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Neural Refrigeration Cycle Network enabled Optimal Control of Closed-loop Fluid Infrastructure System
Goal: Physics-informed, learning-based controllers for next-generation energy-efficient control of HVAC system.
Funded by: Samsung Global Research Outreach Award ($150K/yr)
NVIDIA Academic Grant Program Award
HVAC system
Bottom-up physics-informed modeling strategy + global optimal control
Bottom-up physics-informed modeling strategy + global optimal control
Big VISION Initiative
Goal: Develop the large-scale vision-based industrial inspection dataset, alongside methodology for (1) large-scale manufacturing data sourcing, screening, and quality control, (2) automated fine-grained manufacturing annotation with human feedback, and (3) downstream task-aware dataset design.
Funded by:
UMN Data Science Initiative (see DSI news coverage, $200K)
NVIDIA Academic Grant Program Award
NSF OAC #2608818
Data-Agent: Agentic AI for Autonomous Data Quality Evaluation, Curation and Task-Aware Dataset Design for Vision-Based Industrial Inspection
Industrial inspection is critical to ensuring the safety, reliability, and quality of manufactured products and complex engineering systems, yet advances in AI-enabled inspection are often limited by the availability of high-quality, well-curated data. This NSF-funded project will develop Data-Agent, an agentic AI-powered methodological and software platform for data-centric AI in vision-based industrial inspection. Data-Agent will provide integrated capabilities for automatically evaluating the quality of large and heterogeneous inspection datasets, improving annotations through human feedback, and designing task-aware datasets that are representative and informative for downstream inspection tasks. Building on the VISION Workshop series (https://vision-workshop-26.github.io/cvpr-2026/), an established community for advancing computer vision and AI for industrial inspection, the project will also release VISION V2, together with open-source software, benchmarks, tutorials, and training resources. Through these efforts, the project aims to foster a broader community of researchers, students, and industry practitioners and advance more trustworthy and effective AI systems for advanced manufacturing.
Funded by:
Natioanl Science Foundation (CSSI#2608818, $600k total, $400k to UMN); PI, with Co-PIs Zirui Liu and Yinan Wang.
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