The MIT Programming Languages group has selected &ldquo;Optimizing Datalog for the GPU&rdquo; Sun, Y., Shovon, A.R, Gilray, T., Kumar, S., Micinski, K. to be presented and published at the 2026 MIT PLR Workshop. 
The <a href="https://plr.csail.mit.edu/">MIT Programming Languages Review</a> launched in 2023, to highlight recent research deemed interesting and important, highlighting recent developments with significant potential to shape the future direction of PL research and/or industry practice. The MIT PL Review program committee consists of senior MIT PhD students, with the mission of selecting papers that can substantially transform the PL community and beyond, with a focus on emerging trends rather than established lines of research. 
Abstract: 
&ldquo;Optimizing Datalog for the GPU&rdquo; explores how modern Datalog engines can be redesigned for accelerator hardware. It introduces a GPU-backed Datalog engine built around a novel data structure called hash-indexed sorted array (HISA). HISA enables efficient execution of sparse relational workloads by combining the benefits of constant-time indexed access with dense, range-structured data layouts. By rethinking how a foundational declarative programming technology maps to GPUs, the work highlights a growing intersection between programming languages, program analysis infrastructure, and accelerator-aware system design, illustrating how PL implementation techniques can evolve alongside modern hardware trends. 
The Workshop will be held May 8, 2026.

EVL Researchers Invited to Present &ldquo;Optimizing Datalog for the GPU&rdquo; at MIT PLR Workshop 2026

PhD Student: Kazi Shahrukh Omar 
Committee Members: 
Dr. Fabio Miranda, Chair and Advisor 
Dr. Michael Papka 
Dr. Khairi Reda 
Dr. Saeed Boorboor 
Dr. Lane Harrison, Worcester Polytechnic Institute 
Date/Time: Tuesday, February 10, 2026, 4 PM 
Location: CDRLC 1219 
Zoom Details: 
<a href="https://uic.zoom.us/j/9284268608">https://uic.zoom.us/j/9284268608</a> 
Meeting ID: 928 426 8608 
Abstract: 
As cities continue to grow rapidly, urban environments are producing increasingly large, diverse, and interconnected datasets. While this abundance of data creates new opportunities for urban analytics, it also imposes significant computational challenges when analyzing large-scale urban phenomena. For practical use, computational models should efficiently process heterogeneous datasets and produce faster model outputs. A representative example is accumulated shadow or sunlight access modeling, which requires computation over dense spatial structures and lengthy temporal scales, posing significant challenges for efficient execution at large scales, such as city-level analysis. Another key challenge lies in how these urban data and computational models are presented to stakeholders for effective decision-making. Beyond understanding what is happening, stakeholders must be able to explore what to do next, which calls for scenario-based representations through visual analytics (VA) that structure urban data and models around alternative design and planning outcomes. 
However, the current landscape of urban visual analytics (VA) tools remains largely centered on exploratory data analysis, emphasizing descriptive questions about existing urban conditions. In practice, these tools often take the form of domain-specific dashboards focused on exploratory analysis of static data through visualizations, rather than generating or comparing alternative scenarios. As a result, they offer limited support for the forward-looking reasoning required in urban decision-making, where stakeholders must compare alternative design choices and assess their potential outcomes. Although more recent modular urban VA toolkits improve flexibility in composing data and visualizations, they either lack explicit support for scenario-oriented workflows or require substantial expertise to assemble them. Addressing this gap calls for a shift from an exploration-centric VA to a scenario-oriented VA that explicitly supports reasoning over alternative urban futures. 
A scenario-oriented modular VA stack poses several key challenges across urban data and modeling pipeline. These challenges include: (C1) how to appropriately model complex urban phenomena, (C2) how to enable domain experts to externalize scenarios, (C3) how to support the comparison and analysis of alternative scenarios, and (C4) how to facilitate iterative exploration of counterfactual inputs that would lead to a scenario. This dissertation addresses these challenges by (C1) introducing Deep Umbra, an efficient computational framework for large-scale accumulated shadow modeling; (C2–C3) presenting SCOUT, a modular, scenario-oriented urban visual analytics toolkit that enables domain experts to externalize, analyze, and compare alternative urban scenarios across domains; and (C4) extending SCOUT with ongoing work on CF-Planner, that supports interactive exploration of counterfactual inputs that leads to a scenario outcome.

Prelim Announcement: &ldquo;From Scenarios to Counterfactuals: Towards Visual Analytics for Definition and Analysis of Urban Futures&rdquo;

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

Candidate: Francesco Botto 
Committee: 
Professor G. Elisabeta Marai, Chair and Advisor 
Professor Khairi Reda 
Professor Stefano Di Calro, Politecnico di Torino 
Date/Time: Tuesday, December 9 at 9:30AM 
Location: CDRLC 5424 
Abstract: FlavorCharter introduces a data-driven research framework composed of a mobile platform for crowd-sourced sensory data collection and an interactive visualization environment for exploratory analysis. This integrated system bridges data acquisition and visual reasoning, enabling both large-scale collection and interpretability of flavor perception data. This approach has the potential to document taste loss and empower patients to describe taste loss symptoms in a more precise and standardized language.

