EVL PhD candidate Kunting Qi's paper entitled &ldquo;ML–Driven Auto-tuning Framework for Non-uniform All-to-All Data Exchange&rdquo; (Qi, K., Fan, K., Domke, J., Ba, S., Vishwanath, V., Papka, M.E., Kumar, S.) has been selected as a Distinguished Paper Award Nominee to the IEEE International Conference on High Performance Computing, Data, Analytics, and Data Science (HiPC) 2025. 
HiPC serves as a forum to present current work by researchers from around the world in the areas of high-performance computing and data science. The meeting focuses on all aspects of high-performance computing systems, data science and analytics, and their scientific, engineering, and commercial applications. This year the conference will be held in Hyderabad, India, December 17-20, 2025.

EVL Graduate Researcher Kunting Qi HiPC 2025 Distinguished Paper Award Nominee

IEEE VIS 2025 is the premier forum for advances in theory, methods, and applications of visualization and visual analytics. The conference will convene an international community of researchers and practitioners from universities, government, and industry to exchange recent findings on the design and use of visualization tools. This year&rsquo;s Conference will be held November 2 to 7 in Vienna, Austria 
EVL PhD students Sanjana Srabanti, Leonardo Ferreira, and Gustavo Moreira have had their research papers accepted into the Conference: 
&ldquo;StreetWeave: A Declarative Grammar for Street-Overlaid Visualization of Multivariate Data&rdquo; 
Sanjana Srabanti, G. Elisabeta Marai, Fabio Miranda 
IEEE Transactions on Visualization and Computer Graphics (To appear) 
This is Sanjana&rsquo;s second full paper at a visualization conference, following her first at PacificVis. The paper proposes a design space for street-overlaid visualizations, and a declarative grammar to operationalize such space. 
&ldquo;VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design&rdquo; 
Leonardo Ferreira, Gustavo Moreira, Fabio Miranda 
IEEE Transactions on Visualization and Computer Graphics (To appear) 
This is Leonardo&rsquo;ss second VIS paper, first as first author, and third full paper overall in just 1.5 years into the PhD -- and having joined the program without a CS bachelor&rsquo;ss degree. The paper proposes an LLM-based approach to extract building blocks from papers describing visual analytics systems. The data and interface for the paper are available here: <a href="https://urbantk.org/va-blueprint/">https://urbantk.org/va-blueprint/</a> 
&ldquo;Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics&rdquo; 
Gustavo Moreira, Leonardo Ferreira, Maryam Hosseini, Carolina Veiga, Fabio Miranda 
IEEE Transactions on Visualization and Computer Graphics (To appear) 
This is Gustavo&rsquo;s fourth VIS paper and the third consecutive year he has had a first-author full paper at VIS. In total, it&rsquo;s his eighth published full paper (!) in just three years into his PhD. The paper proposes a series of design features to support human-AI alignment in the creation of urban dataflows. The code and tutorials for the paper are available here: <a href="https://urbantk.org/urbanite/">https://urbantk.org/urbanite/</a>

EVL PhD Students Sanjana Srabanti, Leonardo Ferreira, and Gustavo Moreira Publish to VIS 2025

Join us in congratulating Nafiul Nipu and co-authors for a newly accepted paper in the <a href="https://visualanalyticshealthcare.github.io/homepage/2025/">VAHC: 16th Annual Workshop on Visual Analytics in Healthcare</a> at IEEE VIS'25. The Workshop on Visual Analytics in Healthcare (VAHC) is the premier research event exploring the application of data visualization and visual analytics to biomedicine. The Workshop is organized by General Chair Alessio Arleo, Eindhoven University of Technology, and Co-Chairs Renata Raidou, TU Vienna and Gabriela Morgenshtern, University of Zurich. 
N. Nipu, L. van Dijk, G. Canahuate, C.D. Fuller, G.E. Marai, &ldquo;Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator&rdquo;, VAHC 2025 (16th workshop on Visual Analytics in Healthcare), in conjunction with IEEE VIS 2025, Vienna, Austria, pp. 1-7, November 2025. 
This is a &ldquo;lessons learned&rdquo; paper about our experience developing and deploying an interactive dashboard for predicting oncological risk in head and neck cancer. The tool is hosted at EVL as <a href="https://risk-calculator.evl.uic.edu/">https://risk-calculator.evl.uic.edu/</a>, it is publicly available, and it is currently used by a large clinician base across multiple continents, which is very impressive. From the data visualization perspective, this work is particularly interesting because we collected clinician feedback more than a year after the initial deployment.

