Posts

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.

Poster in Urban Affairs Conference

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Poster: Co-Designing Data Governance Policies for a Neighborhood-Based Digital Health Intervention

Lauren Forbes, ..Ming Tang. Co-Designing Data Governance Policies for a Neighborhood-Based Digital Health Intervention. INTERNATIONAL CONFERENCE ON URBAN AFFAIRS 2026.

Digital health innovations (DHI) present important opportunities to improve individual health outcomes and community well-being; however, they are typically developed by private sector innovators who prioritize the novelty of innovation and data-driven value creation over achieving health equity. Resultantly, Black lived experiences and perceptions of digital innovations are regularly overlooked in digital innovation development. This exclusion, exacerbated by the digital divide and place-based structural barriers, perpetuates racialized health inequities and widespread institutional mistrust among Black communities. In our study, we address these complex challenges through the question, “What are the digital data governance preferences of Black communities that should inform DHI development”? This question is the first of a broader study that seeks to generate a gamified DHI using “digital twinning” (3D, GIS based city modeling with virtual reality) for health outcomes improvement by integrating these community-driven data governance policies and fostering institution trust. We use mixed methods consisting of a longitudinal survey, listening sessions and focus groups, demonstration sessions, co-design hubs, and user-contributed Ecological Momentary Assessment (EMA) data to answer our questions and to test community perceptions of and experiences using the gamified DHI. By the time of this presentation, we anticipate having findings from the first survey, listening sessions, and focus groups along with preliminary community-driven data governance policies and a draft game design architecture. This interdisciplinary study represents a novel opportunity to not only improve health outcomes within marginalized communities, but also to repair institutional mistrust and foster digital literacy and agency through the co-design of digital data governance policies. 

More info on the project is available at THRED: Technology for Health, Resilience, Equity , and Decision-Making.

DT & AI for AEC showcase

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Digital Twins,  AI Design Showcase-Higher Education explored emerging digital technologies and their impact on the AEC industry. The event was organized by the Cincinnati BIM User Group and held on April 16, at College of Design Arcgutectyre, Art, and Planning (DAAP), University of Cincinnati.

Hosted by Prof. Ming Tang at the DAAP, the showcase featured student work from the University of Cincinnati, Northern Kentucky University, and Cincinnati State. Attendees experienced digital twin, AI, and BIM technologies firsthand, highlighting how these student teams are helping shape the future of the AEC workforce.

The UC team presented several projects, including digital twins integrated with IoT, AI-based spatial computing with BIM (focusing on performance, sustainability, and wayfinding on the UC campus), and the SENSE + AI studio.

 

Photo by Nicholas Namyar. 04.16.2026

Special thanks to IMAGINiT Technologies for sponsoring this event.

THRED

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Technology for Health, Resilience, Equity , and Decision-Making 

Team: A&S: Kelly Merrill, Lauren Forbes, Briana Simms, Paris Wheeler, Diego Cuadros, DAAP: Ming Tang

Funding: Center for Clinical & Translational Science & Training. CCTST. Pilot Grant. $50,000. PI. Merrill, Forbes,  Co-I. Tang, Cuadros, Simms, Wheeler. 2026. University of Cincinnati.

Project Aims: 

Aim 1: Co-design a set of digital data governance policies that reflect Black community preferences, concerns, and expectations around the use of their digital health data.  

Aim 2: Assess the utility of digital twin technology (3D city modeling with VR) for community advocacy and population health-related objectives. 

Aim 3: Co-design and develop a community-driven, population health intervention and participatory planning tool. 

Ming Tang’s involvement in the human-centered digital twin can be traced back to his work at the MSU MIND Lab roughly two decades ago, and the THRED project builds directly on that foundation. There is also a clear conceptual link to HomeNetToo project then, where multiple interfaces—a standard web interface, a spatial interface, and an interpersonal interface—were developed to examine how different modes of interaction influence knowledge acquisition across varying cognitive styles. That early work established an important premise: the design of an interface fundamentally shapes how users interpret, understand, and engage with information.

THRED extends this line of inquiry beyond controlled experimental settings into a real-world, system-scale platform by integrating digital twins, and data-driven decision environments. Rather than comparing interfaces in isolation, it synthesizes them—bringing together spatial (3D environments), informational (data visualization and dashboards), and social (community and stakeholder engagement) interfaces into a unified ecosystem. In this sense, THRED represents a shift from experimental interaction design toward an applied, human-centered digital twin framework. It maintains continuity with earlier immersive technology research while significantly expanding its scope, enabling new forms of collective understanding, decision-making, and behavioral insight at urban and societal scales.

Adondale Digtal Twin (ADT) Prototypes

1. DATA VIZ ADT

mobile phone must be put in horizonal orientation in order to see buttons

Embeded example of  “DATA VIZ DT“. Full screen version click here. 

2. Social ADT

Social DT“. Embeded Version below. You can also check out the full screen version Here. 

 

Expalnination

screen shot of earlier versions.