MS Thesis Defense Announcement: &ldquo;FlavorCharter: Development of a Framework for Quantifying and Visually Analyzing Flavor Perception&rdquo;

Title: Scientific Data Management and Visualization to Advance AI for Science 
UIC PI: Luc Renambot 
Sponsor: Argonne National Laboratory, Department of Energy 
Dates: 11/21/25 - 11/20/30 
Abstract: 
The project aims to support ongoing development and deployment of an AI-powered scientific assistant that transforms scientific exploration through intuitive, adaptive, and interoperable interaction with complex data. The result is two proposed thrusts, one in scientific data management and one in scientific data visualization. 
 UIC will participate in the thrust &lsquo;AI-Enhanced Visualization and Human-centered Data Interaction&rsquo;. The hypothesis to be researched is the following: advanced AI-driven visualization and intelligent data exploration - when coupled with novel, efficient data representations - will dramatically accelerate scientific discovery by enabling real-time analysis of large-scale datasets and live experimental data. 
 To investigate this hypothesis, we leverage exemplary data from a range of DOE domains to conduct research in the following three areas: human-centered exploration of scientific data, efficient data representations for interactive exploration, and AI-driven advanced rendering and visualization. UIC will participate in the design and development of an AI assistant for data exploration and a visualization workflow AI assistant.

EVL Research Faculty Member Luc Renambot Collaborates with Argonne National Laboratory Researchers on Newly Awarded &ldquo;Scientific Data Management and Visualization to Advance AI for Science&rdquo;

This year&rsquo;s Art of HPC showcase at the <ahref="https://sc25.supercomputing.org/">International Conference for High Performance Computing SC25</a>, brought together an inspiring community of artists, designers, and technologists exploring the intersections of creativity, high-performance computing, AI, and immersive media. The Art Gallery became a vibrant hub of curiosity and inspiration, where the audience could explore new possibilities at the intersection of art and cutting-edge computation. 
UIC Design and EVL Professor Daria Tsoupikova participated on the panel &ldquo;From Silicon to Surrealism: The Fusion of Art, AI, and HPC,&rdquo; moderated by Tomasz Bednarz with Amy Karle, Ruth West, and Mark Hereld. The discussion explored how large-scale computation is opening new frontiers in creative expression, from generative imagery to immersive storytelling, and how artistic inquiry, in turn, is reshaping our understanding of the purpose and potential of computing itself. 
The EVL Hummingbird VR Performance project, featured prominently in the exhibition, was displayed on giant displays across the show floor, in the Art Gallery, and in the poster ballroom. It was awe-inspiring for Tsoupikova to share this work, born from deep collaboration between theater artists, designers, and computer scientists, which was truly inspiring. 
This was Tsoupikova&rsquo;s first SC, &ldquo;I am deeply grateful to the Art of HPC Committee Joseph Insley, Francesca Samsel, Mark Hereld, India Gordon and Silvina Caino-Lores for championing and elevating artistic innovation at SC25. Their vision made this year&rsquo;s program groundbreaking.&rdquo; 
#SC25, #HPCignites 
<a href="https://sc25.supercomputing.org/program/art-of-hpc/">The Art of HPC</a> Where Creativity Meets HPC: 
Creativity has always been at the heart of innovation. It drives new perspectives, fuels artistic expression, and allows us to see beauty in unexpected places. By leveraging cutting-edge technology and resources, we can push the boundaries of both science and art. 
SC proudly presents Art of HPC, a showcase of the intersection between artistic creativity and high-performance computing. This comprehensive event featured a diverse array of exhibits, presentations, and activities throughout the conference. 
The Art of HPC Panel: 
High-performance computing and AI are sparking a thrilling new era in creativity and the arts. This dynamic panel brought together visionary artists and cutting-edge technologists to reveal how they harness the power of large-scale computation to craft works that dazzle the senses, resonate culturally, and often surprise us in delightful ways. From mesmerizing generative imagery to immersive, interactive experiences, the panelists demonstrated how HPC unlocks artistic possibilities at scales and resolutions previously unimaginable. 
But the excitement doesn&rsquo;t stop there - this is a two-way street. Artistic practices aren&rsquo;t just benefiting from big tech; they&rsquo;re reshaping how we think about computing itself, transforming its purpose from mere efficiency and scientific rigor to a wellspring of inspiration and expressive power. The panel provided an accessible and fascinating peek into a future where silicon and creativity join forces, co-designing bold new forms of culture and unlocking realms of imagination we&rsquo;ve yet to explore.

The Art of HPC Exhibition was a tremendous success at Supercomputing25!

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.

EVL Researchers Invited to Present “Optimizing Datalog for the GPU” at MIT PLR Workshop 2026

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