&ldquo;Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator&rdquo; to be Presented at VAHC 2025

EVL PhD candidate Hossein Fathollahian has his short paper accepted at the <ahref="http://biovis.net/2025/">BioMedVis Challenge 2025</a>, a data contest organized by <a href="https://ieeevis.org/year/2025/welcome">IEEE VIS 2025</a>. 
H. Fathollahian, S. Zhao, M. Nipu, G.E. Marai, &ldquo;Attention-based Region of Interest Discovery in 3D Tissue Images&rdquo;, IEEE BioMedVis Challenge 2025, IEEE VIS, 2025. 
This work presents an approach for the detection and exploration of regions of interest (ROIs) using graph-based analysis. The novel computational approach for ROI detection is a main strength, and the interactive frontend includes several features addressing the challenge tasks, such as quick navigation to prominent ROIs. 
Additionally, Hossein paper &ldquo;From Peaks to Patterns&rdquo; was accepted to the ReDesign Contest: 
Authors: Hossein Fathollahian, Marziye Salahshour 
Abstract: MRS is a non-invasive way of gaining information about cellular metabolism and can be used to study neurological and metabolic disorders. However, the clinical utility is limited by low signal quality, overlapping spectral peaks, and the inability to interpret and compare metabolite data. We developed an interactive visual analysis system that establishes the methods by adding filtering, quality measurement, and a comparative summary of MRS signals. This will allow researchers to perform a more accurate evaluation of spectral data, which will help them understand the molecular processes behind the complex neurological and metabolic processes in greater depth. 
Congratulations, Hossein!

BioMedVis 2025 Accepted Papers

EVL, Computer Science Professor and Director, UIC Institute for Health Data Science Research, Liz Marai delivered the Keynote at the <a href="https://visualanalyticshealthcare.github.io/homepage/2025/program.html">Visual Analytics in Healthcare (VAHC) 2025</a> meeting in early November in Vienna, Austria. VAHC is associated with <a href="https://ieeevis.org/year/2025/welcome">IEEE VIS 2025</a> held November 2 - 7, 2025. 
Title: &ldquo;The Blackbox Patient and the Power-of-Many&rdquo; 
Abstract: 
Precision medicine aims to tailor treatment for individual patients based on big data collected from cohorts of similar patients. This power-of-many can also be used to predict treatment outcomes and treatment-related side effects for individual patients. Visual computing and visual analytics play a crucial role in addressing this problem, from calculating spatial similarity to facilitating risk communication. This talk reflects on work in this area, emerging lessons, and future research directions.

