2026-2027 Faculty Research Grants

Hyun Kwon (Engineering). 

Heterogeneous and sparse data processing for digital twin foundational work.

Developing a digital twin for volatile organic compounds (VOCs) as indicators of health status requires a clear understanding of the relationship between underlying physiological conditions and measurable VOC signatures obtained through diverse sensing modalities. The proposed work will focus on a targeted and tractable component of the problem: data harmonization. Existing VOC-related datasets in the literature are highly heterogeneous, sparse, and often “missing not at random.” This project will systematically collect published VOC datasets, standardize variables across studies, address missingness through appropriate imputation strategies, and generate high-quality synthetic data. The resulting dataset will serve as a foundational resource for external funding proposals and will be sufficient to support peer-reviewed publications. Undergraduate researchers will be actively involved throughout the project, with outcomes disseminated through presentations at the AIChE annual conference and submission to peer-reviewed journals. The ultimate goal of this effort is to position the team for successful external funding from the NSF FDT program and to advance the development of digital twin frameworks for health-related VOC sensing with broad applicability.