcement learning (RL) is exploited for cluster scheduling in the field of high-performance computing (HPC). One of the key challenges for RL driven scheduling is state representation for RL agent (i.e., capturing essential features of dynamic scheduling environment for decision making). Existing state encoding approaches either lack critical scheduling information or suffer from poor scalability. In this study, we present SEM (Scalable and Efficient encoding Model) for general RL driven scheduling in HPC. It captures system resource and waiting job state, both being critical information for scheduling. It encodes these pieces of information into a fixed-sized vector as an input to the agent. A typical agent is built on deep neural network, and its training/inference cost grows exponentially with the size of its input. Production HPC systems contain a large number of computer nodes. As such, a direct encoding of each of the system resources would lead to poor scalability of the RL agent. SEM uses two techniques to transform the system resource state into a small-sized vector, hence being capable of representing a large number of system resources in a vector of 100–200. Our trace-based simulations demonstrate that compared to the existing state encoding methods, SEM can achieve 9X training speedup and 6X inference speedup while maintaining comparable scheduling performance.

   @inproceedings{LiFaPa23,
     author = {Li, Boyang and Fan, Yuping and Papka, Michael E. and Lan, Zhiling},
     editor = {Klus{\'a}{\v{c}}ek, Dalibor and Julita, Corbal{\'a}n and Rodrigo, Gonzalo P.},
     title = {Encoding for Reinforcement Learning Driven Scheduling},
     booktitle = {Job Scheduling Strategies for Parallel Processing},
     year = {2023},
     pages = {68--87},
     publisher = {Springer Nature Switzerland},
     address = {Cham},
     isbn = {978-3-031-22698-4},
   }

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