Professor Liz Marai Delivers Keynote at the Visual Analytics in Healthcare

Candidate: Carla Floricel 
Committee: 
Dr. Michael Papka (chair and advisor) 
Dr. Fabio Miranda 
Dr. Khairi Reda 
Dr. Andrew Johnson 
Dr. Victor Mateevitsi 
Dr. Guadalupe Canahuate (University of Iowa) 
Date: Tuesday October 21st, 4:00 PM CDT 
Location: Engineering Research Facility, Room 2068 
Abstract: 
Post-treatment decision-making is a process that depends on longitudinal studies of patient cohorts, in which identifying the risk of adverse treatment outcomes is critical to improving personalized care. This process benefits from visual analytics because it can overcome many of the challenges that arise with complex patient cohort data. Visual analytics can help to interpret treatment progressions and outcomes, and stratify cohorts by risk categories, which are crucial for improving treatment decision-making. However, post-treatment cohort analysis uses large-scale, heterogeneous, temporal, multivariate datasets with associated
attributes and missing values. Visualization needs to provide scalable, effective methods for cohort analysis at different levels of detail that can uncover patterns and associations among patient attributes that correspond to negative treatment outcomes. Moreover, posttreatment care planning relies on computational cohort data modeling and, as a result, uses both objective and subjective evidence, namely, the clinician’s interpretation of the modeling results. Consequently, cohort modeling and analysis depend on collaborations between clinicians and data modelers. Therefore, visual analytics solutions need to facilitate these collaborations and the interpretation and evaluation of modeling results in a clinical context. 
I am running a few minutes late; my previous meeting is running over. This dissertation explores visual analytics techniques for cohort modeling and analysis
and applies these techniques to post-treatment decision-making. This work addresses the challenges identified above by designing, developing, and evaluating four application-specific visualization systems in collaboration with clinical researchers and data modelers. I first identified the design requirements for a family of cohort modeling problems in cancer symptom and digital biomarker research. Next, I design several systems that integrate unsupervised modeling for the computational back-end and data visualization for the front-end. I propose novel, custom visual encodings for multivariate temporal cohorts that enable iterative risk assessment across cohort stratifications. A first system, OpenDBM, uses visual analytics for behavioral risk assessment in digital biomarker research, using cohorts with hundreds of modeled attributes, and it was designed for the open-source community. This work proposes a novel encoding that aggregates multivariate, spatial, and non-spatial temporal attributes on anatomical locations to explain behavioral biomarkers. A second system, THALIS, shifts
the focus to clinician-modeler collaborations in head and neck cancer cohort modeling and to a multi-stage patient monitoring process, namely, during and post-treatment. This system uses scalable visual encodings to interpret attribute associations and introduces a new encoding for evaluating patient outcomes in multivariate, multi-stage time series. A third system, Roses, builds on the previous work, using custom visualizations for the interpretation and evaluation of outcome risk predictions, this time accommodating configurable analytical workflows for clinicians and modelers. The system introduces a visual encoding
to summarize multi-stage networks, with temporal nodes, which helps to evaluate patterns and associations in modeled outcome risk components. A fourth system, L-VISP, explores visual analytics for understanding and assessing black-box models in cohort risk prediction, with an emphasis on the design requirements for data modeler activities. To support model evaluation, the system visualizes results for machine-derived (cluster) or user-specified cohort stratifications and introduces custom encodings for weighted associations in multivariate attributes. Together, these systems contribute to data visualization and modeling solutions for the challenges that data modelers and clinicians face during collaborations. 
Patient records were used for the cancer research projects. These records contained demographic and diagnostic details, as well as longitudinal symptom ratings. The records were anonymized by our clinical expert collaborators and stored on a private institution cloud. Access was limited and given by our collaborators on a case-by-case basis. For the digital biomarker project, the collaborators provided longitudinal biomarker records. These records were extracted from actor, not patient, videos using a feature-extraction toolkit and stored in a private cloud. There were no personal identifiers in these data, and no research involving human subjects was conducted in this dissertation.

PhD Dissertation Announcement: &ldquo;Patient Cohort Visual Analytics for Post-Treatment Care&rdquo;

On Friday, October 3rd, EVL hosted the College of Engineering&rsquo;s Fall 2025 Advisory Board meeting in EVL&rsquo;s new visual data science space <a href="https://arcade.evl.uic.edu/web/">Arcade</a>. Arcade is located in the new Computer Design Research Learning Center (CDRLC) building and is an instrument designed for UIC science and engineering research and research training - a digital Project Room that enables domain scientists and computer scientists to collaborate on real-world problems by providing them with high-performance computing, high-resolution displays, and sophisticated Artificial Intelligence (AI), data analytic, data simulation, image processing, visualization, and virtual-reality software and services. 
Arcade offers on-site computing with adequate support, as well as collaboration software services. It is a configurable computing environment that prioritizes real-time, interactive, collaborative, and data-intensive applications, while also supporting off-hours batch-mode processing. Users can execute code more quickly, apply more sophisticated analytics to large-scale problems, gain deeper insights, and open new avenues of research. The Arcade&rsquo;s collaboration software enables researchers to interact locally with colleagues using the display walls or engage in video-teleconferences while simultaneously sharing and interacting with their data on the large displays, which remote viewers can also access online.

