Victory City

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Metaverse of Simpson’s Victory City Reconstruct & Reimaging of Orville Simpson’s Vision of Urban Futures using AI

Ming Tang. $4500. Supported by SAID Simpson Urban Futures Grant Program. UC. 8. 2026-8. 2027

This project works on the AI-creation of an online Metaverse by reconstructing and reimaging Orville Simpson’s visionary “Victory City.” Using archival materials from the Simpson Collection—including drawings, paintings, and manuscripts—the project will generate digital interpretations of Simpson’s future city through AI-assisted visualization and 3D modeling. The platform will allow users to explore a multiplayer virtual city accessible through web browsers, mobile devices, and VR headsets. In addition to a virtual reconstruction of Victory City, the project will include a digital gallery presenting Simpson’s archival works and a digital installation at the Simpson Center, reintroducing Simpson’s ideas as a contemporary dialogue on sustainable urban futures.

The goal of this project is to translate Simpson’s visionary urban concepts into an interactive 3D digital environment that allows the public to experience and interpret his ideas spatially. Although Simpson’s online archive contains extensive drawings and models, these materials remain largely static. This project transforms them into an immersive platform where users can explore the spatial logic of Victory City. The project emphasis: 

  • AI-assisted visualization: generative AI tools will be used to produce architectural imagery, animations, and 3D models inspired by Simpson’s designs.
  • Construction of a Metaverse environment: a browser-based immersive environment will be developed to represent major elements of Victory City, including the central 102-story tower and the larger metropolitan system described in Simpson’s writings.

paper in Frontiers

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Differentiated memory and scientific cognition in AI research agents

Cuadros DF, Maiga A-A, Thatham S, Ortiz A, Powers-Fletcher M and Tang M (2026) Differentiated memory and scientific cognition in AI research agents. Front. Big Data 9:1916523. doi: 10.3389/fdata.2026.1916523

AI research agents increasingly support ideation, literature search, coding, experimental execution, analysis, and manuscript drafting across the scientific workflow. This progress advances automated discovery, but workflow automation is not scientific cognition. Scientific reasoning is cumulative and path-dependent: it depends on what a researcher has written, read, learned from critique, absorbed through experience, and used as habitual standards for judging novelty, rigor, feasibility, and significance. We propose Mnemo as a framework for modeling scientific cognition in AI research agents. First, scientific cognition may require differentiated memory, organized into distinct spaces for authored work, external reference, critique, experience, and judgment. Second, provenance should be treated not as passive metadata but as memory routing, because source origin helps determine cognitive function in reasoning. Third, new ideas may be better modeled as controlled collisions across memory spaces, filtered by judgment, rejection, and epistemic calibration, than as generic recombination from model priors. Mnemo motivates a research agenda for AI in science centered on routing fidelity, critique use, judgment alignment, rejection quality, and epistemic calibration.

SENSE-AI Studio

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SENSE–AI: Developing Spatial Experiences through Narrative,  Senses, and Emotions with AI

ARCH 4001 studio. SAID, DAAP, UC.

This SENSE-AI design studio invites students to explore the complex relationship between architecture, human emotion, cultural heritage, and experiential design through the conceptualization and design of a new museum at Fort Ancient, part of the UNESCO-listed Hopewell Ceremonial Earthworks. Using an AI-assisted design approach, students will explore how generative AI, computational analysis, digital heritage, and immersive technologies can support research, site interpretation, design ideation, visualization, and evaluation throughout the architectural design process. Students will investigate how spatial design can evoke, mediate, and communicate the histories, stories, and cultural significance of Fort Ancient—moving beyond functionality to create environments that resonate on psychological, sensory, and emotional levels. By integrating AI-assisted design with architecture, landscape, Indigenous cultural narratives, storytelling, and digital heritage, students will develop proposals for a museum that serves not only as a cultural and educational institution, but also as a place of reflection, empathy, discovery, and connection to the earthworks and surrounding landscape.

The studio examines how space can evoke memory, shape emotion, and deepen our understanding of history and culture. Students will use AI-assisted design, Extended Reality (XR), and immersive visualization as design tools to explore ideas, test spatial experiences, and communicate narratives. Through digital modeling, virtual reality, and interactive media, they will develop and evaluate architectural experiences that engage visitors on sensory, emotional, and cultural levels.

Working closely with the Fort Ancient team, students will create museum proposals that tell the stories of Native American communities through research, design exploration, immersive prototyping, and narrative development. The studio emphasizes thoughtful design, interdisciplinary collaboration, and emerging technologies as a means to create meaningful places that connect people with history, culture, and one another.

