Featured Projects

Exhibition: Views of Cincinnati & Ohio Valley

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Exhibition “Views of Cincinnati & Ohio Valley”

Location: Elevar Gallery, 555 Carr St., Cincinnati.
opening hours: May 1- June 27, 2025, Mon-Thu 9-5 pm, Fri 9-6:30 pm

The exhibition is supported by the Creative Asian Society and the ArtsWave Impact grant.

“Infinite Loop” reflects my interpretation of the Ohio Valley—an ever-shifting landscape shaped by both deep geological time and layers of human history. Inspired by the region’s porous limestone caves, exposed rock formations, and the powerful erosive force of the Ohio River, the sculpture evokes the continuous movement and natural evolution embedded in this terrain. The form, looping without a clear beginning or end, draws from the valley’s complex strata—both literal and metaphorical. It echoes the industrial legacy of Cincinnati: a city built along railroads, powered by migration, and continually transformed by waves of innovation, creativity, and technology. Each undulating surface captures a sense of motion and continuity, speaking to the rhythms of the river and the resilience of a city in flux. By blending references to natural erosion, flood, and industrial infrastructure, Infinite Loop invites reflection on how we shape—and are shaped by—the landscapes we inhabit. It is a meditation on flow, transformation, and the unbroken cycles that define both the Ohio River and the city of Cincinnati itself.

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SMAT: Scalable Multi-Agent AI for DT

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SMAT: Scalable Multi-Agent Machine Learning and Collaborative AI for Digital Twin Platform of Infrastructure and Facility Operations.

Principal Investigators:

  • Prof. Sam Anand, Department of Mechanical Engineering, CEAS
  • Prof. Ming Tang, Extended Reality Lab, Digital Futures, DAAP

Grant: $40,000. UC Industry 4.0/5.0 Institute Consortium Research Project: 03.2025-01.2026

In this phase, we will develop a scalable digital twin that integrates machine, factory, and city-level data with AI-driven real-time decision-making. The key questions we aim to answer are:

  • Can a high-fidelity Digital Twin (DT) be efficiently built using only image and video data?
  • How can multiple specialized Large Language Model (LLM) agents—at machine, factory, and city levels—collaborate to generate relevant insights?
  • How effective is synthetic data from a Digital Twin for object detection and process recognition?
  • Does combining traditional Machine Learning (ML) with Large Language Models (LLMs) improve decision-making in complex manufacturing operations?

The project’s primary goal is to create a scalable, cloud-based digital twin that enhances operational efficiency through AI-driven insights. Additional technical objectives include:

  • Using advanced reality capture techniques (e.g., Gaussian Splatting) to build a Digital Twin from images and videos and simulate fault scenarios at factory and data center levels.
  • Integrating an IIoT data framework to track material flow, process handling, operational metrics, and equipment status for seamless cloud-based analysis.
  • Developing a synthetic data capture system using a simulated drone within the Digital Twin to train reinforcement learning models for fault prediction.
  • Designing a multi-agent AI system combining LLMs, machine learning, and reinforcement learning to enable dynamic communication, prediction, and diagnostics in the factory.

 

last year’s project: IIOT for legacy and intelligent factory machines with XR and LLM feedback with a Digital Twin demonstration of real-time IOT for architecture/building applications using Omniverse.

P&G VISM

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Project VISM (Virtual Interactive Simulation for Material customization)
Interactive Visualization with User-Controlled, Procedural-Based, and Physical-Based Material Customization.

PI. Ming Tang.

P&G Team: Kim Jackson, Andrew Fite, Fei Wang, Allison Roman

UC Team: Ming Tang, Aayush Kumar, Yuki Hirota

Sponsor: P&G. 12/01/2024 – 5/31/2025

Amount: $28,350

 

 

 

GenAI+AR Siemens

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Automatic Scene Creation for Augmented Reality Work Instructions Using Generative AI. Siemens. PI. Ming Tang. co-PI: Tianyu Jiang. $25,000. UC. 4/1/2024-12/31/2024

Students: Aayush Kumar, Mikhail Nikolaenko, Dylan Hutson.

Sponsor: Siemens through UC MME Industry 4.0/5.0 Institute

Investigate integration of LLM Gen-AI with Hololens-based training. 

Reinforcement Learning

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XR-Lab’s project was selected to join the  Undergraduates Pursuing Research in Science and Engineering (UPRISE) Program
UNDERGRADUATE SUMMER RESEARCH PROGRAM IN SCIENCE AND ENGINEERING
May 6 – July 26, 2024

 

Project title: Reinforcement Learning (RL) system in Game Engine

Student: Mikhail Nikolaenko. UC. PI: Ming Tang. 

This project proposes the development of a sophisticated reinforcement learning (RL) system utilizing the robust and versatile environment of Unreal Engine 5 (UE5). The primary objective is to create a flexible and highly realistic simulation platform that can model a multitude of real-life scenarios, ranging from object recognition, urban navigation to emergency response strategies. This platform aims to significantly advance the capabilities of RL algorithms by exposing them to complex, diverse, and dynamically changing environments. Leveraging the advanced graphical and physical simulation capabilities of UE5, the project will focus on creating detailed and varied scenarios in which RL algorithms can be trained and tested. These scenarios will include, but not be limited to, urban traffic systems, natural disaster simulations, and public safety response models. The realism and intricacy of UE5’s environment will provide a challenging and rich training ground for RL models, allowing them to learn and adapt to unpredictable variables akin to those in the real world.

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