Posts
DOE Award: Genesis Mission
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
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
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.
Re-storying Fort Ancient
Re-storying Fort Ancient: An AI-Augmented Digital Twin Installation for Indigenous Earthwork
PI: Ming Tang, Co-Is: John Hancock, Tianyu Jiang, Samira Sarabandikachyani. $5,000. CDRI Grant, DAAP, UC. 5/23/2026- 8/23/2026
Students: Saptarshi Ghosh, Mikhail Nikolaenko, Johnny Bozhi Peng, Ahmad Alrefai
This project proposes the development of an AI-augmented Digital Twin installation for the Fort Ancient Earthworks, a UNESCO World Heritage site in southern Ohio. Using reality capture, spatial computing, and Large Language Models (LLMs), the project will create an interactive web- and mobile-based experience that allows users to explore the site through immersive visualization and natural language conversation.
The project builds upon existing 3D earthwork models previously developed by CERHAS together with earlier XR-Lab spatial computing workflows. This foundation allows the team to focus on AI-driven interaction, interpretation, and public engagement without needing to reconstruct the site from the ground up. The AI system will be developed through a carefully curated Retrieval-Augmented Generation (RAG) workflow using approved scholarly, archival, and Indigenous-informed materials as its knowledge base.
LiDAR Scan of the Hut