Site: Fort Ancient Earthworks, Hopewell UNESCO World Heritage Site, Ohio
Program: Museum of Emotions
Note: This studio is supported by the Gensler Award

      

Image: Left: computer rendering from CERHAS, UC. John Hancock. Right: AI Renderings by Meghan Powell. ARCH 4001. SENSE studio. 2025.

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DOE Award: Genesis Mission

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 I am very excited to share that UC grant proposal, “AI-Enabled Prediction of Coupled Hydrologic and Biogeochemical Processes in Heterogeneous Subsurface Systems,” has been selected for the U.S. Department of Energy’s initiative to accelerate scientific breakthroughs through artificial intelligence. 

DOE Phase 1: The Genesis Mission: Transforming Science and Energy with AI.
Award Amount: $500,000
Project Period: 09/01/2026–05/31/2027
PI: Reza Soltanian, Co-I: Ming Tang
Partner Institutions: New Mexico State University (NMSU), Pacific Northwest National Laboratory (PNNL), and Certerra Subsurface Imaging.

Quoted from UC News: UC selected for federal initiative to use AI to advance scientific discovery. Project will develop new workflows for energy and environmental applications

The University of Cincinnati has been selected to participate in the U.S. Department of Energy’s new national initiative designed to accelerate scientific discovery through artificial intelligence. 

Led by Reza Soltanian, professor of subsurface energy and hydrogeology in UC’s Department of Geosciences, the multidisciplinary project will develop next-generation AI-enabled scientific workflows and digital twins that integrate geological information, advanced geophysical imaging, field observations, physics-based simulations and AI surrogate models to characterize and predict complex subsurface systems.

The UC-led team brings together expertise in hydrogeology, hydrobiogeochemistry, geophysics, artificial intelligence, numerical and surrogate modeling, advanced subsurface imaging and extended reality. The collaboration includes Ming Tang, professor in UC’s School of Architecture and Interior Design and director of the Extended Reality Lab at UC Digital Futures, along with researchers from Pacific Northwest National Laboratory, New Mexico State University and industry partner Certerra Subsurface Imaging.

“The Department of Energy’s selection of this project reflects the strength of UC’s growing artificial intelligence ecosystem,“ Interim Vice President for Research Frank Gerner said.

“Through investments in initiatives such as Digital Futures and the Advanced Research Computing Center, we have created an environment where researchers can combine expertise in AI, data science and domain-specific scholarship to address complex challenges. Professor Soltanian and Professor Tang’s work demonstrate how these capabilities are advancing scientific discovery in ways that can have lasting impact on our energy and environmental future.“

 

paper in Environments Journal

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Toward Sub-Sewershed Spatio-Temporal Wastewater Surveillance: A Critical Review and a Candidate Multimodal Foundation-Model Framework

Cuadros, Diego F., Xi Chen, and Ming Tang. 2026. “Toward Sub-Sewershed Spatio-Temporal Wastewater Surveillance: A Critical Review and a Candidate Multimodal Foundation-Model Framework” Environments 13, no. 7: 382. https://doi.org/10.3390/environments13070382

Wastewater-based epidemiology (WBE) has matured into a population-level surveillance complement with operational precedent in poliovirus environmental surveillance, institutionalised systems for SARS-CoV-2, and expanding evidence across respiratory pathogens, substance-use markers, and antimicrobial-resistance targets at uneven maturity. The unresolved problem is specific: sub-sewershed spatio-temporal inference under sewer-network and observational aggregation. Intra-catchment heterogeneity, hydraulic dynamics, and equity-relevant population differences can all be obscured by aggregate-scale modelling. Current artificial intelligence/machine learning (AI/ML) methods in WBE can be organised into four threads: temporal forecasting, spatial–statistical localisation, sewer-network and hydraulic transport, and cross-site transfer. These methods solve useful parts of the surveillance problem at the scales they target, but they do not yet supply transferable latent sub-sewershed representations under downstream aggregation. This review proposes a candidate multimodal foundation-model framework that treats place and time as jointly learnable entities, integrates a graph backbone over the sewer-network topology, incorporates physics-informed constraints, and embeds equity-conscious downstream validation as a design requirement. The framework is intended to make sub-sewershed hypotheses explicit, testable, uncertainty-bounded, and accountable to environmental-justice-relevant external validation. Whether those hypotheses survive empirical testing remains an open question.

Figure 1. The aggregation problem at the sub-sewershed scale.