EVL

Established in 1973, the Electronic Visualization Laboratory (EVL) is an interdisciplinary research laboratory in the UIC College of Engineering’s Computer Science department. EVL specializes in collaborative visualization, virtual reality, visual data science, and advanced computing and networking infrastructure. EVL enables scientists and engineers to uniquely manage the scale and complexity of their data - to create information visualizations of multidimensional and multivariate data, explore 3D immersive worlds, juxtapose related yet heterogeneous 2D and 3D datasets, access computer infrastructure for machine learning, and move large datasets over high-speed networks - thereby using advanced tools and technologies to access, display, share and interact with their data and glean insight and knowledge.

EVL has always looked 10-15 years into the future, and has a legacy of past successes. In the ’70s, the Lab pioneered real-time interactive graphics and videogame technology; in the ’80s, it began focusing on scientific visualization; in the ’90s, it focused on advanced virtual reality, and on high-speed networking infrastructure; and, in the ’00s, on accessing, sharing and displaying ultra-high-resolution images on tiled display walls. EVL’s best-known inventions include the CAVE™ virtual-reality environment, the ImmersaDesk™, OptIPortal, and the SAGE™ scalable adaptive graphics environment, and the Continuum Collaborative Smart Space; EVL’s key networking initiative is StarLight.

news

07/20/25	New paper: "Parameterized Algorithms for Non-uniform All-to-all"
07/14/25	New paper: "mEnergy: Development of a Novel mHealth Multi-device Platform for Near Real-time Remote Assessment of Energy Disorders"
06/23/25	New paper: "CQSim+: Symbiotic Simulation for Multi-Resource Scheduling in High-Performance Computing"
05/08/25	New paper: "MNMGDatalog: A Scalable Multi-Node Multi-GPU Datalog Engine (poster)"
05/08/25	New paper: "Accelerating Iterative Relational Algebra Operations with WebGPU (poster)"
05/08/25	New paper: "Performance Characterization and Tuning of Non-uniform All-to-all Data Exchanges (poster)"
05/08/25	New paper: "Neurological Diseases Spectrum Mapping using Topological Data Analysis (poster)"
05/08/25	New paper: "EMMA: Efficient Multi-node Memory-aware AllReduce Algorithms (poster)"
05/08/25	New paper: "MAGUS: Memory-Throughput-Based Uncore Frequency Scaling on Heterogeneous Systems (poster)"
05/08/25	New paper: "From Prompts to Priorities: Pairwise Learning-to-Rank Scheduling for Low-Latency LLM Serving (poster)"

selected publications

Sai Priya Jyothula, and Andrew E. Johnson, Enhancing Consumer Insights through VR Metaphor Elicitation, IEEE Transactions on Visualization and Computer Graphics, 2025

DOI BibTeX

@article{JyJo25,
  title = {Enhancing Consumer Insights through VR Metaphor Elicitation},
  author = {Jyothula, Sai Priya and Johnson, Andrew E.},
  year = {2025},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  pages = {1--10},
  doi = {10.1109/tvcg.2025.3549905},
  keywords = {Interviews;Virtual environments;Cognitive science;Visualization;Three-dimensional displays;Cognition;Probes;Faces;Affordances;Training;Human computer interaction (HCI);virtual reality (VR);collaborative virtual environments;asymmetric VR;VR applications;embodied interaction;presence;immersion;perception and cognition;deep metaphors;consumer research;Zaltman's metaphor elicitation technique (ZMET)},
}

Luc Renambot, G. Elisabeta Marai, Daria Tsoupikova, Jonas Talandis, Michael E. Papka, Fabio Miranda, Nikita Soni, Dana Plepys, Lance Long, Maxine Brown, Andrew Johnson, Daniel Sandin, Thomas DeFanti, and Jason Leigh, Immersive Analytics at the Electronic Visualization Laboratory, In IEEE VR 2025 Workshop: Immersive Visualization Laboratory (IVL), Mar, 2025

BibTeX

@inproceedings{ReMaTs25,
  author = {Renambot, Luc and Marai, G. Elisabeta and Tsoupikova, Daria and Talandis, Jonas and Papka, Michael E. and Miranda, Fabio and Soni, Nikita and Plepys, Dana and Long, Lance and Brown, Maxine and Johnson, Andrew and Sandin, Daniel and DeFanti, Thomas and Leigh, Jason},
  title = {Immersive Analytics at the Electronic Visualization Laboratory},
  booktitle = {IEEE VR 2025 Workshop: Immersive Visualization Laboratory (IVL)},
  address = {Saint-Malo, France},
  year = {2025},
  month = mar,
  url = {https://sites.google.com/view/ivl-workshop3},
}

Andrew Wentzel, Serageldin Attia, Xinhua Zhang, Guadalupe Canahuate, Clifton David Fuller, and G. Elisabeta Marai, DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer, IEEE Transactions on Visualization & Computer Graphics, Jan, 2025
Abstract DOI BibTeX

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.
```
@article{WeAtZh25,
  title = {{DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer}},
  author = {Wentzel, Andrew and Attia, Serageldin and Zhang, Xinhua and Canahuate, Guadalupe and Fuller, Clifton David and Marai, G. Elisabeta},
  year = {2025},
  month = jan,
  journal = {IEEE Transactions on Visualization \& Computer Graphics},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  volume = {31},
  number = {01},
  pages = {65--75},
  doi = {10.1109/tvcg.2024.3456160},
  issn = {1941-0506},
  url = {https://doi.ieeecomputersociety.org/10.1109/TVCG.2024.3456160},
  keywords = {Digital twins;Visualization;Computational modeling;Data visualization;Data models;Tumors;Chemotherapy},
}
        
```