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Dr. Miranda is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago and a member of the Electronic Visualization Laboratory. His research focuses on developing techniques for the interactive visual analysis of large-scale data, integrating methods from visualization, data management, machine learning, and computer graphics. In particular, he is interested in the design and development of frameworks, grammars, and knowledge bases that support human-AI collaboration in the context of urban computing and urban data analysis. He has collaborated closely with domain experts across a wide range of fields, resulting in research published in leading venues as well as systems adopted by experts in academia, industry, and government agencies. His work has been supported by multiple funding agencies, including the NSF, NIH, DOT, and the Spencer Foundation, and has received extensive media coverage from outlets such as The New York Times, The Economist, and Architectural Digest. His contributions have also been recognized with several honors, including the SIGMOD Best Demo Award and an IEEE VIS Best Paper Honorable Mention.

Visual analytics (VA) systems have become essential tools for exploring and making sense of complex data across domains like transportation, public health, and urban planning. These systems allow experts to combine computational models with interactive visualization, supporting insight, exploration, and decision-making. We are now at an inflection point in how such systems are designed and implemented. Large language models and generative AI are fundamentally reshaping software development, and VA is no exception. Tasks that once demanded weeks of engineering effort (scaffolding a data pipeline, prototyping an interactive interface, implementing a custom layout algorithm) can now be accomplished in days if not hours. This acceleration opens an expansive new design space for VA researchers and practitioners alike. But it also introduces risk. As LLMs lower the cost of building, they erode the friction that once enforced rigor. Systems can now be assembled faster than the problem is understood. The structured support that VA development has long lacked becomes not just useful but necessary: without it, faster development cycles risk discarding the design rationale and interdisciplinary input that give these systems their value.

In this talk, I will first share a series of VA systems my group has developed in close collaboration with domain experts. While these systems have had real-world impact, they also revealed recurring challenges in authoring and scaling such tools; challenges that motivated a broader shift towards creating toolkits and frameworks that make VA system development more extensible, systematic, and collaborative. I will then present an ecosystem of frameworks that support reusable components, provenance-aware design, and human-AI collaboration. These include the Urban Toolkit, a grammar-based framework for authoring urban visualizations; Curio, a dataflow-based environment for composing and executing VA workflows; and Urbanite, a system for aligning human goals with AI-suggested components through interactive feedback.

From Data to Knowledge to Better Clinical Outcomes: Leveraging Real-World Data to Assess Health Across the Lifespan

Mamoun Mardini
Assistant Professor in the Department of Health Outcomes and Biomedical Informatics at the University of Florida

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Cooperation Algorithms in Multi-Agent Reinforcement Learning

Reza Azadeh
Associate Professor at the Miner School of Computer and Information Sciences at the University of Massachusetts Lowell

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Designs to Support Better Visual Data Communication

Cindy Xiong Bearfield
Assistant Professor at School of Interactive Computing at Georgia Institute of Technology

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AI-Driven Biometric Security and Healthcare Applications: From Anti-Spoofing to Adversarial Resilience

Nima Karimian
Assistant Professor at the Lane Department of Computer Science and Electrical Engineering, West Virginia University

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AI-Driven Biometric Security and Healthcare Applications: From Anti-Spoofing to Adversarial Resilience

Austin Downey
Associate Professor in Mechanical, Aerospace, and Civil Engineering at the University of South Carolina

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Accelerating AI at the Edge: Innovations in Hardware and Algorithms

Ramtin Zand
Assistant Professor of Computer Science at the University of South Carolina

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