EVL Hosts COE Advisory Board Fall Meeting 2025

EVL Director and Computer Science Professor Michael Papka and Professor Zhiling Lan have received a new award from the National Science Foundation Computer and Information Science and Engineering (CISE): Core Programs entitled &ldquo;SHF:Small: Beyond Performance: Energy-Conscious High Performance Computing.&rdquo; The grant project period runs 10/01/2025 to 09/30/2028 for $540,000. 
Abstract: 
As high-performance computing (HPC) systems grow increasingly heterogeneous and application workloads become more diverse, achieving both high performance and energy efficiency poses a significant challenge. While performance optimization remains essential, energy efficiency has emerged as a critical priority due to substantial infrastructure demands, operational costs, and environmental impact. This project tackles two fundamental barriers to energy-conscious HPC: power waste in heterogeneous hardware and the lack of dynamic power coordination. It develops a holistic framework, EcoHPC, to enable energy-efficient execution of hybrid workloads on heterogeneous systems. The anticipated outcomes have broad scientific, economic, and environmental impacts. Additionally, an integrated education plan aims to train the next generation of the HPC workforce. 
The project introduces three core technical innovations. First, it exploits collaborative filtering-based recommendation systems that combine offline analysis with real-time profiling to model application performance–power trade-offs and guide scheduling decisions. Second, it applies multi-objective optimization and Pareto-front analysis to treat power as a first-class schedulable resource, enabling system-wide coordination and optimization. Third, it develops adaptive runtime systems that dynamically predict application phases and resource demands, allowing applications to minimize power waste while maximizing performance under power constraints. Together, these innovations yield new workload models, energy-aware allocation methods, and runtime strategies that significantly enhance energy efficiency in heterogeneous computing environments.

Researchers Michael Papka and Zhiling Lan Receive New NSF CISE Award

&ldquo;ChromPolymerDB: A High-Resolution Database of Single-Cell 3D Chromatin Structures for Functional Genomics&rdquo; authored by Chronis, Constantinos (UIC), Chen, Min (Shanghai Jiao Tong University), Du, Lin (UIC), Zhao, Siyuan (UIC), Ye, Bowei (UIC), Delafrouz, Pourya (UIC), Farooq, Hammad (UIC), Chattopadhyay, Debaleena (UIC), Marai, G. Elisabeta (UIC), Shao, Zhifeng (Shanghai Jiao Tong University), Liang, Jie (UIC), and Czajkowsky, Daniel M (Shanghai Jiao Tong University) has been published in NAR-02636-2025, Nucleic Acids Research, 2025. 
This work provides a valuable interface and algorithm resource and a database for exploring scHi-C (single-cell Hi-C) data and integrating these analyses into ongoing research. This work stems from a project by Lin Du, advised by Jie Liang. Constantinos Chronis, an Assistant Professor in the Department of Biochemistry and Molecular Genetics at UIC, shepherded the submission after Jie Liang passed away.

EVL Researchers Co-Author Prestigious Nucleic Acids Research Publication

Hypertext 2025 Virtual Reality Exhibition in the CAVE2 VR Environment 
Electronic Visualization Laboratory (EVL), University of Illinois Chicago (UIC) 
842 W Taylor St. Room 2036 
Chicago IL 60607 
September 16, 2025 @ 4.30pm 
Your VR Theater experience awaits! 
Plunge into the surrounds of our burning forests. Explore the ghostly seams of the logistical city. 
Immersive CAVE2 Virtual Reality Exhibition -- Hidden Costs of Logistics and the Disappearing Wild -- highlights immersive projects that explore the intersections of anthropology, design, theater, creative coding, typography, and hypertext interaction to tackle some of the world’s most complex problems. From disappearing ecosystems to the invisible machinery behind global supply chains, each project invites participants to observe and reflect on the environmental, social, and economic impacts shaping the American Midwest and our planetary well-being. 
Developed by multi-institutional teams of anthropologists, artists, and computer scientists from the University of Chicago, UIC, and Yale, and created by the Creative Coding participants in the Electronic Visualization Laboratory (EVL), these VR experiences confront pressing global challenges with ethnographic insight and design innovation. Participants will journey through digitally rendered 360 landscapes of deforestation and the unseen logistical labor behind everyday commodities. By merging art, technology, and interactive hypertext in storytelling, these projects advance critical conversations around climate change, social justice, and the hidden infrastructures that sustain -- and strain -- life on Earth. 
Supported and organized by the Hypertext2025 Conference, the Electronic Visualization Laboratory (EVL), the Department of Computer Science and the School of Design at the University of Illinois Chicago (UIC) and the Department of Anthropology at the University of Chicago. 
Featured Projects: 
CAVE2 Virtual Reality Exhibition: Project Fulfillment 
by Daria Tsoupikova, Julie Y. Chu, Alexandria Schmidt, Harini Kumar, Shahira Bhasha, Gaeun Lee, Eunseo Ko, Kenzell Huggins, Darshan Shet and Farah Kamleh 
Interactive VR Exhibition: Why Must We Make It All Burn? 
by Alexandra Preuss 
ACM Hypertext 2025 Conference <a href="https://ht.acm.org/ht2025/">website</a>.

EVL Graduate Researcher Kunting Qi HiPC 2025 Distinguished Paper Award Nominee

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